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'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
a : Tuple = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __lowerCamelCase ( ) -> Tuple:
UpperCAmelCase : int = Github(os.environ["""GITHUB_TOKEN"""] )
UpperCAmelCase : Tuple = g.get_repo("""huggingface/transformers""" )
UpperCAmelCase : Any = repo.get_issues(state="""open""" )
for issue in open_issues:
UpperCAmelCase : str = sorted([comment for comment in issue.get_comments()] , key=lambda _lowercase : i.created_at , reverse=_lowercase )
UpperCAmelCase : str = comments[0] if len(_lowercase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 3_0
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="""closed""" )
elif (
(dt.utcnow() - issue.updated_at).days > 2_3
and (dt.utcnow() - issue.created_at).days >= 3_0
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
if __name__ == "__main__":
main()
| 338 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
a : Union[str, Any] = 1_0_0
a : Dict = set(range(3, NUM_PRIMES, 2))
primes.add(2)
a : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0 )
def __lowerCamelCase ( _lowercase ) -> set[int]:
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
UpperCAmelCase : set[int] = set()
UpperCAmelCase : int
UpperCAmelCase : int
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def __lowerCamelCase ( _lowercase = 5_0_0_0 ) -> int | None:
for number_to_partition in range(1 , _lowercase ):
if len(partition(_lowercase ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 | 1 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCamelCase ( ) -> List[str]:
raise RuntimeError("""CUDA out of memory.""" )
class UpperCamelCase_ ( nn.Module ):
def __init__( self ) -> List[str]:
super().__init__()
UpperCAmelCase : Optional[Any] = nn.Linear(3 , 4 )
UpperCAmelCase : Any = nn.BatchNormad(4 )
UpperCAmelCase : Union[str, Any] = nn.Linear(4 , 5 )
def _lowercase( self , A ) -> List[str]:
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Tuple = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(A ):
nonlocal batch_sizes
batch_sizes.append(A )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(A , [128, 64, 32, 16, 8] )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(A , A ):
nonlocal batch_sizes
batch_sizes.append(A )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
UpperCAmelCase , UpperCAmelCase : int = mock_training_loop_function("""hello""" )
self.assertListEqual(A , [128, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, """hello"""] )
def _lowercase( self ) -> int:
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(A ):
pass
with self.assertRaises(A ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def _lowercase( self ) -> Tuple:
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(A ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(A ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def _lowercase( self ) -> Any:
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(A , A , A ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(A ) as cm:
mock_training_loop_function(128 , """hello""" , """world""" )
self.assertIn("""Batch size was passed into `f`""" , cm.exception.args[0] )
self.assertIn("""`f(arg1='hello', arg2='world')""" , cm.exception.args[0] )
def _lowercase( self ) -> Optional[Any]:
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(A ):
raise ValueError("""Oops, we had an error!""" )
with self.assertRaises(A ) as cm:
mock_training_loop_function()
self.assertIn("""Oops, we had an error!""" , cm.exception.args[0] )
@require_cuda
def _lowercase( self ) -> List[str]:
UpperCAmelCase : int = torch.cuda.memory_allocated()
UpperCAmelCase : Any = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , A )
UpperCAmelCase : Union[str, Any] = release_memory(A )
self.assertEqual(torch.cuda.memory_allocated() , A )
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __lowerCamelCase ( _lowercase ) -> str:
UpperCAmelCase : Optional[Any] = FileLock(str(tmpdir / """foo.lock""" ) )
UpperCAmelCase : List[str] = FileLock(str(tmpdir / """foo.lock""" ) )
UpperCAmelCase : List[Any] = 0.01
with locka.acquire():
with pytest.raises(_lowercase ):
UpperCAmelCase : Any = time.time()
locka.acquire(_lowercase )
assert time.time() - _start > timeout
def __lowerCamelCase ( _lowercase ) -> Dict:
UpperCAmelCase : List[Any] = """a""" * 1_0_0_0 + """.lock"""
UpperCAmelCase : int = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(_lowercase )
assert len(os.path.basename(locka._lock_file ) ) <= 2_5_5
UpperCAmelCase : Optional[Any] = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(_lowercase ):
locka.acquire(0 )
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
a : Optional[Any] = [8, 5, 9, 7]
a : List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a : Any = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class UpperCamelCase_ :
def __init__( self , A , A , A , ) -> None:
UpperCAmelCase : Optional[int] = claim_vector
UpperCAmelCase : List[Any] = allocated_resources_table
UpperCAmelCase : Optional[int] = maximum_claim_table
def _lowercase( self ) -> list[int]:
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _lowercase( self ) -> list[int]:
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _lowercase( self ) -> list[list[int]]:
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(A ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _lowercase( self ) -> dict[int, list[int]]:
return {self.__need().index(A ): i for i in self.__need()}
def _lowercase( self , **A ) -> None:
UpperCAmelCase : str = self.__need()
UpperCAmelCase : Optional[int] = self.__allocated_resources_table
UpperCAmelCase : Union[str, Any] = self.__available_resources()
UpperCAmelCase : List[str] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
UpperCAmelCase : List[Any] = False
for each_need in need_list:
UpperCAmelCase : Dict = True
for index, need in enumerate(A ):
if need > available_resources[index]:
UpperCAmelCase : Any = False
break
if execution:
UpperCAmelCase : Tuple = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
UpperCAmelCase : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(A )
# update available/freed resources stack
UpperCAmelCase : Union[str, Any] = np.array(A ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(A ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _lowercase( self ) -> Dict:
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(A ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(A ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(A ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(A ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : str = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'levit'
def __init__( self , A=224 , A=3 , A=3 , A=2 , A=1 , A=16 , A=[128, 256, 384] , A=[4, 8, 12] , A=[4, 4, 4] , A=[16, 16, 16] , A=0 , A=[2, 2, 2] , A=[2, 2, 2] , A=0.0_2 , **A , ) -> int:
super().__init__(**A )
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = kernel_size
UpperCAmelCase : Optional[int] = stride
UpperCAmelCase : Dict = padding
UpperCAmelCase : List[Any] = hidden_sizes
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : Any = key_dim
UpperCAmelCase : str = drop_path_rate
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = attention_ratio
UpperCAmelCase : Optional[Any] = mlp_ratio
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : int = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
| 338 | 1 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __lowerCamelCase ( _lowercase ) -> str:
UpperCAmelCase : Tuple = []
for line in lines:
UpperCAmelCase : List[str] = re.sub(R"""#.*""" , """""" , _lowercase ) # remove comments
if line:
filtered_lines.append(_lowercase )
UpperCAmelCase : Optional[int] = """\n""".join(_lowercase )
# Make a hash from all this code
UpperCAmelCase : Optional[Any] = full_str.encode("""utf-8""" )
return shaaaa(_lowercase ).hexdigest()
# get importable module names and hash for caching
a : str = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
a : int = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
a : str = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
a : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : List[str] = """Hello, World!"""
a : List[Any] = """en_XX"""
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Dict = Path("""data_bin""" )
UpperCAmelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowercase ).parent ) , checkpoint_file=Path(_lowercase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowercase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowercase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowercase )
UpperCAmelCase : List[str] = xmod.model.encoder.sentence_encoder
UpperCAmelCase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowercase )
UpperCAmelCase : str = XmodForSequenceClassification(_lowercase ) if classification_head else XmodForMaskedLM(_lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : int = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : List[str] = model.roberta.encoder.layer[i]
UpperCAmelCase : Optional[Any] = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
UpperCAmelCase : List[Any] = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Any = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
UpperCAmelCase : Any = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[str] = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : Tuple = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
UpperCAmelCase : List[str] = xmod_layer.fca.weight
UpperCAmelCase : str = xmod_layer.fca.bias
# output
UpperCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
UpperCAmelCase : Dict = xmod_layer.fca.weight
UpperCAmelCase : Dict = xmod_layer.fca.bias
UpperCAmelCase : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase : Union[str, Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : str = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : List[str] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : List[Any] = bert_output.adapter_modules[lang_code]
UpperCAmelCase : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any = from_adapter.fca.weight
UpperCAmelCase : int = from_adapter.fca.bias
UpperCAmelCase : Dict = from_adapter.fca.weight
UpperCAmelCase : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Tuple = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].dense.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].dense.bias
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].out_proj.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
UpperCAmelCase : Dict = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : str = xmod.model.encoder.lm_head.weight
UpperCAmelCase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : Any = xmod.encode(_lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowercase )
UpperCAmelCase : Optional[int] = model(_lowercase )[0]
if classification_head:
UpperCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase ) )
else:
UpperCAmelCase : Optional[Any] = xmod.model(_lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Dict = torch.allclose(_lowercase , _lowercase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowercase ).mkdir(parents=_lowercase , exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
a : List[str] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 338 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( _lowercase , _lowercase ) -> list[int]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = len(_lowercase ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
UpperCAmelCase : Dict = i + 1
else:
UpperCAmelCase : List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'''{two_pointer([2, 7, 1_1, 1_5], 9) = }''')
| 338 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338 | 1 |
'''simple docstring'''
from math import factorial
def __lowerCamelCase ( _lowercase = 2_0 ) -> int:
UpperCAmelCase : List[Any] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase : Any = n // 2
return int(factorial(_lowercase ) / (factorial(_lowercase ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(2_0))
else:
try:
a : str = int(sys.argv[1])
print(solution(n))
except ValueError:
print("""Invalid entry - please enter a number.""")
| 338 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
a : str = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase_ ( __magic_name__ , unittest.TestCase ):
lowercase = SpeechTaTokenizer
lowercase = False
lowercase = True
def _lowercase( self ) -> List[str]:
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase : str = SpeechTaTokenizer(A )
UpperCAmelCase : Optional[int] = AddedToken("""<mask>""" , lstrip=A , rstrip=A )
UpperCAmelCase : Any = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase( self , A ) -> Optional[int]:
UpperCAmelCase : Dict = """this is a test"""
UpperCAmelCase : str = """this is a test"""
return input_text, output_text
def _lowercase( self , A , A=False , A=20 , A=5 ) -> List[str]:
UpperCAmelCase , UpperCAmelCase : Dict = self.get_input_output_texts(A )
UpperCAmelCase : Union[str, Any] = tokenizer.encode(A , add_special_tokens=A )
UpperCAmelCase : Union[str, Any] = tokenizer.decode(A , clean_up_tokenization_spaces=A )
return text, ids
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : str = """<pad>"""
UpperCAmelCase : Optional[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) , A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) , A )
def _lowercase( self ) -> List[str]:
UpperCAmelCase : List[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-4] , """œ""" )
self.assertEqual(vocab_keys[-2] , """<mask>""" )
self.assertEqual(vocab_keys[-1] , """<ctc_blank>""" )
self.assertEqual(len(A ) , 81 )
def _lowercase( self ) -> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def _lowercase( self ) -> str:
UpperCAmelCase : Optional[Any] = self.get_tokenizers(do_lower_case=A )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
UpperCAmelCase : List[Any] = tokenizer.vocab_size
UpperCAmelCase : Union[str, Any] = len(A )
self.assertNotEqual(A , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
UpperCAmelCase : List[str] = ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
UpperCAmelCase : Any = tokenizer.add_tokens(A )
UpperCAmelCase : List[Any] = tokenizer.vocab_size
UpperCAmelCase : str = len(A )
self.assertNotEqual(A , 0 )
self.assertEqual(A , A )
self.assertEqual(A , len(A ) )
self.assertEqual(A , all_size + len(A ) )
UpperCAmelCase : Optional[Any] = tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l""" , add_special_tokens=A )
self.assertGreaterEqual(len(A ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
UpperCAmelCase : int = {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
UpperCAmelCase : int = tokenizer.add_special_tokens(A )
UpperCAmelCase : Union[str, Any] = tokenizer.vocab_size
UpperCAmelCase : Any = len(A )
self.assertNotEqual(A , 0 )
self.assertEqual(A , A )
self.assertEqual(A , len(A ) )
self.assertEqual(A , all_size_a + len(A ) )
UpperCAmelCase : Optional[Any] = tokenizer.encode(
""">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l""" , add_special_tokens=A )
self.assertGreaterEqual(len(A ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def _lowercase( self ) -> List[str]:
pass
def _lowercase( self ) -> Union[str, Any]:
pass
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Tuple = self.get_tokenizer()
UpperCAmelCase : List[str] = tokenizer.tokenize("""This is a test""" )
# fmt: off
self.assertListEqual(A , [SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
A , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
UpperCAmelCase : str = tokenizer.convert_tokens_to_ids(A )
# fmt: off
self.assertListEqual(A , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
UpperCAmelCase : int = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(
A , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
@slow
def _lowercase( self ) -> Union[str, Any]:
# Use custom sequence because this tokenizer does not handle numbers.
UpperCAmelCase : str = [
"""Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
"""general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
"""Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
"""models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
"""BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
"""conditioning on both left and right context in all layers.""",
"""The quick brown fox jumps over the lazy dog.""",
]
# fmt: off
UpperCAmelCase : Any = {
"""input_ids""": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
"""attention_mask""": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A , model_name="""microsoft/speecht5_asr""" , revision="""c5ef64c71905caeccde0e4462ef3f9077224c524""" , sequences=A , )
| 338 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a : List[Any] = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = BlenderbotSmallTokenizer
def __init__( self , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , A=True , **A , ) -> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=A , merges=A , add_prefix_space=A , trim_offsets=A , ) , bos_token=A , eos_token=A , unk_token=A , **A , )
UpperCAmelCase : Optional[Any] = add_prefix_space
def _lowercase( self , A , A=None ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 338 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Any = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFormerConfig""",
"""Blip2VisionConfig""",
],
"""processing_blip_2""": ["""Blip2Processor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Tuple = [
"""BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Blip2Model""",
"""Blip2QFormerModel""",
"""Blip2PreTrainedModel""",
"""Blip2ForConditionalGeneration""",
"""Blip2VisionModel""",
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
a : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
a : Optional[Any] = [
"""EAGER""",
"""AOT_EAGER""",
"""INDUCTOR""",
"""NVFUSER""",
"""AOT_NVFUSER""",
"""AOT_CUDAGRAPHS""",
"""OFI""",
"""FX2TRT""",
"""ONNXRT""",
"""IPEX""",
]
def __lowerCamelCase ( _lowercase , _lowercase=None , _lowercase=None , _lowercase=None ) -> str:
UpperCAmelCase : Dict = True
while ask_again:
UpperCAmelCase : Any = input(_lowercase )
try:
if default is not None and len(_lowercase ) == 0:
return default
return convert_value(_lowercase ) if convert_value is not None else result
except Exception:
if error_message is not None:
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase=[] , _lowercase=None , _lowercase=0 ) -> List[str]:
UpperCAmelCase : int = BulletMenu(_lowercase , _lowercase )
UpperCAmelCase : List[str] = menu.run(default_choice=_lowercase )
return convert_value(_lowercase ) if convert_value is not None else result
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
UpperCAmelCase : Any = int(_lowercase )
return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = int(_lowercase )
return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value] )
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Optional[Any] = int(_lowercase )
return DynamoBackend(DYNAMO_BACKENDS[value] ).value
def __lowerCamelCase ( _lowercase ) -> Union[str, Any]:
UpperCAmelCase : int = int(_lowercase )
return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value] )
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Optional[int] = int(_lowercase )
return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value] )
def __lowerCamelCase ( _lowercase ) -> str:
return {"yes": True, "no": False}[value.lower()]
class UpperCamelCase_ ( argparse.RawDescriptionHelpFormatter ):
def _lowercase( self , A , A , A , A ) -> Dict:
UpperCAmelCase : Optional[Any] = super()._format_usage(A , A , A , A )
UpperCAmelCase : Tuple = usage.replace("""<command> [<args>] """ , """""" )
return usage
| 338 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase_ ( __magic_name__ ):
lowercase = ['image_processor', 'tokenizer']
lowercase = 'CLIPImageProcessor'
lowercase = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , A=None , A=None , **A ) -> Union[str, Any]:
UpperCAmelCase : int = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , A , )
UpperCAmelCase : List[Any] = kwargs.pop("""feature_extractor""" )
UpperCAmelCase : int = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(A , A )
def __call__( self , A=None , A=None , A=None , **A ) -> Tuple:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
UpperCAmelCase : int = self.tokenizer(A , return_tensors=A , **A )
if images is not None:
UpperCAmelCase : List[str] = self.image_processor(A , return_tensors=A , **A )
if text is not None and images is not None:
UpperCAmelCase : Any = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A ) , tensor_type=A )
def _lowercase( self , *A , **A ) -> str:
return self.tokenizer.batch_decode(*A , **A )
def _lowercase( self , *A , **A ) -> List[str]:
return self.tokenizer.decode(*A , **A )
@property
def _lowercase( self ) -> Dict:
UpperCAmelCase : Dict = self.tokenizer.model_input_names
UpperCAmelCase : Tuple = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _lowercase( self ) -> Optional[int]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A , )
return self.image_processor_class
@property
def _lowercase( self ) -> Optional[int]:
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , A , )
return self.image_processor
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase : str = [[0 for i in range(_lowercase )] for j in range(_lowercase )]
# since string of zero length match pattern of zero length
UpperCAmelCase : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowercase ):
UpperCAmelCase : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowercase ):
UpperCAmelCase : Optional[Any] = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _lowercase ):
for j in range(1 , _lowercase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase : Union[str, Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase : List[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase : Optional[int] = dp[i - 1][j]
else:
UpperCAmelCase : Any = 0
else:
UpperCAmelCase : str = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a : List[str] = """aab"""
a : Optional[int] = """c*a*b"""
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 338 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Dict = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
a : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int:
UpperCAmelCase : int = 1
UpperCAmelCase : str = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase : Tuple = pre_numerator
UpperCAmelCase : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1
UpperCAmelCase : Union[str, Any] = cur_numerator
UpperCAmelCase : Optional[int] = e_cont * pre_numerator + temp
return sum_digits(_lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> bool:
if not isinstance(_lowercase , _lowercase ):
UpperCAmelCase : List[str] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowercase )
if number < 0:
return False
UpperCAmelCase : List[Any] = number * number
while number > 0:
if number % 1_0 != number_square % 1_0:
return False
number //= 1_0
number_square //= 1_0
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=0.0_1 , A=1000 ) -> List[str]:
UpperCAmelCase : List[Any] = p_stop
UpperCAmelCase : Optional[int] = max_length
def __iter__( self ) -> Union[str, Any]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase : Any = random.random() < self.p_stop
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A , A , A=False , A=True ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
UpperCAmelCase : List[str] = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def _lowercase( self ) -> Union[str, Any]:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : Tuple = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(A , A )
def _lowercase( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def _lowercase( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : str = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def _lowercase( self ) -> List[Any]:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase : List[str] = [BatchSamplerShard(A , 2 , A , even_batches=A ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def _lowercase( self , A , A , A , A=False , A=2 , A=False ) -> Tuple:
random.seed(A )
UpperCAmelCase : Dict = list(A )
UpperCAmelCase : Any = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
UpperCAmelCase : Dict = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(A )
iterable_dataset_lists.append(list(A ) )
UpperCAmelCase : Optional[Any] = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
UpperCAmelCase : List[Any] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
UpperCAmelCase : List[Any] = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = 42
UpperCAmelCase : List[Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
UpperCAmelCase : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase : Optional[Any] = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _lowercase( self ) -> Dict:
Accelerator()
UpperCAmelCase : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 338 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
a : Optional[Any] = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A = 101 ) -> Tuple:
UpperCAmelCase : Optional[int] = length
def __len__( self ) -> str:
return self.length
def __getitem__( self , A ) -> int:
return i
class UpperCamelCase_ :
def __call__( self , A ) -> int:
return {"input_ids": torch.tensor(A ), "labels": torch.tensor(A )}
class UpperCamelCase_ ( nn.Module ):
def __init__( self ) -> Union[str, Any]:
super().__init__()
# Add some (unused) params otherwise DDP will complain.
UpperCAmelCase : Any = nn.Linear(120 , 80 )
def _lowercase( self , A , A=None ) -> int:
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class UpperCamelCase_ ( __magic_name__ ):
@require_torch_neuroncore
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : str = f'''--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
UpperCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
UpperCAmelCase : Tuple = f'''--output_dir {output_dir}'''.split()
UpperCAmelCase : Optional[int] = ["""torchrun"""] + distributed_args + args
execute_subprocess_async(A , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class UpperCamelCase_ ( __magic_name__ ):
@require_torch_multi_gpu
def _lowercase( self ) -> Dict:
UpperCAmelCase : Union[str, Any] = f'''--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
UpperCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
UpperCAmelCase : Union[str, Any] = f'''--output_dir {output_dir}'''.split()
UpperCAmelCase : List[str] = ["""torchrun"""] + distributed_args + args
execute_subprocess_async(A , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
a : List[Any] = HfArgumentParser((TrainingArguments,))
a : Optional[int] = parser.parse_args_into_dataclasses()[0]
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [1_0_1, 4_0, 7]:
a : Dict = DummyDataset(dataset_length)
def __lowerCamelCase ( _lowercase ) -> Dict:
UpperCAmelCase : Dict = list(range(len(_lowercase ) ) )
UpperCAmelCase : Union[str, Any] = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"""Predictions and/or labels do not match expected results:\n - predictions: """
F'''{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}''' )
return {"success": success}
a : Tuple = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
a : Any = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
a : List[str] = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
a : List[str] = 2
a : Optional[Any] = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
a : Any = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
a : int = None
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a : str = {
"""sample_size""": 3_2,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": 1_0_0_0,
"""block_out_channels""": [3_2, 6_4],
"""attention_head_dim""": 8,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
a : Any = {
"""sample_size""": 6_4,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 3,
"""num_class_embeds""": 1_0_0_0,
"""block_out_channels""": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
"""attention_head_dim""": 6_4,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
a : Dict = {
"""sample_size""": 2_5_6,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": None,
"""block_out_channels""": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
"""attention_head_dim""": 6_4,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """default""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
a : Tuple = {
"""num_train_timesteps""": 4_0,
"""sigma_min""": 0.0_0_2,
"""sigma_max""": 8_0.0,
}
a : List[str] = {
"""num_train_timesteps""": 2_0_1,
"""sigma_min""": 0.0_0_2,
"""sigma_max""": 8_0.0,
}
a : Any = {
"""num_train_timesteps""": 1_5_1,
"""sigma_min""": 0.0_0_2,
"""sigma_max""": 8_0.0,
}
def __lowerCamelCase ( _lowercase ) -> List[str]:
if isinstance(_lowercase , _lowercase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("""boolean value expected""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=False ) -> int:
UpperCAmelCase : str = checkpoint[F'''{old_prefix}.in_layers.0.weight''']
UpperCAmelCase : Union[str, Any] = checkpoint[F'''{old_prefix}.in_layers.0.bias''']
UpperCAmelCase : Any = checkpoint[F'''{old_prefix}.in_layers.2.weight''']
UpperCAmelCase : Dict = checkpoint[F'''{old_prefix}.in_layers.2.bias''']
UpperCAmelCase : Optional[int] = checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
UpperCAmelCase : str = checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
UpperCAmelCase : Tuple = checkpoint[F'''{old_prefix}.out_layers.0.weight''']
UpperCAmelCase : Dict = checkpoint[F'''{old_prefix}.out_layers.0.bias''']
UpperCAmelCase : Union[str, Any] = checkpoint[F'''{old_prefix}.out_layers.3.weight''']
UpperCAmelCase : List[str] = checkpoint[F'''{old_prefix}.out_layers.3.bias''']
if has_skip:
UpperCAmelCase : Tuple = checkpoint[F'''{old_prefix}.skip_connection.weight''']
UpperCAmelCase : Optional[int] = checkpoint[F'''{old_prefix}.skip_connection.bias''']
return new_checkpoint
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=None ) -> Union[str, Any]:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Union[str, Any] = checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3 , dim=0 )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[int] = checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3 , dim=0 )
UpperCAmelCase : Tuple = checkpoint[F'''{old_prefix}.norm.weight''']
UpperCAmelCase : Optional[int] = checkpoint[F'''{old_prefix}.norm.bias''']
UpperCAmelCase : Optional[Any] = weight_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase : Any = bias_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase : str = weight_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase : str = bias_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase : str = weight_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase : Optional[int] = bias_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase : List[str] = (
checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
)
UpperCAmelCase : Optional[int] = checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def __lowerCamelCase ( _lowercase , _lowercase ) -> Optional[int]:
UpperCAmelCase : int = torch.load(_lowercase , map_location="""cpu""" )
UpperCAmelCase : List[str] = {}
UpperCAmelCase : Any = checkpoint["""time_embed.0.weight"""]
UpperCAmelCase : Optional[int] = checkpoint["""time_embed.0.bias"""]
UpperCAmelCase : Any = checkpoint["""time_embed.2.weight"""]
UpperCAmelCase : List[Any] = checkpoint["""time_embed.2.bias"""]
if unet_config["num_class_embeds"] is not None:
UpperCAmelCase : List[str] = checkpoint["""label_emb.weight"""]
UpperCAmelCase : Optional[int] = checkpoint["""input_blocks.0.0.weight"""]
UpperCAmelCase : Dict = checkpoint["""input_blocks.0.0.bias"""]
UpperCAmelCase : List[Any] = unet_config["""down_block_types"""]
UpperCAmelCase : Optional[int] = unet_config["""layers_per_block"""]
UpperCAmelCase : Union[str, Any] = unet_config["""attention_head_dim"""]
UpperCAmelCase : Any = unet_config["""block_out_channels"""]
UpperCAmelCase : Any = 1
UpperCAmelCase : str = channels_list[0]
for i, layer_type in enumerate(_lowercase ):
UpperCAmelCase : List[str] = channels_list[i]
UpperCAmelCase : Optional[int] = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(_lowercase ):
UpperCAmelCase : Union[str, Any] = F'''down_blocks.{i}.resnets.{j}'''
UpperCAmelCase : int = F'''input_blocks.{current_layer}.0'''
UpperCAmelCase : int = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase : Dict = convert_resnet(_lowercase , _lowercase , _lowercase , _lowercase , has_skip=_lowercase )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(_lowercase ):
UpperCAmelCase : str = F'''down_blocks.{i}.resnets.{j}'''
UpperCAmelCase : Union[str, Any] = F'''input_blocks.{current_layer}.0'''
UpperCAmelCase : Dict = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase : str = convert_resnet(_lowercase , _lowercase , _lowercase , _lowercase , has_skip=_lowercase )
UpperCAmelCase : Union[str, Any] = F'''down_blocks.{i}.attentions.{j}'''
UpperCAmelCase : Any = F'''input_blocks.{current_layer}.1'''
UpperCAmelCase : Optional[Any] = convert_attention(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
current_layer += 1
if i != len(_lowercase ) - 1:
UpperCAmelCase : Dict = F'''down_blocks.{i}.downsamplers.0'''
UpperCAmelCase : Dict = F'''input_blocks.{current_layer}.0'''
UpperCAmelCase : Optional[Any] = convert_resnet(_lowercase , _lowercase , _lowercase , _lowercase )
current_layer += 1
UpperCAmelCase : Any = current_channels
# hardcoded the mid-block for now
UpperCAmelCase : Any = """mid_block.resnets.0"""
UpperCAmelCase : Any = """middle_block.0"""
UpperCAmelCase : Any = convert_resnet(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : List[str] = """mid_block.attentions.0"""
UpperCAmelCase : List[str] = """middle_block.1"""
UpperCAmelCase : Union[str, Any] = convert_attention(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = """mid_block.resnets.1"""
UpperCAmelCase : List[Any] = """middle_block.2"""
UpperCAmelCase : Dict = convert_resnet(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Optional[Any] = unet_config["""up_block_types"""]
for i, layer_type in enumerate(_lowercase ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase : str = F'''up_blocks.{i}.resnets.{j}'''
UpperCAmelCase : Tuple = F'''output_blocks.{current_layer}.0'''
UpperCAmelCase : int = convert_resnet(_lowercase , _lowercase , _lowercase , _lowercase , has_skip=_lowercase )
current_layer += 1
if i != len(_lowercase ) - 1:
UpperCAmelCase : Dict = F'''up_blocks.{i}.upsamplers.0'''
UpperCAmelCase : str = F'''output_blocks.{current_layer-1}.1'''
UpperCAmelCase : Dict = convert_resnet(_lowercase , _lowercase , _lowercase , _lowercase )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase : Union[str, Any] = F'''up_blocks.{i}.resnets.{j}'''
UpperCAmelCase : List[str] = F'''output_blocks.{current_layer}.0'''
UpperCAmelCase : Any = convert_resnet(_lowercase , _lowercase , _lowercase , _lowercase , has_skip=_lowercase )
UpperCAmelCase : int = F'''up_blocks.{i}.attentions.{j}'''
UpperCAmelCase : Optional[int] = F'''output_blocks.{current_layer}.1'''
UpperCAmelCase : Dict = convert_attention(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
current_layer += 1
if i != len(_lowercase ) - 1:
UpperCAmelCase : List[str] = F'''up_blocks.{i}.upsamplers.0'''
UpperCAmelCase : List[str] = F'''output_blocks.{current_layer-1}.2'''
UpperCAmelCase : List[Any] = convert_resnet(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = checkpoint["""out.0.weight"""]
UpperCAmelCase : Tuple = checkpoint["""out.0.bias"""]
UpperCAmelCase : Dict = checkpoint["""out.2.weight"""]
UpperCAmelCase : List[str] = checkpoint["""out.2.bias"""]
return new_checkpoint
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
parser.add_argument(
"""--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
)
parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")
a : List[str] = parser.parse_args()
a : Any = strabool(args.class_cond)
a : Union[str, Any] = os.path.basename(args.unet_path)
print(F'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
a : Any = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
a : str = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
a : str = TEST_UNET_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
a : Tuple = None
a : Tuple = con_pt_to_diffuser(args.unet_path, unet_config)
a : Union[str, Any] = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
a : Optional[int] = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
a : int = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
a : List[Any] = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
a : Any = CMStochasticIterativeScheduler(**scheduler_config)
a : List[str] = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 338 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
for i in range(_lowercase , _lowercase ):
if array[i] == target:
return i
return -1
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
UpperCAmelCase : Tuple = 0
UpperCAmelCase : List[str] = len(_lowercase )
while left <= right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = (left + right) // 3 + 1
UpperCAmelCase : Union[str, Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCAmelCase : Any = one_third - 1
elif array[two_third] < target:
UpperCAmelCase : Tuple = two_third + 1
else:
UpperCAmelCase : int = one_third + 1
UpperCAmelCase : List[Any] = two_third - 1
else:
return -1
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = (left + right) // 3 + 1
UpperCAmelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowercase , one_third - 1 , _lowercase , _lowercase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowercase , _lowercase , _lowercase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowercase , _lowercase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Any = input("""Enter numbers separated by comma:\n""").strip()
a : Any = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
a : Tuple = int(input("""Enter the number to be found in the list:\n""").strip())
a : Union[str, Any] = ite_ternary_search(collection, target)
a : Optional[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 338 | 1 |
'''simple docstring'''
import argparse
a : Optional[int] = """docs/source/_static/js/custom.js"""
def __lowerCamelCase ( _lowercase ) -> Tuple:
with open(_lowercase , encoding="""utf-8""" , newline="""\n""" ) as f:
UpperCAmelCase : List[str] = f.readlines()
UpperCAmelCase : str = 0
# First let's put the right version
while not lines[index].startswith("""const stableVersion =""" ):
index += 1
UpperCAmelCase : Optional[int] = F'''const stableVersion = "v{version}"\n'''
# Then update the dictionary
while not lines[index].startswith("""const versionMapping = {""" ):
index += 1
# We go until the end
while not lines[index].startswith("""}""" ):
index += 1
# We add the new version at the end
lines[index - 1] += F''' "v{version}": "v{version}",\n'''
with open(_lowercase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""--version""", help="""Release version.""")
a : Any = parser.parse_args()
update_custom_js(args.version)
| 338 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Optional[int] = 0
def __eq__( self , A ) -> Optional[Any]:
return self.position == cell.position
def _lowercase( self ) -> Tuple:
print(self.position )
class UpperCamelCase_ :
def __init__( self , A=(5, 5) ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = np.zeros(A )
UpperCAmelCase : int = world_size[0]
UpperCAmelCase : List[str] = world_size[1]
def _lowercase( self ) -> List[Any]:
print(self.w )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : Optional[Any] = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCAmelCase : List[Any] = cell.position[0]
UpperCAmelCase : Union[str, Any] = cell.position[1]
UpperCAmelCase : Optional[int] = []
for n in neughbour_cord:
UpperCAmelCase : Any = current_x + n[0]
UpperCAmelCase : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCAmelCase : str = Cell()
UpperCAmelCase : List[str] = (x, y)
UpperCAmelCase : Dict = cell
neighbours.append(A )
return neighbours
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
_open.append(_lowercase )
while _open:
UpperCAmelCase : Any = np.argmin([n.f for n in _open] )
UpperCAmelCase : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowercase ) )
if current == goal:
break
for n in world.get_neigbours(_lowercase ):
for c in _closed:
if c == n:
continue
UpperCAmelCase : List[str] = current.g + 1
UpperCAmelCase , UpperCAmelCase : List[str] = n.position
UpperCAmelCase , UpperCAmelCase : Dict = goal.position
UpperCAmelCase : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCAmelCase : Dict = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowercase )
UpperCAmelCase : Dict = []
while current.parent is not None:
path.append(current.position )
UpperCAmelCase : Optional[int] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a : List[str] = Gridworld()
# Start position and goal
a : Optional[int] = Cell()
a : Optional[Any] = (0, 0)
a : Optional[Any] = Cell()
a : str = (4, 4)
print(F'''path from {start.position} to {goal.position}''')
a : List[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a : Any = 1
print(world.w)
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> list[int]:
UpperCAmelCase : List[str] = int(_lowercase )
# Initialize Result
UpperCAmelCase : Tuple = []
# Traverse through all denomination
for denomination in reversed(_lowercase ):
# Find denominations
while int(_lowercase ) >= int(_lowercase ):
total_value -= int(_lowercase )
answer.append(_lowercase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
a : Dict = []
a : Any = """0"""
if (
input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
== "y"
):
a : List[Any] = int(input("""Enter the number of denominations you want to add: """).strip())
for i in range(0, n):
denominations.append(int(input(F'''Denomination {i}: ''').strip()))
a : Optional[Any] = input("""Enter the change you want to make in Indian Currency: """).strip()
else:
# All denominations of Indian Currency if user does not enter
a : List[str] = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
a : int = input("""Enter the change you want to make: """).strip()
if int(value) == 0 or int(value) < 0:
print("""The total value cannot be zero or negative.""")
else:
print(F'''Following is minimal change for {value}: ''')
a : str = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=""" """)
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
a : int = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use SegformerImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338 | 1 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 1 |
'''simple docstring'''
class UpperCamelCase_ :
def __init__( self , A , A , A ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = name
UpperCAmelCase : Any = value
UpperCAmelCase : Dict = weight
def __repr__( self ) -> List[Any]:
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _lowercase( self ) -> str:
return self.value
def _lowercase( self ) -> Dict:
return self.name
def _lowercase( self ) -> Union[str, Any]:
return self.weight
def _lowercase( self ) -> List[Any]:
return self.value / self.weight
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = []
for i in range(len(_lowercase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Any:
UpperCAmelCase : List[Any] = sorted(_lowercase , key=_lowercase , reverse=_lowercase )
UpperCAmelCase : str = []
UpperCAmelCase , UpperCAmelCase : Optional[int] = 0.0, 0.0
for i in range(len(_lowercase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __lowerCamelCase ( ) -> Optional[int]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = split_dict._to_yaml_list()
assert len(_lowercase ) == len(_lowercase )
UpperCAmelCase : List[Any] = SplitDict._from_yaml_list(_lowercase )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCAmelCase : List[str] = None
# the split name of split_dict takes over the name of the split info object
UpperCAmelCase : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=_lowercase ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCAmelCase : Optional[Any] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a : Optional[Any] = logging.get_logger(__name__)
a : List[str] = {
"""microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}
class UpperCamelCase_ ( __magic_name__ , __magic_name__ ):
lowercase = 'focalnet'
def __init__( self , A=224 , A=4 , A=3 , A=96 , A=False , A=[192, 384, 768, 768] , A=[2, 2, 6, 2] , A=[2, 2, 2, 2] , A=[3, 3, 3, 3] , A="gelu" , A=4.0 , A=0.0 , A=0.1 , A=False , A=1e-4 , A=False , A=False , A=False , A=0.0_2 , A=1e-5 , A=32 , A=None , A=None , **A , ) -> Any:
super().__init__(**A )
UpperCAmelCase : str = image_size
UpperCAmelCase : str = patch_size
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = embed_dim
UpperCAmelCase : List[Any] = use_conv_embed
UpperCAmelCase : Optional[Any] = hidden_sizes
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : str = focal_levels
UpperCAmelCase : Any = focal_windows
UpperCAmelCase : Union[str, Any] = hidden_act
UpperCAmelCase : str = mlp_ratio
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : Dict = drop_path_rate
UpperCAmelCase : Tuple = use_layerscale
UpperCAmelCase : List[str] = layerscale_value
UpperCAmelCase : Tuple = use_post_layernorm
UpperCAmelCase : Tuple = use_post_layernorm_in_modulation
UpperCAmelCase : Optional[int] = normalize_modulator
UpperCAmelCase : List[Any] = initializer_range
UpperCAmelCase : Dict = layer_norm_eps
UpperCAmelCase : List[str] = encoder_stride
UpperCAmelCase : str = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
UpperCAmelCase , UpperCAmelCase : Optional[int] = get_aligned_output_features_output_indices(
out_features=A , out_indices=A , stage_names=self.stage_names )
| 338 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
a : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
def __lowerCamelCase ( _lowercase ) -> Union[str, Any]:
warnings.warn(
"""The preprocess method is deprecated and will be removed in a future version. Please"""
""" use VaeImageProcessor.preprocess instead""" , _lowercase , )
if isinstance(_lowercase , torch.Tensor ):
return image
elif isinstance(_lowercase , PIL.Image.Image ):
UpperCAmelCase : int = [image]
if isinstance(image[0] , PIL.Image.Image ):
UpperCAmelCase , UpperCAmelCase : Optional[Any] = image[0].size
UpperCAmelCase , UpperCAmelCase : List[Any] = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
UpperCAmelCase : Dict = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
UpperCAmelCase : str = np.concatenate(_lowercase , axis=0 )
UpperCAmelCase : str = np.array(_lowercase ).astype(np.floataa ) / 255.0
UpperCAmelCase : Union[str, Any] = image.transpose(0 , 3 , 1 , 2 )
UpperCAmelCase : List[str] = 2.0 * image - 1.0
UpperCAmelCase : List[Any] = torch.from_numpy(_lowercase )
elif isinstance(image[0] , torch.Tensor ):
UpperCAmelCase : Tuple = torch.cat(_lowercase , dim=0 )
return image
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if isinstance(_lowercase , torch.Tensor ):
return mask
elif isinstance(_lowercase , PIL.Image.Image ):
UpperCAmelCase : Optional[Any] = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
UpperCAmelCase , UpperCAmelCase : str = mask[0].size
UpperCAmelCase , UpperCAmelCase : Optional[int] = (x - x % 3_2 for x in (w, h)) # resize to integer multiple of 32
UpperCAmelCase : Any = [np.array(m.convert("""L""" ).resize((w, h) , resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask]
UpperCAmelCase : Union[str, Any] = np.concatenate(_lowercase , axis=0 )
UpperCAmelCase : List[Any] = mask.astype(np.floataa ) / 255.0
UpperCAmelCase : List[Any] = 0
UpperCAmelCase : Dict = 1
UpperCAmelCase : Dict = torch.from_numpy(_lowercase )
elif isinstance(mask[0] , torch.Tensor ):
UpperCAmelCase : Union[str, Any] = torch.cat(_lowercase , dim=0 )
return mask
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 42
lowercase = 42
def __init__( self , A , A ) -> str:
super().__init__()
self.register_modules(unet=A , scheduler=A )
@torch.no_grad()
def __call__( self , A , A , A = 250 , A = 0.0 , A = 10 , A = 10 , A = None , A = "pil" , A = True , ) -> Union[ImagePipelineOutput, Tuple]:
UpperCAmelCase : str = image
UpperCAmelCase : Dict = _preprocess_image(A )
UpperCAmelCase : Optional[int] = original_image.to(device=self.device , dtype=self.unet.dtype )
UpperCAmelCase : str = _preprocess_mask(A )
UpperCAmelCase : List[str] = mask_image.to(device=self.device , dtype=self.unet.dtype )
UpperCAmelCase : Union[str, Any] = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(A )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
UpperCAmelCase : str = original_image.shape
UpperCAmelCase : Optional[Any] = randn_tensor(A , generator=A , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(A , A , A , self.device )
UpperCAmelCase : List[str] = eta
UpperCAmelCase : Optional[int] = self.scheduler.timesteps[0] + 1
UpperCAmelCase : List[Any] = generator[0] if isinstance(A , A ) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
if t < t_last:
# predict the noise residual
UpperCAmelCase : List[Any] = self.unet(A , A ).sample
# compute previous image: x_t -> x_t-1
UpperCAmelCase : str = self.scheduler.step(A , A , A , A , A , A ).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
UpperCAmelCase : Tuple = self.scheduler.undo_step(A , A , A )
UpperCAmelCase : str = t
UpperCAmelCase : Optional[int] = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : List[str] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : Optional[Any] = self.numpy_to_pil(A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A )
| 338 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a : Dict = None
a : Optional[int] = logging.get_logger(__name__)
a : int = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
a : List[Any] = {
"""vocab_file""": {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
},
}
a : Dict = {
"""xlnet-base-cased""": None,
"""xlnet-large-cased""": None,
}
a : Dict = """▁"""
# Segments (not really needed)
a : Dict = 0
a : Any = 1
a : Optional[Any] = 2
a : Tuple = 3
a : str = 4
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = 'left'
lowercase = XLNetTokenizer
def __init__( self , A=None , A=None , A=False , A=True , A=False , A="<s>" , A="</s>" , A="<unk>" , A="<sep>" , A="<pad>" , A="<cls>" , A="<mask>" , A=["<eop>", "<eod>"] , **A , ) -> Tuple:
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : Any = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else mask_token
super().__init__(
vocab_file=A , tokenizer_file=A , do_lower_case=A , remove_space=A , keep_accents=A , bos_token=A , eos_token=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , additional_special_tokens=A , **A , )
UpperCAmelCase : Optional[Any] = 3
UpperCAmelCase : Union[str, Any] = do_lower_case
UpperCAmelCase : str = remove_space
UpperCAmelCase : List[str] = keep_accents
UpperCAmelCase : str = vocab_file
UpperCAmelCase : List[str] = False if not self.vocab_file else True
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : int = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : str = [self.sep_token_id]
UpperCAmelCase : Tuple = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _lowercase( self , A , A = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : Union[str, Any] = os.path.join(
A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
return (out_vocab_file,)
| 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class UpperCamelCase_ ( unittest.TestCase ):
def __init__( self , A , A=7 , A=3 , A=30 , A=400 , A=True , A=None , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , A=True , A=1 / 255 , A=True , ) -> Optional[Any]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
UpperCAmelCase : str = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
UpperCAmelCase : str = parent
UpperCAmelCase : List[str] = batch_size
UpperCAmelCase : Tuple = num_channels
UpperCAmelCase : Union[str, Any] = min_resolution
UpperCAmelCase : str = max_resolution
UpperCAmelCase : Dict = do_resize
UpperCAmelCase : List[Any] = size
UpperCAmelCase : List[str] = do_normalize
UpperCAmelCase : str = image_mean
UpperCAmelCase : Optional[Any] = image_std
UpperCAmelCase : Dict = do_rescale
UpperCAmelCase : Optional[Any] = rescale_factor
UpperCAmelCase : Union[str, Any] = do_pad
def _lowercase( self ) -> Union[str, Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _lowercase( self , A , A=False ) -> Any:
if not batched:
UpperCAmelCase : List[str] = image_inputs[0]
if isinstance(A , Image.Image ):
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = image.size
else:
UpperCAmelCase , UpperCAmelCase : Tuple = image.shape[1], image.shape[2]
if w < h:
UpperCAmelCase : Optional[Any] = int(self.size["""shortest_edge"""] * h / w )
UpperCAmelCase : Any = self.size["""shortest_edge"""]
elif w > h:
UpperCAmelCase : Dict = self.size["""shortest_edge"""]
UpperCAmelCase : Union[str, Any] = int(self.size["""shortest_edge"""] * w / h )
else:
UpperCAmelCase : Optional[int] = self.size["""shortest_edge"""]
UpperCAmelCase : Tuple = self.size["""shortest_edge"""]
else:
UpperCAmelCase : Union[str, Any] = []
for image in image_inputs:
UpperCAmelCase , UpperCAmelCase : List[str] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCAmelCase : List[Any] = max(A , key=lambda A : item[0] )[0]
UpperCAmelCase : Tuple = max(A , key=lambda A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCamelCase_ ( __magic_name__ , unittest.TestCase ):
lowercase = ConditionalDetrImageProcessor if is_vision_available() else None
def _lowercase( self ) -> Dict:
UpperCAmelCase : Optional[int] = ConditionalDetrImageProcessingTester(self )
@property
def _lowercase( self ) -> str:
return self.image_processor_tester.prepare_image_processor_dict()
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , """image_mean""" ) )
self.assertTrue(hasattr(A , """image_std""" ) )
self.assertTrue(hasattr(A , """do_normalize""" ) )
self.assertTrue(hasattr(A , """do_resize""" ) )
self.assertTrue(hasattr(A , """size""" ) )
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1333} )
self.assertEqual(image_processor.do_pad , A )
UpperCAmelCase : int = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , A )
def _lowercase( self ) -> str:
pass
def _lowercase( self ) -> Union[str, Any]:
# Initialize image_processing
UpperCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
UpperCAmelCase : Optional[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCAmelCase , UpperCAmelCase : str = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase , UpperCAmelCase : Any = self.image_processor_tester.get_expected_values(A , batched=A )
UpperCAmelCase : List[Any] = image_processing(A , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowercase( self ) -> Optional[Any]:
# Initialize image_processing
UpperCAmelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
UpperCAmelCase : Optional[int] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCAmelCase , UpperCAmelCase : List[Any] = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase : Dict = image_processing(A , return_tensors="""pt""" ).pixel_values
UpperCAmelCase , UpperCAmelCase : Tuple = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowercase( self ) -> Optional[int]:
# Initialize image_processing
UpperCAmelCase : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
UpperCAmelCase : Tuple = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCAmelCase , UpperCAmelCase : Tuple = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase : Tuple = image_processing(A , return_tensors="""pt""" ).pixel_values
UpperCAmelCase , UpperCAmelCase : Dict = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _lowercase( self ) -> List[Any]:
# prepare image and target
UpperCAmelCase : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
UpperCAmelCase : str = json.loads(f.read() )
UpperCAmelCase : Optional[int] = {"""image_id""": 39769, """annotations""": target}
# encode them
UpperCAmelCase : Optional[int] = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""" )
UpperCAmelCase : Tuple = image_processing(images=A , annotations=A , return_tensors="""pt""" )
# verify pixel values
UpperCAmelCase : Dict = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , A )
UpperCAmelCase : Tuple = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
UpperCAmelCase : List[str] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , A ) )
# verify boxes
UpperCAmelCase : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , A )
UpperCAmelCase : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , A , atol=1e-3 ) )
# verify image_id
UpperCAmelCase : Union[str, Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , A ) )
# verify is_crowd
UpperCAmelCase : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , A ) )
# verify class_labels
UpperCAmelCase : Tuple = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , A ) )
# verify orig_size
UpperCAmelCase : Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , A ) )
# verify size
UpperCAmelCase : Dict = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , A ) )
@slow
def _lowercase( self ) -> Optional[Any]:
# prepare image, target and masks_path
UpperCAmelCase : int = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
UpperCAmelCase : str = json.loads(f.read() )
UpperCAmelCase : Tuple = {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}
UpperCAmelCase : Any = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
UpperCAmelCase : str = ConditionalDetrImageProcessor(format="""coco_panoptic""" )
UpperCAmelCase : Any = image_processing(images=A , annotations=A , masks_path=A , return_tensors="""pt""" )
# verify pixel values
UpperCAmelCase : List[str] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , A )
UpperCAmelCase : Union[str, Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
UpperCAmelCase : Dict = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , A ) )
# verify boxes
UpperCAmelCase : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , A )
UpperCAmelCase : List[str] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , A , atol=1e-3 ) )
# verify image_id
UpperCAmelCase : Dict = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , A ) )
# verify is_crowd
UpperCAmelCase : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , A ) )
# verify class_labels
UpperCAmelCase : int = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , A ) )
# verify masks
UpperCAmelCase : Optional[Any] = 822873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , A )
# verify orig_size
UpperCAmelCase : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , A ) )
# verify size
UpperCAmelCase : Union[str, Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , A ) )
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( __magic_name__ ):
lowercase = (DEISMultistepScheduler,)
lowercase = (('num_inference_steps', 25),)
def _lowercase( self , **A ) -> str:
UpperCAmelCase : Any = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
}
config.update(**A )
return config
def _lowercase( self , A=0 , **A ) -> Any:
UpperCAmelCase : str = dict(self.forward_default_kwargs )
UpperCAmelCase : Any = kwargs.pop("""num_inference_steps""" , A )
UpperCAmelCase : List[Any] = self.dummy_sample
UpperCAmelCase : Optional[int] = 0.1 * sample
UpperCAmelCase : str = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : Optional[int] = self.get_scheduler_config(**A )
UpperCAmelCase : str = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals
UpperCAmelCase : Union[str, Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A )
UpperCAmelCase : Dict = scheduler_class.from_pretrained(A )
new_scheduler.set_timesteps(A )
# copy over dummy past residuals
UpperCAmelCase : Any = dummy_past_residuals[: new_scheduler.config.solver_order]
UpperCAmelCase , UpperCAmelCase : Optional[Any] = sample, sample
for t in range(A , time_step + scheduler.config.solver_order + 1 ):
UpperCAmelCase : int = scheduler.step(A , A , A , **A ).prev_sample
UpperCAmelCase : Any = new_scheduler.step(A , A , A , **A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowercase( self ) -> Optional[Any]:
pass
def _lowercase( self , A=0 , **A ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = dict(self.forward_default_kwargs )
UpperCAmelCase : Union[str, Any] = kwargs.pop("""num_inference_steps""" , A )
UpperCAmelCase : Optional[int] = self.dummy_sample
UpperCAmelCase : Tuple = 0.1 * sample
UpperCAmelCase : Any = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : Any = self.get_scheduler_config()
UpperCAmelCase : str = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase : Any = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A )
UpperCAmelCase : Optional[int] = scheduler_class.from_pretrained(A )
# copy over dummy past residuals
new_scheduler.set_timesteps(A )
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase : List[str] = dummy_past_residuals[: new_scheduler.config.solver_order]
UpperCAmelCase : int = scheduler.step(A , A , A , **A ).prev_sample
UpperCAmelCase : Tuple = new_scheduler.step(A , A , A , **A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowercase( self , A=None , **A ) -> int:
if scheduler is None:
UpperCAmelCase : List[str] = self.scheduler_classes[0]
UpperCAmelCase : Optional[Any] = self.get_scheduler_config(**A )
UpperCAmelCase : int = scheduler_class(**A )
UpperCAmelCase : List[Any] = self.scheduler_classes[0]
UpperCAmelCase : Dict = self.get_scheduler_config(**A )
UpperCAmelCase : Any = scheduler_class(**A )
UpperCAmelCase : Any = 10
UpperCAmelCase : int = self.dummy_model()
UpperCAmelCase : str = self.dummy_sample_deter
scheduler.set_timesteps(A )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : List[str] = model(A , A )
UpperCAmelCase : str = scheduler.step(A , A , A ).prev_sample
return sample
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[Any] = dict(self.forward_default_kwargs )
UpperCAmelCase : List[str] = kwargs.pop("""num_inference_steps""" , A )
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : Union[str, Any] = self.get_scheduler_config()
UpperCAmelCase : Any = scheduler_class(**A )
UpperCAmelCase : str = self.dummy_sample
UpperCAmelCase : List[str] = 0.1 * sample
if num_inference_steps is not None and hasattr(A , """set_timesteps""" ):
scheduler.set_timesteps(A )
elif num_inference_steps is not None and not hasattr(A , """set_timesteps""" ):
UpperCAmelCase : Optional[int] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase : List[str] = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
UpperCAmelCase : int = dummy_past_residuals[: scheduler.config.solver_order]
UpperCAmelCase : Optional[int] = scheduler.timesteps[5]
UpperCAmelCase : List[Any] = scheduler.timesteps[6]
UpperCAmelCase : Dict = scheduler.step(A , A , A , **A ).prev_sample
UpperCAmelCase : Tuple = scheduler.step(A , A , A , **A ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _lowercase( self ) -> List[str]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
UpperCAmelCase : int = DEISMultistepScheduler(**self.get_scheduler_config() )
UpperCAmelCase : int = self.full_loop(scheduler=A )
UpperCAmelCase : Optional[Any] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3
UpperCAmelCase : Optional[int] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
UpperCAmelCase : Dict = DPMSolverMultistepScheduler.from_config(scheduler.config )
UpperCAmelCase : List[Any] = UniPCMultistepScheduler.from_config(scheduler.config )
UpperCAmelCase : int = DEISMultistepScheduler.from_config(scheduler.config )
UpperCAmelCase : Optional[Any] = self.full_loop(scheduler=A )
UpperCAmelCase : str = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3
def _lowercase( self ) -> List[str]:
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=A )
def _lowercase( self ) -> Tuple:
self.check_over_configs(thresholding=A )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=A , prediction_type=A , sample_max_value=A , algorithm_type="""deis""" , solver_order=A , solver_type=A , )
def _lowercase( self ) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def _lowercase( self ) -> Union[str, Any]:
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=A , solver_type=A , prediction_type=A , algorithm_type=A , )
UpperCAmelCase : Dict = self.full_loop(
solver_order=A , solver_type=A , prediction_type=A , algorithm_type=A , )
assert not torch.isnan(A ).any(), "Samples have nan numbers"
def _lowercase( self ) -> List[Any]:
self.check_over_configs(lower_order_final=A )
self.check_over_configs(lower_order_final=A )
def _lowercase( self ) -> Any:
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=A , time_step=0 )
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = self.full_loop()
UpperCAmelCase : Any = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Tuple = self.full_loop(prediction_type="""v_prediction""" )
UpperCAmelCase : Tuple = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.0_9_1 ) < 1e-3
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : List[Any] = self.scheduler_classes[0]
UpperCAmelCase : List[Any] = self.get_scheduler_config(thresholding=A , dynamic_thresholding_ratio=0 )
UpperCAmelCase : Any = scheduler_class(**A )
UpperCAmelCase : List[Any] = 10
UpperCAmelCase : List[Any] = self.dummy_model()
UpperCAmelCase : List[Any] = self.dummy_sample_deter.half()
scheduler.set_timesteps(A )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Any = model(A , A )
UpperCAmelCase : Tuple = scheduler.step(A , A , A ).prev_sample
assert sample.dtype == torch.floataa
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : str = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'levit'
def __init__( self , A=224 , A=3 , A=3 , A=2 , A=1 , A=16 , A=[128, 256, 384] , A=[4, 8, 12] , A=[4, 4, 4] , A=[16, 16, 16] , A=0 , A=[2, 2, 2] , A=[2, 2, 2] , A=0.0_2 , **A , ) -> int:
super().__init__(**A )
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = kernel_size
UpperCAmelCase : Optional[int] = stride
UpperCAmelCase : Dict = padding
UpperCAmelCase : List[Any] = hidden_sizes
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : Any = key_dim
UpperCAmelCase : str = drop_path_rate
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = attention_ratio
UpperCAmelCase : Optional[Any] = mlp_ratio
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : int = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
| 338 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : Optional[int] = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": ["""XLMTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Tuple = [
"""XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLMForMultipleChoice""",
"""XLMForQuestionAnswering""",
"""XLMForQuestionAnsweringSimple""",
"""XLMForSequenceClassification""",
"""XLMForTokenClassification""",
"""XLMModel""",
"""XLMPreTrainedModel""",
"""XLMWithLMHeadModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[int] = [
"""TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLMForMultipleChoice""",
"""TFXLMForQuestionAnsweringSimple""",
"""TFXLMForSequenceClassification""",
"""TFXLMForTokenClassification""",
"""TFXLMMainLayer""",
"""TFXLMModel""",
"""TFXLMPreTrainedModel""",
"""TFXLMWithLMHeadModel""",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : List[str] = """Hello, World!"""
a : List[Any] = """en_XX"""
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Dict = Path("""data_bin""" )
UpperCAmelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowercase ).parent ) , checkpoint_file=Path(_lowercase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowercase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowercase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowercase )
UpperCAmelCase : List[str] = xmod.model.encoder.sentence_encoder
UpperCAmelCase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowercase )
UpperCAmelCase : str = XmodForSequenceClassification(_lowercase ) if classification_head else XmodForMaskedLM(_lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : int = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : List[str] = model.roberta.encoder.layer[i]
UpperCAmelCase : Optional[Any] = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
UpperCAmelCase : List[Any] = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Any = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
UpperCAmelCase : Any = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[str] = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : Tuple = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
UpperCAmelCase : List[str] = xmod_layer.fca.weight
UpperCAmelCase : str = xmod_layer.fca.bias
# output
UpperCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
UpperCAmelCase : Dict = xmod_layer.fca.weight
UpperCAmelCase : Dict = xmod_layer.fca.bias
UpperCAmelCase : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase : Union[str, Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : str = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : List[str] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : List[Any] = bert_output.adapter_modules[lang_code]
UpperCAmelCase : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any = from_adapter.fca.weight
UpperCAmelCase : int = from_adapter.fca.bias
UpperCAmelCase : Dict = from_adapter.fca.weight
UpperCAmelCase : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Tuple = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].dense.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].dense.bias
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].out_proj.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
UpperCAmelCase : Dict = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : str = xmod.model.encoder.lm_head.weight
UpperCAmelCase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : Any = xmod.encode(_lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowercase )
UpperCAmelCase : Optional[int] = model(_lowercase )[0]
if classification_head:
UpperCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase ) )
else:
UpperCAmelCase : Optional[Any] = xmod.model(_lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Dict = torch.allclose(_lowercase , _lowercase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowercase ).mkdir(parents=_lowercase , exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
a : List[str] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 338 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a : Tuple = logging.get_logger(__name__)
a : int = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
a : Optional[Any] = {
"""tokenizer_file""": {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
},
}
a : List[Any] = {
"""gpt-neox-20b""": 2_0_4_8,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ['input_ids', 'attention_mask']
def __init__( self , A=None , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , **A , ) -> Any:
super().__init__(
A , A , tokenizer_file=A , unk_token=A , bos_token=A , eos_token=A , add_prefix_space=A , **A , )
UpperCAmelCase : Union[str, Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , A ) != add_prefix_space:
UpperCAmelCase : Any = getattr(A , pre_tok_state.pop("""type""" ) )
UpperCAmelCase : Dict = add_prefix_space
UpperCAmelCase : Optional[Any] = pre_tok_class(**A )
UpperCAmelCase : int = add_prefix_space
def _lowercase( self , A , A = None ) -> Tuple[str]:
UpperCAmelCase : str = self._tokenizer.model.save(A , name=A )
return tuple(A )
def _lowercase( self , A ) -> List[int]:
UpperCAmelCase : Optional[int] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(A , add_special_tokens=A ) + [self.eos_token_id] )
if len(A ) > self.model_max_length:
UpperCAmelCase : str = input_ids[-self.model_max_length :]
return input_ids
| 338 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def __lowerCamelCase ( _lowercase = 1_0_0_0_0_0_0 , _lowercase = 1_0 ) -> int:
UpperCAmelCase : defaultdict = defaultdict(_lowercase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
UpperCAmelCase : Optional[Any] = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
UpperCAmelCase : Tuple = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(_lowercase , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 1_0 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 | 1 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Optional[int] = 0
def __eq__( self , A ) -> Optional[Any]:
return self.position == cell.position
def _lowercase( self ) -> Tuple:
print(self.position )
class UpperCamelCase_ :
def __init__( self , A=(5, 5) ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = np.zeros(A )
UpperCAmelCase : int = world_size[0]
UpperCAmelCase : List[str] = world_size[1]
def _lowercase( self ) -> List[Any]:
print(self.w )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : Optional[Any] = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCAmelCase : List[Any] = cell.position[0]
UpperCAmelCase : Union[str, Any] = cell.position[1]
UpperCAmelCase : Optional[int] = []
for n in neughbour_cord:
UpperCAmelCase : Any = current_x + n[0]
UpperCAmelCase : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCAmelCase : str = Cell()
UpperCAmelCase : List[str] = (x, y)
UpperCAmelCase : Dict = cell
neighbours.append(A )
return neighbours
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
_open.append(_lowercase )
while _open:
UpperCAmelCase : Any = np.argmin([n.f for n in _open] )
UpperCAmelCase : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowercase ) )
if current == goal:
break
for n in world.get_neigbours(_lowercase ):
for c in _closed:
if c == n:
continue
UpperCAmelCase : List[str] = current.g + 1
UpperCAmelCase , UpperCAmelCase : List[str] = n.position
UpperCAmelCase , UpperCAmelCase : Dict = goal.position
UpperCAmelCase : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCAmelCase : Dict = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowercase )
UpperCAmelCase : Dict = []
while current.parent is not None:
path.append(current.position )
UpperCAmelCase : Optional[int] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a : List[str] = Gridworld()
# Start position and goal
a : Optional[int] = Cell()
a : Optional[Any] = (0, 0)
a : Optional[Any] = Cell()
a : str = (4, 4)
print(F'''path from {start.position} to {goal.position}''')
a : List[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a : Any = 1
print(world.w)
| 338 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a : List[Any] = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = BlenderbotSmallTokenizer
def __init__( self , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , A=True , **A , ) -> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=A , merges=A , add_prefix_space=A , trim_offsets=A , ) , bos_token=A , eos_token=A , unk_token=A , **A , )
UpperCAmelCase : Optional[Any] = add_prefix_space
def _lowercase( self , A , A=None ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 338 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=1_0))
| 350 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class UpperCamelCase_ ( A__ , unittest.TestCase ):
lowercase = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def _lowercase( self , A=0 ) -> int:
UpperCAmelCase : Tuple = np.random.RandomState(__A )
UpperCAmelCase : List[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : Any = self.get_dummy_inputs()
UpperCAmelCase : List[Any] = pipe(**__A ).images
UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : List[Any] = np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
UpperCAmelCase : Union[str, Any] = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__A )
pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : List[str] = self.get_dummy_inputs()
UpperCAmelCase : Optional[int] = pipe(**__A ).images
UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : List[Any] = np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowercase( self ) -> Any:
UpperCAmelCase : Tuple = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
UpperCAmelCase : str = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : Optional[Any] = self.get_dummy_inputs()
UpperCAmelCase : Union[str, Any] = pipe(**__A ).images
UpperCAmelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : Any = np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : int = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
UpperCAmelCase : Optional[Any] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : List[str] = self.get_dummy_inputs()
UpperCAmelCase : List[Any] = pipe(**__A ).images
UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : List[str] = np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowercase( self ) -> Any:
UpperCAmelCase : str = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
UpperCAmelCase : int = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : Dict = self.get_dummy_inputs()
UpperCAmelCase : Optional[int] = pipe(**__A ).images
UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : int = np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowercase( self ) -> int:
UpperCAmelCase : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
UpperCAmelCase : Union[str, Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : List[Any] = self.get_dummy_inputs()
UpperCAmelCase : Union[str, Any] = pipe(**__A ).images
UpperCAmelCase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : Optional[Any] = np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowercase( self ) -> str:
UpperCAmelCase : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : str = self.get_dummy_inputs()
UpperCAmelCase : Tuple = 3 * [inputs["""prompt"""]]
# forward
UpperCAmelCase : Tuple = pipe(**__A )
UpperCAmelCase : Any = output.images[0, -3:, -3:, -1]
UpperCAmelCase : List[Any] = self.get_dummy_inputs()
UpperCAmelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCAmelCase : Optional[int] = pipe.tokenizer(
__A , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=__A , return_tensors="""np""" , )
UpperCAmelCase : Dict = text_inputs["""input_ids"""]
UpperCAmelCase : str = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
UpperCAmelCase : Optional[int] = prompt_embeds
# forward
UpperCAmelCase : Union[str, Any] = pipe(**__A )
UpperCAmelCase : List[str] = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def _lowercase( self ) -> str:
UpperCAmelCase : Any = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : List[Any] = self.get_dummy_inputs()
UpperCAmelCase : Union[str, Any] = 3 * ["""this is a negative prompt"""]
UpperCAmelCase : List[Any] = negative_prompt
UpperCAmelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCAmelCase : str = pipe(**__A )
UpperCAmelCase : Any = output.images[0, -3:, -3:, -1]
UpperCAmelCase : List[Any] = self.get_dummy_inputs()
UpperCAmelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCAmelCase : Optional[Any] = []
for p in [prompt, negative_prompt]:
UpperCAmelCase : Union[str, Any] = pipe.tokenizer(
__A , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=__A , return_tensors="""np""" , )
UpperCAmelCase : List[Any] = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
UpperCAmelCase : List[str] = embeds
# forward
UpperCAmelCase : Union[str, Any] = pipe(**__A )
UpperCAmelCase : Union[str, Any] = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCamelCase_ ( unittest.TestCase ):
@property
def _lowercase( self ) -> List[Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : List[Any] = ort.SessionOptions()
UpperCAmelCase : Optional[Any] = False
return options
def _lowercase( self ) -> Dict:
# using the PNDM scheduler by default
UpperCAmelCase : Dict = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=__A , feature_extractor=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : Dict = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
UpperCAmelCase : Any = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="""np""" )
UpperCAmelCase : List[Any] = output.images
UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase : List[str] = np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowercase( self ) -> Tuple:
UpperCAmelCase : int = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
UpperCAmelCase : List[Any] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=__A , safety_checker=__A , feature_extractor=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : Any = """open neural network exchange"""
UpperCAmelCase : str = np.random.RandomState(0 )
UpperCAmelCase : int = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=__A , output_type="""np""" )
UpperCAmelCase : Optional[int] = output.images
UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase : Tuple = np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowercase( self ) -> str:
UpperCAmelCase : List[Any] = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
UpperCAmelCase : int = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=__A , safety_checker=__A , feature_extractor=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : Dict = """open neural network exchange"""
UpperCAmelCase : str = np.random.RandomState(0 )
UpperCAmelCase : str = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=__A , output_type="""np""" )
UpperCAmelCase : Dict = output.images
UpperCAmelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase : Optional[int] = np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowercase( self ) -> Tuple:
UpperCAmelCase : List[Any] = 0
def test_callback_fn(A , A , A ) -> None:
UpperCAmelCase : Dict = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
UpperCAmelCase : Union[str, Any] = latents[0, -3:, -3:, -1]
UpperCAmelCase : List[str] = np.array(
[-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
UpperCAmelCase : Union[str, Any] = latents[0, -3:, -3:, -1]
UpperCAmelCase : Dict = np.array(
[-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
UpperCAmelCase : List[str] = False
UpperCAmelCase : List[str] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=__A , feature_extractor=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
UpperCAmelCase : Optional[Any] = """Andromeda galaxy in a bottle"""
UpperCAmelCase : Dict = np.random.RandomState(0 )
pipe(
prompt=__A , num_inference_steps=5 , guidance_scale=7.5 , generator=__A , callback=__A , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _lowercase( self ) -> List[str]:
UpperCAmelCase : List[str] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=__A , feature_extractor=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(__A , __A )
assert pipe.safety_checker is None
UpperCAmelCase : str = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__A )
UpperCAmelCase : int = OnnxStableDiffusionPipeline.from_pretrained(__A )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
UpperCAmelCase : Optional[int] = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
| 351 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Any = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Dict = [
"""VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMSNModel""",
"""ViTMSNForImageClassification""",
"""ViTMSNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 352 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase : str = [[0 for i in range(_lowercase )] for j in range(_lowercase )]
# since string of zero length match pattern of zero length
UpperCAmelCase : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowercase ):
UpperCAmelCase : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowercase ):
UpperCAmelCase : Optional[Any] = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _lowercase ):
for j in range(1 , _lowercase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase : Union[str, Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase : List[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase : Optional[int] = dp[i - 1][j]
else:
UpperCAmelCase : Any = 0
else:
UpperCAmelCase : str = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a : List[str] = """aab"""
a : Optional[int] = """c*a*b"""
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = 1_0_0_0_0_0_0 ) -> int:
UpperCAmelCase : Any = set(range(3 , __lowerCAmelCase , 2 ) )
primes.add(2 )
for p in range(3 , __lowerCAmelCase , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , __lowerCAmelCase , __lowerCAmelCase ) ) )
UpperCAmelCase : str = [float(__lowerCAmelCase ) for n in range(limit + 1 )]
for p in primes:
for n in range(__lowerCAmelCase , limit + 1 , __lowerCAmelCase ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 353 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int:
UpperCAmelCase : int = 1
UpperCAmelCase : str = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase : Tuple = pre_numerator
UpperCAmelCase : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1
UpperCAmelCase : Union[str, Any] = cur_numerator
UpperCAmelCase : Optional[int] = e_cont * pre_numerator + temp
return sum_digits(_lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCamelCase_ :
def __init__( self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.0_2 , A=3 , A=4 , A=None , ) -> Dict:
UpperCAmelCase : Union[str, Any] = parent
UpperCAmelCase : Union[str, Any] = batch_size
UpperCAmelCase : Union[str, Any] = seq_length
UpperCAmelCase : Any = is_training
UpperCAmelCase : Optional[int] = use_input_mask
UpperCAmelCase : str = use_token_type_ids
UpperCAmelCase : Any = use_labels
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Any = hidden_size
UpperCAmelCase : Optional[Any] = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : Dict = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : Tuple = attention_probs_dropout_prob
UpperCAmelCase : Optional[Any] = max_position_embeddings
UpperCAmelCase : Dict = type_vocab_size
UpperCAmelCase : List[Any] = type_sequence_label_size
UpperCAmelCase : int = initializer_range
UpperCAmelCase : Optional[int] = num_labels
UpperCAmelCase : Tuple = num_choices
UpperCAmelCase : Optional[Any] = scope
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : List[str] = None
if self.use_input_mask:
UpperCAmelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : List[str] = None
if self.use_token_type_ids:
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase : Tuple = None
UpperCAmelCase : List[str] = None
UpperCAmelCase : int = None
if self.use_labels:
UpperCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase : Any = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase : Dict = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase( self ) -> List[str]:
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , )
def _lowercase( self , A , A , A , A , A , A , A ) -> str:
UpperCAmelCase : Union[str, Any] = NystromformerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : List[Any] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase )
UpperCAmelCase : Tuple = model(_lowerCamelCase , token_type_ids=_lowerCamelCase )
UpperCAmelCase : Dict = model(_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase( self , A , A , A , A , A , A , A ) -> Any:
UpperCAmelCase : Optional[int] = NystromformerForMaskedLM(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : Tuple = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase( self , A , A , A , A , A , A , A ) -> Dict:
UpperCAmelCase : Optional[Any] = NystromformerForQuestionAnswering(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : List[str] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , start_positions=_lowerCamelCase , end_positions=_lowerCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowercase( self , A , A , A , A , A , A , A ) -> List[Any]:
UpperCAmelCase : List[Any] = self.num_labels
UpperCAmelCase : Tuple = NystromformerForSequenceClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : int = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase( self , A , A , A , A , A , A , A ) -> Tuple:
UpperCAmelCase : Union[str, Any] = self.num_labels
UpperCAmelCase : int = NystromformerForTokenClassification(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase( self , A , A , A , A , A , A , A ) -> List[str]:
UpperCAmelCase : Dict = self.num_choices
UpperCAmelCase : Optional[int] = NystromformerForMultipleChoice(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase : List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase : List[Any] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
UpperCAmelCase
) : Tuple = config_and_inputs
UpperCAmelCase : Optional[int] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( a__ , a__ , unittest.TestCase ):
lowercase = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase = (
{
'feature-extraction': NystromformerModel,
'fill-mask': NystromformerForMaskedLM,
'question-answering': NystromformerForQuestionAnswering,
'text-classification': NystromformerForSequenceClassification,
'token-classification': NystromformerForTokenClassification,
'zero-shot': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : List[Any] = NystromformerModelTester(self )
UpperCAmelCase : int = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=37 )
def _lowercase( self ) -> int:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase : Any = type
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _lowercase( self ) -> int:
UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase )
def _lowercase( self ) -> int:
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase )
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase )
@slow
def _lowercase( self ) -> List[str]:
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] = NystromformerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
@slow
def _lowercase( self ) -> List[str]:
UpperCAmelCase : List[Any] = NystromformerModel.from_pretrained("""uw-madison/nystromformer-512""" )
UpperCAmelCase : Optional[int] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
UpperCAmelCase : str = model(_lowerCamelCase )[0]
UpperCAmelCase : int = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , _lowerCamelCase )
UpperCAmelCase : Union[str, Any] = torch.tensor(
[[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1e-4 ) )
@slow
def _lowercase( self ) -> str:
UpperCAmelCase : List[Any] = '''the [MASK] of Belgium is Brussels'''
UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512""" )
UpperCAmelCase : Dict = NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512""" )
UpperCAmelCase : Optional[int] = tokenizer(_lowerCamelCase , return_tensors="""pt""" )
with torch.no_grad():
UpperCAmelCase : Optional[int] = model(encoding.input_ids ).logits
UpperCAmelCase : Optional[int] = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(_lowerCamelCase ) , """capital""" )
| 354 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=0.0_1 , A=1000 ) -> List[str]:
UpperCAmelCase : List[Any] = p_stop
UpperCAmelCase : Optional[int] = max_length
def __iter__( self ) -> Union[str, Any]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase : Any = random.random() < self.p_stop
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A , A , A=False , A=True ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
UpperCAmelCase : List[str] = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def _lowercase( self ) -> Union[str, Any]:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : Tuple = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(A , A )
def _lowercase( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def _lowercase( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : str = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def _lowercase( self ) -> List[Any]:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase : List[str] = [BatchSamplerShard(A , 2 , A , even_batches=A ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def _lowercase( self , A , A , A , A=False , A=2 , A=False ) -> Tuple:
random.seed(A )
UpperCAmelCase : Dict = list(A )
UpperCAmelCase : Any = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
UpperCAmelCase : Dict = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(A )
iterable_dataset_lists.append(list(A ) )
UpperCAmelCase : Optional[Any] = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
UpperCAmelCase : List[Any] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
UpperCAmelCase : List[Any] = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = 42
UpperCAmelCase : List[Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
UpperCAmelCase : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase : Optional[Any] = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _lowercase( self ) -> Dict:
Accelerator()
UpperCAmelCase : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 338 | 0 |
from typing import Any
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , ) -> Optional[int]:
_validation(
__a , __a , __a , __a , __a , )
# Creates data structures and fill initial step
UpperCAmelCase : dict = {}
UpperCAmelCase : dict = {}
for state in states_space:
UpperCAmelCase : List[str] = observations_space[0]
UpperCAmelCase : Union[str, Any] = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
UpperCAmelCase : Union[str, Any] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(__a ) ):
UpperCAmelCase : List[Any] = observations_space[o]
UpperCAmelCase : List[Any] = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
UpperCAmelCase : Tuple = ''
UpperCAmelCase : Tuple = -1
for k_state in states_space:
UpperCAmelCase : Dict = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
UpperCAmelCase : Any = probability
UpperCAmelCase : Optional[int] = k_state
# Update probabilities and pointers dicts
UpperCAmelCase : str = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
UpperCAmelCase : Any = arg_max
# The final observation
UpperCAmelCase : str = observations_space[len(__a ) - 1]
# argmax for given final observation
UpperCAmelCase : str = ''
UpperCAmelCase : Any = -1
for k_state in states_space:
UpperCAmelCase : int = probabilities[(k_state, final_observation)]
if probability > max_probability:
UpperCAmelCase : Tuple = probability
UpperCAmelCase : Optional[int] = k_state
UpperCAmelCase : str = arg_max
# Process pointers backwards
UpperCAmelCase : Union[str, Any] = last_state
UpperCAmelCase : Optional[int] = []
for o in range(len(__a ) - 1 , -1 , -1 ):
result.append(__a )
UpperCAmelCase : List[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , ) -> Dict:
_validate_not_empty(
__a , __a , __a , __a , __a , )
_validate_lists(__a , __a )
_validate_dicts(
__a , __a , __a )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , ) -> str:
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("""There\'s an empty parameter""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> Dict:
_validate_list(__a , """observations_space""" )
_validate_list(__a , """states_space""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> Optional[int]:
if not isinstance(_object , __a ):
UpperCAmelCase : Any = F'''{var_name} must be a list'''
raise ValueError(__a )
else:
for x in _object:
if not isinstance(__a , __a ):
UpperCAmelCase : Union[str, Any] = F'''{var_name} must be a list of strings'''
raise ValueError(__a )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , ) -> Tuple:
_validate_dict(__a , """initial_probabilities""" , __a )
_validate_nested_dict(__a , """transition_probabilities""" )
_validate_nested_dict(__a , """emission_probabilities""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> List[Any]:
_validate_dict(_object , __a , __a )
for x in _object.values():
_validate_dict(__a , __a , __a , __a )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase = False ) -> List[Any]:
if not isinstance(_object , __a ):
UpperCAmelCase : List[Any] = F'''{var_name} must be a dict'''
raise ValueError(__a )
if not all(isinstance(__a , __a ) for x in _object ):
UpperCAmelCase : Union[str, Any] = F'''{var_name} all keys must be strings'''
raise ValueError(__a )
if not all(isinstance(__a , __a ) for x in _object.values() ):
UpperCAmelCase : str = 'nested dictionary ' if nested else ''
UpperCAmelCase : Union[str, Any] = F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
raise ValueError(__a )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a : Tuple = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Tuple = ["""VisionEncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[int] = ["""TFVisionEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = ["""FlaxVisionEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 356 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Dict = 10
def _lowercase( self ) -> List[str]:
UpperCAmelCase : List[Any] = [1, 2, 3, 4]
UpperCAmelCase : Union[str, Any] = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(_snake_case , self.block_size , 0 ) , _snake_case )
def _lowercase( self ) -> int:
UpperCAmelCase : List[Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
UpperCAmelCase : int = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_snake_case , self.block_size , 0 ) , _snake_case )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Union[str, Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
UpperCAmelCase : Tuple = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_snake_case , self.block_size , 0 ) , _snake_case )
def _lowercase( self ) -> int:
UpperCAmelCase : Union[str, Any] = """It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this."""
UpperCAmelCase , UpperCAmelCase : Tuple = process_story(_snake_case )
self.assertEqual(_snake_case , [] )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : int = """"""
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = process_story(_snake_case )
self.assertEqual(_snake_case , [] )
self.assertEqual(_snake_case , [] )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = (
"""It was the year of Our Lord one thousand seven hundred and """
"""seventy-five\n\nSpiritual revelations were conceded to England """
"""at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
)
UpperCAmelCase , UpperCAmelCase : Optional[Any] = process_story(_snake_case )
UpperCAmelCase : Any = [
"""It was the year of Our Lord one thousand seven hundred and seventy-five.""",
"""Spiritual revelations were conceded to England at that favoured period, as at this.""",
]
self.assertEqual(_snake_case , _snake_case )
UpperCAmelCase : int = ["""It was the best of times."""]
self.assertEqual(_snake_case , _snake_case )
def _lowercase( self ) -> int:
UpperCAmelCase : str = torch.tensor([1, 2, 3, 4] )
UpperCAmelCase : int = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(_snake_case , 0 ).numpy() , expected.numpy() )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Optional[int] = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
UpperCAmelCase : Dict = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_snake_case , 23 ).numpy() , expected.numpy() )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Optional[Any] = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
UpperCAmelCase : Optional[int] = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_snake_case , 1 ).numpy() , expected.numpy() )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : int = 101
UpperCAmelCase : Tuple = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
UpperCAmelCase : Union[str, Any] = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
UpperCAmelCase : Union[str, Any] = compute_token_type_ids(_snake_case , _snake_case )
np.testing.assert_array_equal(_snake_case , _snake_case )
| 357 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
for i in range(_lowercase , _lowercase ):
if array[i] == target:
return i
return -1
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
UpperCAmelCase : Tuple = 0
UpperCAmelCase : List[str] = len(_lowercase )
while left <= right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = (left + right) // 3 + 1
UpperCAmelCase : Union[str, Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCAmelCase : Any = one_third - 1
elif array[two_third] < target:
UpperCAmelCase : Tuple = two_third + 1
else:
UpperCAmelCase : int = one_third + 1
UpperCAmelCase : List[Any] = two_third - 1
else:
return -1
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = (left + right) // 3 + 1
UpperCAmelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowercase , one_third - 1 , _lowercase , _lowercase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowercase , _lowercase , _lowercase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowercase , _lowercase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Any = input("""Enter numbers separated by comma:\n""").strip()
a : Any = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
a : Tuple = int(input("""Enter the number to be found in the list:\n""").strip())
a : Union[str, Any] = ite_ternary_search(collection, target)
a : Optional[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def __lowerCamelCase ( ) -> List[Any]:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
if __name__ == "__main__":
test_and_gate()
print(and_gate(1, 0))
print(and_gate(0, 0))
print(and_gate(0, 1))
print(and_gate(1, 1))
| 358 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Optional[int] = 0
def __eq__( self , A ) -> Optional[Any]:
return self.position == cell.position
def _lowercase( self ) -> Tuple:
print(self.position )
class UpperCamelCase_ :
def __init__( self , A=(5, 5) ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = np.zeros(A )
UpperCAmelCase : int = world_size[0]
UpperCAmelCase : List[str] = world_size[1]
def _lowercase( self ) -> List[Any]:
print(self.w )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : Optional[Any] = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCAmelCase : List[Any] = cell.position[0]
UpperCAmelCase : Union[str, Any] = cell.position[1]
UpperCAmelCase : Optional[int] = []
for n in neughbour_cord:
UpperCAmelCase : Any = current_x + n[0]
UpperCAmelCase : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCAmelCase : str = Cell()
UpperCAmelCase : List[str] = (x, y)
UpperCAmelCase : Dict = cell
neighbours.append(A )
return neighbours
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
_open.append(_lowercase )
while _open:
UpperCAmelCase : Any = np.argmin([n.f for n in _open] )
UpperCAmelCase : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowercase ) )
if current == goal:
break
for n in world.get_neigbours(_lowercase ):
for c in _closed:
if c == n:
continue
UpperCAmelCase : List[str] = current.g + 1
UpperCAmelCase , UpperCAmelCase : List[str] = n.position
UpperCAmelCase , UpperCAmelCase : Dict = goal.position
UpperCAmelCase : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCAmelCase : Dict = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowercase )
UpperCAmelCase : Dict = []
while current.parent is not None:
path.append(current.position )
UpperCAmelCase : Optional[int] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a : List[str] = Gridworld()
# Start position and goal
a : Optional[int] = Cell()
a : Optional[Any] = (0, 0)
a : Optional[Any] = Cell()
a : str = (4, 4)
print(F'''path from {start.position} to {goal.position}''')
a : List[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a : Any = 1
print(world.w)
| 338 | 0 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
a : str = """sshleifer/mar_enro_6_3_student"""
class UpperCamelCase_ ( __A ):
def _lowercase( self ) -> str:
super().setUp()
UpperCAmelCase : Union[str, Any] = cached_path(
"""https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz""" , extract_compressed_file=__lowercase , )
UpperCAmelCase : Optional[int] = f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def _lowercase( self ) -> Union[str, Any]:
MarianMTModel.from_pretrained(__lowercase )
@slow
@require_torch_gpu
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = {
"""$MAX_LEN""": 64,
"""$BS""": 64,
"""$GAS""": 1,
"""$ENRO_DIR""": self.data_dir,
"""facebook/mbart-large-cc25""": MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
"""--learning_rate=3e-5""": """--learning_rate 3e-4""",
"""--num_train_epochs 6""": """--num_train_epochs 1""",
}
# Clean up bash script
UpperCAmelCase : Tuple = (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py""" )[1].strip()
UpperCAmelCase : Any = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
for k, v in env_vars_to_replace.items():
UpperCAmelCase : List[str] = bash_script.replace(__lowercase , str(__lowercase ) )
UpperCAmelCase : List[str] = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
UpperCAmelCase : List[str] = f'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
UpperCAmelCase : Union[str, Any] = ["""finetune.py"""] + bash_script.split() + args
with patch.object(__lowercase , """argv""" , __lowercase ):
UpperCAmelCase : Tuple = argparse.ArgumentParser()
UpperCAmelCase : Optional[Any] = pl.Trainer.add_argparse_args(__lowercase )
UpperCAmelCase : str = SummarizationModule.add_model_specific_args(__lowercase , os.getcwd() )
UpperCAmelCase : Dict = parser.parse_args()
UpperCAmelCase : List[Any] = main(__lowercase )
# Check metrics
UpperCAmelCase : List[Any] = load_json(model.metrics_save_path )
UpperCAmelCase : Optional[int] = metrics["""val"""][0]
UpperCAmelCase : int = metrics["""val"""][-1]
self.assertEqual(len(metrics["""val"""] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}'''] , __lowercase )
self.assertGreater(last_step_stats["""val_avg_gen_time"""] , 0.0_1 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats["""val_avg_gen_time"""] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats["""val_avg_bleu"""] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
UpperCAmelCase : Any = os.listdir(__lowercase )
UpperCAmelCase : Dict = [x for x in contents if x.endswith(""".ckpt""" )][0]
UpperCAmelCase : Dict = os.path.join(args.output_dir , __lowercase )
UpperCAmelCase : Optional[int] = torch.load(__lowercase , map_location="""cpu""" )
UpperCAmelCase : Any = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
UpperCAmelCase : str = {os.path.basename(__lowercase ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
class UpperCamelCase_ ( __A ):
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def _lowercase( self ) -> Dict:
UpperCAmelCase : Union[str, Any] = f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
UpperCAmelCase : Tuple = {
"""--fp16_opt_level=O1""": """""",
"""$MAX_LEN""": 128,
"""$BS""": 16,
"""$GAS""": 1,
"""$ENRO_DIR""": data_dir,
"""$m""": """sshleifer/student_marian_en_ro_6_1""",
"""val_check_interval=0.25""": """val_check_interval=1.0""",
}
# Clean up bash script
UpperCAmelCase : Optional[Any] = (
(self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py""" )[1].strip()
)
UpperCAmelCase : Dict = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
UpperCAmelCase : Optional[int] = bash_script.replace("""--fp16 """ , """ """ )
for k, v in env_vars_to_replace.items():
UpperCAmelCase : Optional[int] = bash_script.replace(__lowercase , str(__lowercase ) )
UpperCAmelCase : int = self.get_auto_remove_tmp_dir()
UpperCAmelCase : List[str] = bash_script.replace("""--fp16""" , """""" )
UpperCAmelCase : List[Any] = 6
UpperCAmelCase : List[str] = (
["""distillation.py"""]
+ bash_script.split()
+ [
f'''--output_dir={output_dir}''',
"""--gpus=1""",
"""--learning_rate=1e-3""",
f'''--num_train_epochs={epochs}''',
"""--warmup_steps=10""",
"""--val_check_interval=1.0""",
"""--do_predict""",
]
)
with patch.object(__lowercase , """argv""" , __lowercase ):
UpperCAmelCase : Tuple = argparse.ArgumentParser()
UpperCAmelCase : Union[str, Any] = pl.Trainer.add_argparse_args(__lowercase )
UpperCAmelCase : List[str] = SummarizationDistiller.add_model_specific_args(__lowercase , os.getcwd() )
UpperCAmelCase : str = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
UpperCAmelCase : int = distill_main(__lowercase )
# Check metrics
UpperCAmelCase : Union[str, Any] = load_json(model.metrics_save_path )
UpperCAmelCase : List[Any] = metrics["""val"""][0]
UpperCAmelCase : Dict = metrics["""val"""][-1]
assert len(metrics["""val"""] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.0_1
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}'''] , __lowercase )
# check lightning ckpt can be loaded and has a reasonable statedict
UpperCAmelCase : Optional[int] = os.listdir(__lowercase )
UpperCAmelCase : Optional[Any] = [x for x in contents if x.endswith(""".ckpt""" )][0]
UpperCAmelCase : List[str] = os.path.join(args.output_dir , __lowercase )
UpperCAmelCase : str = torch.load(__lowercase , map_location="""cpu""" )
UpperCAmelCase : str = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
UpperCAmelCase : Dict = {os.path.basename(__lowercase ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
| 359 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCamelCase_ :
def _lowercase( self , A ) -> Dict:
raise NotImplementedError()
def _lowercase( self ) -> Union[str, Any]:
raise NotImplementedError()
class UpperCamelCase_ ( A_ ):
def __init__( self , A , A = False , **A ) -> str:
UpperCAmelCase : str = tokenizer
UpperCAmelCase : List[Any] = skip_prompt
UpperCAmelCase : List[str] = decode_kwargs
# variables used in the streaming process
UpperCAmelCase : Tuple = []
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Optional[int] = True
def _lowercase( self , A ) -> Optional[int]:
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError("""TextStreamer only supports batch size 1""" )
elif len(value.shape ) > 1:
UpperCAmelCase : Dict = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
UpperCAmelCase : str = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
UpperCAmelCase : Union[str, Any] = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith("""\n""" ):
UpperCAmelCase : Dict = text[self.print_len :]
UpperCAmelCase : Dict = []
UpperCAmelCase : Optional[Any] = 0
# If the last token is a CJK character, we print the characters.
elif len(snake_case__ ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
UpperCAmelCase : str = text[self.print_len :]
self.print_len += len(snake_case__ )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
UpperCAmelCase : int = text[self.print_len : text.rfind(""" """ ) + 1]
self.print_len += len(snake_case__ )
self.on_finalized_text(snake_case__ )
def _lowercase( self ) -> List[Any]:
if len(self.token_cache ) > 0:
UpperCAmelCase : Dict = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
UpperCAmelCase : int = text[self.print_len :]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Dict = 0
else:
UpperCAmelCase : Tuple = ""
UpperCAmelCase : List[str] = True
self.on_finalized_text(snake_case__ , stream_end=snake_case__ )
def _lowercase( self , A , A = False ) -> Tuple:
print(snake_case__ , flush=snake_case__ , end="""""" if not stream_end else None )
def _lowercase( self , A ) -> Optional[int]:
if (
(cp >= 0X4e00 and cp <= 0X9fff)
or (cp >= 0X3400 and cp <= 0X4dbf) #
or (cp >= 0X20000 and cp <= 0X2a6df) #
or (cp >= 0X2a700 and cp <= 0X2b73f) #
or (cp >= 0X2b740 and cp <= 0X2b81f) #
or (cp >= 0X2b820 and cp <= 0X2ceaf) #
or (cp >= 0Xf900 and cp <= 0Xfaff)
or (cp >= 0X2f800 and cp <= 0X2fa1f) #
): #
return True
return False
class UpperCamelCase_ ( A_ ):
def __init__( self , A , A = False , A = None , **A ) -> List[Any]:
super().__init__(snake_case__ , snake_case__ , **snake_case__ )
UpperCAmelCase : Tuple = Queue()
UpperCAmelCase : Dict = None
UpperCAmelCase : Dict = timeout
def _lowercase( self , A , A = False ) -> List[str]:
self.text_queue.put(snake_case__ , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self ) -> Tuple:
return self
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338 | 0 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a : Any = TypeVar("""T""")
class UpperCamelCase_ ( Generic[T] ):
lowercase = 42 # Cache store of keys
lowercase = 42 # References of the keys in cache
lowercase = 10 # Maximum capacity of cache
def __init__( self , A ) -> Optional[int]:
UpperCAmelCase : int = deque()
UpperCAmelCase : Optional[int] = set()
if not n:
UpperCAmelCase : List[Any] = sys.maxsize
elif n < 0:
raise ValueError("""n should be an integer greater than 0.""" )
else:
UpperCAmelCase : Optional[Any] = n
def _lowercase( self , A ) -> Tuple:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
UpperCAmelCase : Any = self.dq_store.pop()
self.key_reference.remove(_a )
else:
self.dq_store.remove(_a )
self.dq_store.appendleft(_a )
self.key_reference.add(_a )
def _lowercase( self ) -> Optional[int]:
for k in self.dq_store:
print(_a )
def __repr__( self ) -> Union[str, Any]:
return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}'''
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Optional[Any] = LRUCache(4)
lru_cache.refer("""A""")
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer("""A""")
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 361 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 0 |
'''simple docstring'''
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'philschmid/bart-large-cnn-samsum'
lowercase = (
'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
'and returns a summary of the text.'
)
lowercase = 'summarizer'
lowercase = AutoTokenizer
lowercase = AutoModelForSeqaSeqLM
lowercase = ['text']
lowercase = ['text']
def _lowercase( self , A ) -> List[str]:
return self.pre_processor(A , return_tensors="""pt""" , truncation=A )
def _lowercase( self , A ) -> Union[str, Any]:
return self.model.generate(**A )[0]
def _lowercase( self , A ) -> List[str]:
return self.pre_processor.decode(A , skip_special_tokens=A , clean_up_tokenization_spaces=A )
| 362 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = split_dict._to_yaml_list()
assert len(_lowercase ) == len(_lowercase )
UpperCAmelCase : List[Any] = SplitDict._from_yaml_list(_lowercase )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCAmelCase : List[str] = None
# the split name of split_dict takes over the name of the split info object
UpperCAmelCase : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=_lowercase ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCAmelCase : Optional[Any] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 0 |
import warnings
from functools import wraps
from typing import Callable
def __lowerCamelCase ( _lowercase ) -> Callable:
@wraps(_lowerCamelCase )
def _inner_fn(*_lowercase , **_lowercase ):
warnings.warn(
(F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''') , _lowerCamelCase , )
return fn(*_lowerCamelCase , **_lowerCamelCase )
return _inner_fn
| 363 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 | 0 |
'''simple docstring'''
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class UpperCamelCase_ ( unittest.TestCase ):
def __init__( self , A , A=2 , A=56 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=2 , A=2 , A=7 , A="gelu_new" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.0_2 , A=4 , A="block_sparse" , A=True , A=False , A=2 , A=3 , ) -> str:
UpperCAmelCase : Dict = parent
UpperCAmelCase : Optional[Any] = batch_size
UpperCAmelCase : Union[str, Any] = seq_length
UpperCAmelCase : Union[str, Any] = is_training
UpperCAmelCase : str = use_attention_mask
UpperCAmelCase : Optional[Any] = use_token_type_ids
UpperCAmelCase : str = use_labels
UpperCAmelCase : Tuple = vocab_size
UpperCAmelCase : List[str] = hidden_size
UpperCAmelCase : int = num_hidden_layers
UpperCAmelCase : List[str] = num_attention_heads
UpperCAmelCase : Dict = intermediate_size
UpperCAmelCase : int = hidden_act
UpperCAmelCase : Optional[Any] = hidden_dropout_prob
UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase : Any = max_position_embeddings
UpperCAmelCase : Tuple = type_vocab_size
UpperCAmelCase : Optional[Any] = type_sequence_label_size
UpperCAmelCase : Any = initializer_range
UpperCAmelCase : Tuple = num_choices
UpperCAmelCase : List[str] = rescale_embeddings
UpperCAmelCase : int = attention_type
UpperCAmelCase : Union[str, Any] = use_bias
UpperCAmelCase : Dict = block_size
UpperCAmelCase : Optional[Any] = num_random_blocks
def _lowercase( self ) -> Any:
UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : List[Any] = None
if self.use_attention_mask:
UpperCAmelCase : int = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Dict = None
if self.use_token_type_ids:
UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase : Union[str, Any] = BigBirdConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase_ , initializer_range=self.initializer_range , attention_type=self.attention_type , block_size=self.block_size , num_random_blocks=self.num_random_blocks , use_bias=self.use_bias , rescale_embeddings=self.rescale_embeddings , )
return config, input_ids, token_type_ids, attention_mask
def _lowercase( self ) -> List[str]:
UpperCAmelCase : int = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Tuple = config_and_inputs
UpperCAmelCase : List[str] = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""attention_mask""": attention_mask,
}
return config, inputs_dict
@require_flax
class UpperCamelCase_ ( a_ , unittest.TestCase ):
lowercase = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
lowercase = False
lowercase = False
def _lowercase( self ) -> Tuple:
UpperCAmelCase : str = FlaxBigBirdModelTester(self )
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def _lowercase( self ) -> Any:
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def _lowercase( self ) -> Dict:
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def _lowercase( self ) -> Tuple:
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def _lowercase( self ) -> Optional[Any]:
super().test_hidden_states_output()
@slow
def _lowercase( self ) -> Union[str, Any]:
for model_class_name in self.all_model_classes:
UpperCAmelCase : Optional[Any] = model_class_name.from_pretrained("""google/bigbird-roberta-base""" )
self.assertIsNotNone(lowercase_ )
def _lowercase( self ) -> Union[str, Any]:
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def _lowercase( self ) -> Tuple:
UpperCAmelCase , UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase : Optional[Any] = self._prepare_for_class(lowercase_ , lowercase_ )
UpperCAmelCase : Dict = model_class(lowercase_ )
@jax.jit
def model_jitted(A , A=None , **A ):
return model(input_ids=lowercase_ , attention_mask=lowercase_ , **lowercase_ )
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase : Optional[int] = model_jitted(**lowercase_ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase : str = model_jitted(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
for jitted_output, output in zip(lowercase_ , lowercase_ ):
self.assertEqual(jitted_output.shape , output.shape )
def _lowercase( self , A , A , A , A=1e-5 , A="outputs" , A=None ) -> Optional[int]:
if name.startswith("""outputs.attentions""" ):
return
else:
super().check_pt_flax_outputs(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
| 364 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
a = logging.get_logger(__name__)
# General docstring
a = """RegNetConfig"""
# Base docstring
a = """facebook/regnet-y-040"""
a = [1, 1_0_8_8, 7, 7]
# Image classification docstring
a = """facebook/regnet-y-040"""
a = """tabby, tabby cat"""
a = [
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A = 3 , A = 1 , A = 1 , A = "relu" , **A , ) -> str:
super().__init__(**_a )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
UpperCAmelCase : Optional[Any] = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
UpperCAmelCase : Tuple = tf.keras.layers.ConvaD(
filters=_a , kernel_size=_a , strides=_a , padding="""VALID""" , groups=_a , use_bias=_a , name="""convolution""" , )
UpperCAmelCase : Optional[Any] = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="""normalization""" )
UpperCAmelCase : List[str] = ACTaFN[activation] if activation is not None else tf.identity
def _lowercase( self , A ) -> List[Any]:
UpperCAmelCase : List[Any] = self.convolution(self.padding(_a ) )
UpperCAmelCase : Tuple = self.normalization(_a )
UpperCAmelCase : Union[str, Any] = self.activation(_a )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , **A ) -> Tuple:
super().__init__(**_a )
UpperCAmelCase : List[str] = config.num_channels
UpperCAmelCase : Dict = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name="""embedder""" , )
def _lowercase( self , A ) -> Union[str, Any]:
UpperCAmelCase : List[str] = shape_list(_a )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
UpperCAmelCase : Dict = tf.transpose(_a , perm=(0, 2, 3, 1) )
UpperCAmelCase : List[str] = self.embedder(_a )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A = 2 , **A ) -> int:
super().__init__(**_a )
UpperCAmelCase : Optional[Any] = tf.keras.layers.ConvaD(
filters=_a , kernel_size=1 , strides=_a , use_bias=_a , name="""convolution""" )
UpperCAmelCase : str = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="""normalization""" )
def _lowercase( self , A , A = False ) -> tf.Tensor:
return self.normalization(self.convolution(_a ) , training=_a )
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A , **A ) -> int:
super().__init__(**_a )
UpperCAmelCase : List[Any] = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_a , name="""pooler""" )
UpperCAmelCase : Optional[int] = [
tf.keras.layers.ConvaD(filters=_a , kernel_size=1 , activation="""relu""" , name="""attention.0""" ),
tf.keras.layers.ConvaD(filters=_a , kernel_size=1 , activation="""sigmoid""" , name="""attention.2""" ),
]
def _lowercase( self , A ) -> List[Any]:
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
UpperCAmelCase : str = self.pooler(_a )
for layer_module in self.attention:
UpperCAmelCase : Dict = layer_module(_a )
UpperCAmelCase : Optional[Any] = hidden_state * pooled
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A , A , A = 1 , **A ) -> Union[str, Any]:
super().__init__(**_a )
UpperCAmelCase : Union[str, Any] = in_channels != out_channels or stride != 1
UpperCAmelCase : str = max(1 , out_channels // config.groups_width )
UpperCAmelCase : Tuple = (
TFRegNetShortCut(_a , stride=_a , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
UpperCAmelCase : Tuple = [
TFRegNetConvLayer(_a , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
_a , stride=_a , groups=_a , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetConvLayer(_a , kernel_size=1 , activation=_a , name="""layer.2""" ),
]
UpperCAmelCase : int = ACTaFN[config.hidden_act]
def _lowercase( self , A ) -> Any:
UpperCAmelCase : Tuple = hidden_state
for layer_module in self.layers:
UpperCAmelCase : List[str] = layer_module(_a )
UpperCAmelCase : Optional[int] = self.shortcut(_a )
hidden_state += residual
UpperCAmelCase : Union[str, Any] = self.activation(_a )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A , A , A = 1 , **A ) -> List[str]:
super().__init__(**_a )
UpperCAmelCase : List[str] = in_channels != out_channels or stride != 1
UpperCAmelCase : Union[str, Any] = max(1 , out_channels // config.groups_width )
UpperCAmelCase : int = (
TFRegNetShortCut(_a , stride=_a , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
UpperCAmelCase : Any = [
TFRegNetConvLayer(_a , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
_a , stride=_a , groups=_a , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetSELayer(_a , reduced_channels=int(round(in_channels / 4 ) ) , name="""layer.2""" ),
TFRegNetConvLayer(_a , kernel_size=1 , activation=_a , name="""layer.3""" ),
]
UpperCAmelCase : Union[str, Any] = ACTaFN[config.hidden_act]
def _lowercase( self , A ) -> List[str]:
UpperCAmelCase : Tuple = hidden_state
for layer_module in self.layers:
UpperCAmelCase : str = layer_module(_a )
UpperCAmelCase : int = self.shortcut(_a )
hidden_state += residual
UpperCAmelCase : List[Any] = self.activation(_a )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A , A , A = 2 , A = 2 , **A ) -> Union[str, Any]:
super().__init__(**_a )
UpperCAmelCase : Union[str, Any] = TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
UpperCAmelCase : Any = [
# downsampling is done in the first layer with stride of 2
layer(_a , _a , _a , stride=_a , name="""layers.0""" ),
*[layer(_a , _a , _a , name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def _lowercase( self , A ) -> Any:
for layer_module in self.layers:
UpperCAmelCase : Tuple = layer_module(_a )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , **A ) -> List[str]:
super().__init__(**_a )
UpperCAmelCase : int = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
_a , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name="""stages.0""" , ) )
UpperCAmelCase : Any = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(_a , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(_a , _a , _a , depth=_a , name=f'''stages.{i+1}''' ) )
def _lowercase( self , A , A = False , A = True ) -> TFBaseModelOutputWithNoAttention:
UpperCAmelCase : List[Any] = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
UpperCAmelCase : Optional[Any] = hidden_states + (hidden_state,)
UpperCAmelCase : Union[str, Any] = stage_module(_a )
if output_hidden_states:
UpperCAmelCase : Tuple = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=_a , hidden_states=_a )
@keras_serializable
class UpperCamelCase_ ( tf.keras.layers.Layer ):
lowercase = RegNetConfig
def __init__( self , A , **A ) -> Optional[int]:
super().__init__(**_a )
UpperCAmelCase : List[Any] = config
UpperCAmelCase : Union[str, Any] = TFRegNetEmbeddings(_a , name="""embedder""" )
UpperCAmelCase : List[str] = TFRegNetEncoder(_a , name="""encoder""" )
UpperCAmelCase : Optional[int] = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_a , name="""pooler""" )
@unpack_inputs
def _lowercase( self , A , A = None , A = None , A = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
UpperCAmelCase : int = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase : Union[str, Any] = self.embedder(_a , training=_a )
UpperCAmelCase : Any = self.encoder(
_a , output_hidden_states=_a , return_dict=_a , training=_a )
UpperCAmelCase : Tuple = encoder_outputs[0]
UpperCAmelCase : int = self.pooler(_a )
# Change to NCHW output format have uniformity in the modules
UpperCAmelCase : Union[str, Any] = tf.transpose(_a , perm=(0, 3, 1, 2) )
UpperCAmelCase : Optional[int] = tf.transpose(_a , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
UpperCAmelCase : int = tuple([tf.transpose(_a , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_a , pooler_output=_a , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class UpperCamelCase_ ( __lowercase ):
lowercase = RegNetConfig
lowercase = "regnet"
lowercase = "pixel_values"
@property
def _lowercase( self ) -> Tuple:
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) , dtype=tf.floataa )}
a = R"""
Parameters:
This model is a Tensorflow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
a = R"""
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConveNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , __lowercase , )
class UpperCamelCase_ ( __lowercase ):
def __init__( self , A , *A , **A ) -> Any:
super().__init__(_a , *_a , **_a )
UpperCAmelCase : Optional[Any] = TFRegNetMainLayer(_a , name="""regnet""" )
@unpack_inputs
@add_start_docstrings_to_model_forward(_a )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_a , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _lowercase( self , A , A = None , A = None , A=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
UpperCAmelCase : str = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase : Optional[int] = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase : Tuple = self.regnet(
pixel_values=_a , output_hidden_states=_a , return_dict=_a , training=_a , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , __lowercase , )
class UpperCamelCase_ ( __lowercase , __lowercase ):
def __init__( self , A , *A , **A ) -> Tuple:
super().__init__(_a , *_a , **_a )
UpperCAmelCase : str = config.num_labels
UpperCAmelCase : int = TFRegNetMainLayer(_a , name="""regnet""" )
# classification head
UpperCAmelCase : Optional[int] = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name="""classifier.1""" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(_a )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_a , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _lowercase( self , A = None , A = None , A = None , A = None , A=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
UpperCAmelCase : int = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase : List[str] = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase : List[str] = self.regnet(
_a , output_hidden_states=_a , return_dict=_a , training=_a )
UpperCAmelCase : Dict = outputs.pooler_output if return_dict else outputs[1]
UpperCAmelCase : Dict = self.classifier[0](_a )
UpperCAmelCase : List[str] = self.classifier[1](_a )
UpperCAmelCase : List[Any] = None if labels is None else self.hf_compute_loss(labels=_a , logits=_a )
if not return_dict:
UpperCAmelCase : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=_a , logits=_a , hidden_states=outputs.hidden_states )
| 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
'''simple docstring'''
class UpperCamelCase_ :
def __init__( self , A , A , A ) -> Any:
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = value
UpperCAmelCase : int = weight
def __repr__( self ) -> Dict:
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _lowercase( self ) -> Dict:
return self.value
def _lowercase( self ) -> List[Any]:
return self.name
def _lowercase( self ) -> Optional[int]:
return self.weight
def _lowercase( self ) -> str:
return self.value / self.weight
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Any:
UpperCAmelCase : Union[str, Any] = []
for i in range(len(lowerCAmelCase__ ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = sorted(lowerCAmelCase__ , key=lowerCAmelCase__ , reverse=lowerCAmelCase__ )
UpperCAmelCase : str = []
UpperCAmelCase : Tuple = 0.0, 0.0
for i in range(len(lowerCAmelCase__ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __lowerCamelCase ( ) -> Optional[Any]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 | 0 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A ) -> str:
UpperCAmelCase : Dict = 3
UpperCAmelCase : Optional[int] = 250
UpperCAmelCase : Union[str, Any] = ids_tensor((batch_size, length) , lowerCAmelCase__ )
UpperCAmelCase : List[Any] = torch.ones((batch_size, length) , device=lowerCAmelCase__ , dtype=torch.float ) / length
return input_ids, scores
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Tuple = self._get_tensors(5 )
UpperCAmelCase : Any = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
UpperCAmelCase : Tuple = self._get_tensors(9 )
self.assertFalse(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
UpperCAmelCase : Union[str, Any] = self._get_tensors(10 )
self.assertTrue(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : str = MaxLengthCriteria(max_length=10 )
UpperCAmelCase : Optional[int] = self._get_tensors(5 )
self.assertFalse(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
UpperCAmelCase : List[Any] = self._get_tensors(9 )
self.assertFalse(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
UpperCAmelCase : Optional[Any] = self._get_tensors(10 )
self.assertTrue(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Any = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
UpperCAmelCase : Tuple = self._get_tensors(5 )
self.assertFalse(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
UpperCAmelCase : Optional[Any] = self._get_tensors(9 )
self.assertFalse(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
UpperCAmelCase : int = self._get_tensors(10 )
self.assertTrue(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
UpperCAmelCase : Union[str, Any] = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 10 )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Optional[Any] = self._get_tensors(5 )
UpperCAmelCase : Tuple = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
UpperCAmelCase : Optional[int] = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(lowerCAmelCase__ , lowerCAmelCase__ ) )
def _lowercase( self ) -> Tuple:
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 10 )
with self.assertWarns(lowerCAmelCase__ ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 11 )
UpperCAmelCase : Union[str, Any] = validate_stopping_criteria(StoppingCriteriaList() , 11 )
self.assertEqual(len(lowerCAmelCase__ ) , 1 )
| 367 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : str = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'levit'
def __init__( self , A=224 , A=3 , A=3 , A=2 , A=1 , A=16 , A=[128, 256, 384] , A=[4, 8, 12] , A=[4, 4, 4] , A=[16, 16, 16] , A=0 , A=[2, 2, 2] , A=[2, 2, 2] , A=0.0_2 , **A , ) -> int:
super().__init__(**A )
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = kernel_size
UpperCAmelCase : Optional[int] = stride
UpperCAmelCase : Dict = padding
UpperCAmelCase : List[Any] = hidden_sizes
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : Any = key_dim
UpperCAmelCase : str = drop_path_rate
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = attention_ratio
UpperCAmelCase : Optional[Any] = mlp_ratio
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : int = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
| 338 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a : Optional[Any] = logging.get_logger(__name__)
a : Tuple = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
a : Optional[Any] = {
'vocab_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
},
'merges_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
},
}
a : Tuple = {'allegro/herbert-base-cased': 5_1_4}
a : List[str] = {}
class UpperCamelCase_ ( _UpperCamelCase ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_INIT_CONFIGURATION
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = HerbertTokenizer
def __init__( self , A=None , A=None , A=None , A="<s>" , A="<unk>" , A="<pad>" , A="<mask>" , A="</s>" , **A , ) -> int:
super().__init__(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Dict = [self.cls_token_id]
UpperCAmelCase : Optional[int] = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _lowercase( self , A , A = None , A = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
if token_ids_a is None:
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Tuple = [self.sep_token_id]
UpperCAmelCase : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowercase( self , A , A = None ) -> Tuple[str]:
UpperCAmelCase : Any = self._tokenizer.model.save(_SCREAMING_SNAKE_CASE , name=_SCREAMING_SNAKE_CASE )
return tuple(_SCREAMING_SNAKE_CASE )
| 368 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : List[str] = """Hello, World!"""
a : List[Any] = """en_XX"""
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Dict = Path("""data_bin""" )
UpperCAmelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowercase ).parent ) , checkpoint_file=Path(_lowercase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowercase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowercase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowercase )
UpperCAmelCase : List[str] = xmod.model.encoder.sentence_encoder
UpperCAmelCase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowercase )
UpperCAmelCase : str = XmodForSequenceClassification(_lowercase ) if classification_head else XmodForMaskedLM(_lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : int = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : List[str] = model.roberta.encoder.layer[i]
UpperCAmelCase : Optional[Any] = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
UpperCAmelCase : List[Any] = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Any = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
UpperCAmelCase : Any = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[str] = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : Tuple = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
UpperCAmelCase : List[str] = xmod_layer.fca.weight
UpperCAmelCase : str = xmod_layer.fca.bias
# output
UpperCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
UpperCAmelCase : Dict = xmod_layer.fca.weight
UpperCAmelCase : Dict = xmod_layer.fca.bias
UpperCAmelCase : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase : Union[str, Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : str = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : List[str] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : List[Any] = bert_output.adapter_modules[lang_code]
UpperCAmelCase : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any = from_adapter.fca.weight
UpperCAmelCase : int = from_adapter.fca.bias
UpperCAmelCase : Dict = from_adapter.fca.weight
UpperCAmelCase : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Tuple = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].dense.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].dense.bias
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].out_proj.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
UpperCAmelCase : Dict = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : str = xmod.model.encoder.lm_head.weight
UpperCAmelCase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : Any = xmod.encode(_lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowercase )
UpperCAmelCase : Optional[int] = model(_lowercase )[0]
if classification_head:
UpperCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase ) )
else:
UpperCAmelCase : Optional[Any] = xmod.model(_lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Dict = torch.allclose(_lowercase , _lowercase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowercase ).mkdir(parents=_lowercase , exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
a : List[str] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 338 | 0 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __lowerCamelCase ( _lowercase ) -> List[Any]:
return (data["data"], data["target"])
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = XGBRegressor(verbosity=0 , random_state=4_2 )
xgb.fit(a__ , a__ )
# Predict target for test data
UpperCAmelCase : Tuple = xgb.predict(a__ )
UpperCAmelCase : List[Any] = predictions.reshape(len(a__ ) , 1 )
return predictions
def __lowerCamelCase ( ) -> str:
UpperCAmelCase : Union[str, Any] = fetch_california_housing()
UpperCAmelCase : Union[str, Any] = data_handling(a__ )
UpperCAmelCase : List[str] = train_test_split(
a__ , a__ , test_size=0.25 , random_state=1 )
UpperCAmelCase : Dict = xgboost(a__ , a__ , a__ )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(a__ , a__ )}''' )
print(F'''Mean Square Error : {mean_squared_error(a__ , a__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 369 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
a : Union[str, Any] = logging.get_logger(__name__)
# General docstring
a : List[str] = """RegNetConfig"""
# Base docstring
a : Any = """facebook/regnet-y-040"""
a : str = [1, 1_0_8_8, 7, 7]
# Image classification docstring
a : List[str] = """facebook/regnet-y-040"""
a : Optional[Any] = """tabby, tabby cat"""
a : Any = [
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A = 3 , A = 1 , A = 1 , A = "relu" , **A , ) -> int:
super().__init__(**_lowerCAmelCase )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
UpperCAmelCase : Dict = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
UpperCAmelCase : Optional[Any] = tf.keras.layers.ConvaD(
filters=_lowerCAmelCase , kernel_size=_lowerCAmelCase , strides=_lowerCAmelCase , padding="""VALID""" , groups=_lowerCAmelCase , use_bias=_lowerCAmelCase , name="""convolution""" , )
UpperCAmelCase : str = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="""normalization""" )
UpperCAmelCase : int = ACTaFN[activation] if activation is not None else tf.identity
def _lowercase( self , A ) -> Optional[int]:
UpperCAmelCase : List[Any] = self.convolution(self.padding(_lowerCAmelCase ) )
UpperCAmelCase : List[str] = self.normalization(_lowerCAmelCase )
UpperCAmelCase : Optional[int] = self.activation(_lowerCAmelCase )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , **A ) -> str:
super().__init__(**_lowerCAmelCase )
UpperCAmelCase : Tuple = config.num_channels
UpperCAmelCase : Any = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name="""embedder""" , )
def _lowercase( self , A ) -> Any:
UpperCAmelCase : List[str] = shape_list(_lowerCAmelCase )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
UpperCAmelCase : Tuple = tf.transpose(_lowerCAmelCase , perm=(0, 2, 3, 1) )
UpperCAmelCase : Union[str, Any] = self.embedder(_lowerCAmelCase )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A = 2 , **A ) -> List[str]:
super().__init__(**_lowerCAmelCase )
UpperCAmelCase : int = tf.keras.layers.ConvaD(
filters=_lowerCAmelCase , kernel_size=1 , strides=_lowerCAmelCase , use_bias=_lowerCAmelCase , name="""convolution""" )
UpperCAmelCase : Any = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="""normalization""" )
def _lowercase( self , A , A = False ) -> Dict:
return self.normalization(self.convolution(_lowerCAmelCase ) , training=_lowerCAmelCase )
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A , **A ) -> Tuple:
super().__init__(**_lowerCAmelCase )
UpperCAmelCase : str = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowerCAmelCase , name="""pooler""" )
UpperCAmelCase : int = [
tf.keras.layers.ConvaD(filters=_lowerCAmelCase , kernel_size=1 , activation="""relu""" , name="""attention.0""" ),
tf.keras.layers.ConvaD(filters=_lowerCAmelCase , kernel_size=1 , activation="""sigmoid""" , name="""attention.2""" ),
]
def _lowercase( self , A ) -> List[str]:
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
UpperCAmelCase : Optional[int] = self.pooler(_lowerCAmelCase )
for layer_module in self.attention:
UpperCAmelCase : int = layer_module(_lowerCAmelCase )
UpperCAmelCase : List[Any] = hidden_state * pooled
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A , A , A = 1 , **A ) -> List[Any]:
super().__init__(**_lowerCAmelCase )
UpperCAmelCase : str = in_channels != out_channels or stride != 1
UpperCAmelCase : int = max(1 , out_channels // config.groups_width )
UpperCAmelCase : Dict = (
TFRegNetShortCut(_lowerCAmelCase , stride=_lowerCAmelCase , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
UpperCAmelCase : Any = [
TFRegNetConvLayer(_lowerCAmelCase , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
_lowerCAmelCase , stride=_lowerCAmelCase , groups=_lowerCAmelCase , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetConvLayer(_lowerCAmelCase , kernel_size=1 , activation=_lowerCAmelCase , name="""layer.2""" ),
]
UpperCAmelCase : Optional[int] = ACTaFN[config.hidden_act]
def _lowercase( self , A ) -> List[Any]:
UpperCAmelCase : Optional[Any] = hidden_state
for layer_module in self.layers:
UpperCAmelCase : List[str] = layer_module(_lowerCAmelCase )
UpperCAmelCase : Union[str, Any] = self.shortcut(_lowerCAmelCase )
hidden_state += residual
UpperCAmelCase : Any = self.activation(_lowerCAmelCase )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A , A , A = 1 , **A ) -> Optional[Any]:
super().__init__(**_lowerCAmelCase )
UpperCAmelCase : Optional[Any] = in_channels != out_channels or stride != 1
UpperCAmelCase : Dict = max(1 , out_channels // config.groups_width )
UpperCAmelCase : Optional[Any] = (
TFRegNetShortCut(_lowerCAmelCase , stride=_lowerCAmelCase , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
UpperCAmelCase : int = [
TFRegNetConvLayer(_lowerCAmelCase , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
_lowerCAmelCase , stride=_lowerCAmelCase , groups=_lowerCAmelCase , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetSELayer(_lowerCAmelCase , reduced_channels=int(round(in_channels / 4 ) ) , name="""layer.2""" ),
TFRegNetConvLayer(_lowerCAmelCase , kernel_size=1 , activation=_lowerCAmelCase , name="""layer.3""" ),
]
UpperCAmelCase : Tuple = ACTaFN[config.hidden_act]
def _lowercase( self , A ) -> List[str]:
UpperCAmelCase : int = hidden_state
for layer_module in self.layers:
UpperCAmelCase : List[str] = layer_module(_lowerCAmelCase )
UpperCAmelCase : Union[str, Any] = self.shortcut(_lowerCAmelCase )
hidden_state += residual
UpperCAmelCase : int = self.activation(_lowerCAmelCase )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A , A , A = 2 , A = 2 , **A ) -> List[Any]:
super().__init__(**_lowerCAmelCase )
UpperCAmelCase : Tuple = TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
UpperCAmelCase : Optional[Any] = [
# downsampling is done in the first layer with stride of 2
layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , stride=_lowerCAmelCase , name="""layers.0""" ),
*[layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def _lowercase( self , A ) -> Dict:
for layer_module in self.layers:
UpperCAmelCase : int = layer_module(_lowerCAmelCase )
return hidden_state
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , **A ) -> Optional[Any]:
super().__init__(**_lowerCAmelCase )
UpperCAmelCase : Union[str, Any] = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
_lowerCAmelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name="""stages.0""" , ) )
UpperCAmelCase : Optional[int] = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(_lowerCAmelCase , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , depth=_lowerCAmelCase , name=f'''stages.{i+1}''' ) )
def _lowercase( self , A , A = False , A = True ) -> Optional[int]:
UpperCAmelCase : Union[str, Any] = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
UpperCAmelCase : Optional[Any] = hidden_states + (hidden_state,)
UpperCAmelCase : Optional[Any] = stage_module(_lowerCAmelCase )
if output_hidden_states:
UpperCAmelCase : int = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=_lowerCAmelCase , hidden_states=_lowerCAmelCase )
@keras_serializable
class UpperCamelCase_ ( tf.keras.layers.Layer ):
lowercase = RegNetConfig
def __init__( self , A , **A ) -> Any:
super().__init__(**_lowerCAmelCase )
UpperCAmelCase : List[str] = config
UpperCAmelCase : List[str] = TFRegNetEmbeddings(_lowerCAmelCase , name="""embedder""" )
UpperCAmelCase : Any = TFRegNetEncoder(_lowerCAmelCase , name="""encoder""" )
UpperCAmelCase : Optional[int] = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowerCAmelCase , name="""pooler""" )
@unpack_inputs
def _lowercase( self , A , A = None , A = None , A = False , ) -> Optional[Any]:
UpperCAmelCase : List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase : List[str] = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase : Tuple = self.embedder(_lowerCAmelCase , training=_lowerCAmelCase )
UpperCAmelCase : str = self.encoder(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , training=_lowerCAmelCase )
UpperCAmelCase : str = encoder_outputs[0]
UpperCAmelCase : List[Any] = self.pooler(_lowerCAmelCase )
# Change to NCHW output format have uniformity in the modules
UpperCAmelCase : Dict = tf.transpose(_lowerCAmelCase , perm=(0, 3, 1, 2) )
UpperCAmelCase : int = tf.transpose(_lowerCAmelCase , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
UpperCAmelCase : Tuple = tuple([tf.transpose(_lowerCAmelCase , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_lowerCAmelCase , pooler_output=_lowerCAmelCase , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class UpperCamelCase_ ( __UpperCamelCase ):
lowercase = RegNetConfig
lowercase = "regnet"
lowercase = "pixel_values"
@property
def _lowercase( self ) -> Union[str, Any]:
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) , dtype=tf.floataa )}
a : Tuple = R"""\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"""
a : List[Any] = R"""\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"""
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , __UpperCamelCase , )
class UpperCamelCase_ ( __UpperCamelCase ):
def __init__( self , A , *A , **A ) -> str:
super().__init__(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
UpperCAmelCase : Optional[int] = TFRegNetMainLayer(_lowerCAmelCase , name="""regnet""" )
@unpack_inputs
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _lowercase( self , A , A = None , A = None , A=False , ) -> Any:
UpperCAmelCase : Optional[int] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase : List[Any] = self.regnet(
pixel_values=_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , training=_lowerCAmelCase , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , __UpperCamelCase , )
class UpperCamelCase_ ( __UpperCamelCase , __UpperCamelCase ):
def __init__( self , A , *A , **A ) -> List[str]:
super().__init__(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
UpperCAmelCase : Union[str, Any] = config.num_labels
UpperCAmelCase : Optional[Any] = TFRegNetMainLayer(_lowerCAmelCase , name="""regnet""" )
# classification head
UpperCAmelCase : List[Any] = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name="""classifier.1""" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _lowercase( self , A = None , A = None , A = None , A = None , A=False , ) -> str:
UpperCAmelCase : int = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase : int = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase : List[str] = self.regnet(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , training=_lowerCAmelCase )
UpperCAmelCase : Dict = outputs.pooler_output if return_dict else outputs[1]
UpperCAmelCase : Dict = self.classifier[0](_lowerCAmelCase )
UpperCAmelCase : Any = self.classifier[1](_lowerCAmelCase )
UpperCAmelCase : Optional[int] = None if labels is None else self.hf_compute_loss(labels=_lowerCAmelCase , logits=_lowerCAmelCase )
if not return_dict:
UpperCAmelCase : Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=_lowerCAmelCase , logits=_lowerCAmelCase , hidden_states=outputs.hidden_states )
| 370 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : int = ["MLukeTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
a : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 371 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a : List[Any] = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = BlenderbotSmallTokenizer
def __init__( self , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , A=True , **A , ) -> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=A , merges=A , add_prefix_space=A , trim_offsets=A , ) , bos_token=A , eos_token=A , unk_token=A , **A , )
UpperCAmelCase : Optional[Any] = add_prefix_space
def _lowercase( self , A , A=None ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : Dict = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = ["""YolosFeatureExtractor"""]
a : Any = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : str = [
"""YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""YolosForObjectDetection""",
"""YolosModel""",
"""YolosPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
a : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 350 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
a : int = None
a : List[str] = logging.get_logger(__name__)
a : Union[str, Any] = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
a : List[Any] = {
"""vocab_file""": {
"""facebook/mbart-large-en-ro""": (
"""https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
),
"""facebook/mbart-large-cc25""": (
"""https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
"""facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
},
}
a : Union[str, Any] = {
"""facebook/mbart-large-en-ro""": 1_0_2_4,
"""facebook/mbart-large-cc25""": 1_0_2_4,
}
# fmt: off
a : Any = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]
class UpperCamelCase_ ( lowercase__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = ["""input_ids""", """attention_mask"""]
lowercase = MBartTokenizer
lowercase = []
lowercase = []
def __init__( self , A=None , A=None , A="<s>" , A="</s>" , A="</s>" , A="<s>" , A="<unk>" , A="<pad>" , A="<mask>" , A=None , A=None , A=None , **A , ) -> int:
UpperCAmelCase : int = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else mask_token
super().__init__(
vocab_file=lowercase_ , tokenizer_file=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , sep_token=lowercase_ , cls_token=lowercase_ , unk_token=lowercase_ , pad_token=lowercase_ , mask_token=lowercase_ , src_lang=lowercase_ , tgt_lang=lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
UpperCAmelCase : str = vocab_file
UpperCAmelCase : Dict = False if not self.vocab_file else True
UpperCAmelCase : Any = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
UpperCAmelCase : List[str] = {
lang_code: self.convert_tokens_to_ids(lowercase_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
UpperCAmelCase : Optional[Any] = src_lang if src_lang is not None else "en_XX"
UpperCAmelCase : Any = self.convert_tokens_to_ids(self._src_lang )
UpperCAmelCase : Union[str, Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _lowercase( self ) -> Any:
return self._src_lang
@src_lang.setter
def _lowercase( self , A ) -> List[Any]:
UpperCAmelCase : Union[str, Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _lowercase( self , A , A = None ) -> str:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowercase( self , A , A = None ) -> int:
UpperCAmelCase : Union[str, Any] = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowercase( self , A , A , A , A , **A ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
UpperCAmelCase : str = src_lang
UpperCAmelCase : Optional[Any] = self(lowercase_ , add_special_tokens=lowercase_ , return_tensors=lowercase_ , **lowercase_ )
UpperCAmelCase : Optional[int] = self.convert_tokens_to_ids(lowercase_ )
UpperCAmelCase : Union[str, Any] = tgt_lang_id
return inputs
def _lowercase( self , A , A = "en_XX" , A = None , A = "ro_RO" , **A , ) -> List[Any]:
UpperCAmelCase : Union[str, Any] = src_lang
UpperCAmelCase : Optional[Any] = tgt_lang
return super().prepare_seqaseq_batch(lowercase_ , lowercase_ , **lowercase_ )
def _lowercase( self ) -> Any:
return self.set_src_lang_special_tokens(self.src_lang )
def _lowercase( self ) -> Optional[int]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _lowercase( self , A ) -> Optional[int]:
UpperCAmelCase : str = self.convert_tokens_to_ids(lowercase_ )
UpperCAmelCase : List[str] = []
UpperCAmelCase : Dict = [self.eos_token_id, self.cur_lang_code]
UpperCAmelCase : List[str] = self.convert_ids_to_tokens(self.prefix_tokens )
UpperCAmelCase : Optional[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
UpperCAmelCase : Union[str, Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowercase( self , A ) -> Union[str, Any]:
UpperCAmelCase : int = self.convert_tokens_to_ids(lowercase_ )
UpperCAmelCase : Union[str, Any] = []
UpperCAmelCase : Dict = [self.eos_token_id, self.cur_lang_code]
UpperCAmelCase : Tuple = self.convert_ids_to_tokens(self.prefix_tokens )
UpperCAmelCase : int = self.convert_ids_to_tokens(self.suffix_tokens )
UpperCAmelCase : str = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowercase( self , A , A = None ) -> Tuple:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(lowercase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
UpperCAmelCase : Optional[Any] = os.path.join(
lowercase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
return (out_vocab_file,)
| 351 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase = 1_0_0 , ) -> float:
UpperCAmelCase : Optional[int] = x_start
UpperCAmelCase : int = fnc(_snake_case )
UpperCAmelCase : List[str] = 0.0
for _ in range(_snake_case ):
# Approximates curve as a sequence of linear lines and sums their length
UpperCAmelCase : int = (x_end - x_start) / steps + xa
UpperCAmelCase : Dict = fnc(_snake_case )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
UpperCAmelCase : Tuple = xa
UpperCAmelCase : Dict = fxa
return length
if __name__ == "__main__":
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
return math.sin(1_0 * x )
print("""f(x) = sin(10 * x)""")
print("""The length of the curve from x = -10 to x = 10 is:""")
a : Union[str, Any] = 1_0
while i <= 1_0_0_0_0_0:
print(F'''With {i} steps: {line_length(f, -1_0, 1_0, i)}''')
i *= 1_0
| 352 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase : str = [[0 for i in range(_lowercase )] for j in range(_lowercase )]
# since string of zero length match pattern of zero length
UpperCAmelCase : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowercase ):
UpperCAmelCase : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowercase ):
UpperCAmelCase : Optional[Any] = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _lowercase ):
for j in range(1 , _lowercase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase : Union[str, Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase : List[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase : Optional[int] = dp[i - 1][j]
else:
UpperCAmelCase : Any = 0
else:
UpperCAmelCase : str = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a : List[str] = """aab"""
a : Optional[int] = """c*a*b"""
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = 2_0_0 ) -> Any:
UpperCAmelCase : Tuple = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase : List[str] = [0] * (pence + 1)
UpperCAmelCase : Tuple = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(_A , pence + 1 , 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(2_0_0) == 7_3_6_8_2
| 353 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int:
UpperCAmelCase : int = 1
UpperCAmelCase : str = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase : Tuple = pre_numerator
UpperCAmelCase : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1
UpperCAmelCase : Union[str, Any] = cur_numerator
UpperCAmelCase : Optional[int] = e_cont * pre_numerator + temp
return sum_digits(_lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> bool:
# Base Case
if curr_ind == len(_lowercase ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(_lowercase ) ):
if valid_connection(_lowercase , _lowercase , _lowercase , _lowercase ):
# Insert current vertex into path as next transition
UpperCAmelCase : Optional[int] = next_ver
# Validate created path
if util_hamilton_cycle(_lowercase , _lowercase , curr_ind + 1 ):
return True
# Backtrack
UpperCAmelCase : Union[str, Any] = -1
return False
def __lowerCamelCase ( _lowercase , _lowercase = 0 ) -> list[int]:
UpperCAmelCase : Tuple = [-1] * (len(_lowercase ) + 1)
# initialize start and end of path with starting index
UpperCAmelCase : Optional[int] = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(_lowercase , _lowercase , 1 ) else []
| 354 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=0.0_1 , A=1000 ) -> List[str]:
UpperCAmelCase : List[Any] = p_stop
UpperCAmelCase : Optional[int] = max_length
def __iter__( self ) -> Union[str, Any]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase : Any = random.random() < self.p_stop
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A , A , A=False , A=True ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
UpperCAmelCase : List[str] = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def _lowercase( self ) -> Union[str, Any]:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : Tuple = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(A , A )
def _lowercase( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def _lowercase( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : str = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def _lowercase( self ) -> List[Any]:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase : List[str] = [BatchSamplerShard(A , 2 , A , even_batches=A ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def _lowercase( self , A , A , A , A=False , A=2 , A=False ) -> Tuple:
random.seed(A )
UpperCAmelCase : Dict = list(A )
UpperCAmelCase : Any = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
UpperCAmelCase : Dict = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(A )
iterable_dataset_lists.append(list(A ) )
UpperCAmelCase : Optional[Any] = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
UpperCAmelCase : List[Any] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
UpperCAmelCase : List[Any] = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = 42
UpperCAmelCase : List[Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
UpperCAmelCase : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase : Optional[Any] = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _lowercase( self ) -> Dict:
Accelerator()
UpperCAmelCase : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 338 | 0 |
a : Dict = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
a : List[Any] = {value: key for key, value in MORSE_CODE_DICT.items()}
def __lowerCamelCase ( _lowercase ) -> Tuple:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def __lowerCamelCase ( _lowercase ) -> int:
return "".join(REVERSE_DICT[char] for char in message.split() )
def __lowerCamelCase ( ) -> Any:
UpperCAmelCase : List[str] = """Morse code here!"""
print(snake_case_ )
UpperCAmelCase : List[str] = encrypt(snake_case_ )
print(snake_case_ )
UpperCAmelCase : Optional[Any] = decrypt(snake_case_ )
print(snake_case_ )
if __name__ == "__main__":
main()
| 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
'''simple docstring'''
import math
class UpperCamelCase_ :
def __init__( self , A=0 ) -> Any: # a graph with Node 0,1,...,N-1
UpperCAmelCase : List[str] = n
UpperCAmelCase : Any = [
[math.inf for j in range(0 , A )] for i in range(0 , A )
] # adjacency matrix for weight
UpperCAmelCase : int = [
[math.inf for j in range(0 , A )] for i in range(0 , A )
] # dp[i][j] stores minimum distance from i to j
def _lowercase( self , A , A , A ) -> Optional[int]:
UpperCAmelCase : Union[str, Any] = w
def _lowercase( self ) -> List[Any]:
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
UpperCAmelCase : str = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def _lowercase( self , A , A ) -> List[str]:
return self.dp[u][v]
if __name__ == "__main__":
a : int = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 356 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : List[str] = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class UpperCamelCase_ ( __lowerCamelCase ):
lowercase = 'vivit'
def __init__( self , A=224 , A=32 , A=[2, 16, 16] , A=3 , A=768 , A=12 , A=12 , A=3072 , A="gelu_fast" , A=0.0 , A=0.0 , A=0.0_2 , A=1e-06 , A=True , **A , ) -> Any:
UpperCAmelCase : str = hidden_size
UpperCAmelCase : List[str] = num_hidden_layers
UpperCAmelCase : List[str] = num_attention_heads
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : Any = hidden_act
UpperCAmelCase : List[Any] = hidden_dropout_prob
UpperCAmelCase : Tuple = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = initializer_range
UpperCAmelCase : List[str] = layer_norm_eps
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_frames
UpperCAmelCase : Optional[Any] = tubelet_size
UpperCAmelCase : Any = num_channels
UpperCAmelCase : Tuple = qkv_bias
super().__init__(**UpperCamelCase_ )
| 357 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
for i in range(_lowercase , _lowercase ):
if array[i] == target:
return i
return -1
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
UpperCAmelCase : Tuple = 0
UpperCAmelCase : List[str] = len(_lowercase )
while left <= right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = (left + right) // 3 + 1
UpperCAmelCase : Union[str, Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCAmelCase : Any = one_third - 1
elif array[two_third] < target:
UpperCAmelCase : Tuple = two_third + 1
else:
UpperCAmelCase : int = one_third + 1
UpperCAmelCase : List[Any] = two_third - 1
else:
return -1
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = (left + right) // 3 + 1
UpperCAmelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowercase , one_third - 1 , _lowercase , _lowercase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowercase , _lowercase , _lowercase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowercase , _lowercase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Any = input("""Enter numbers separated by comma:\n""").strip()
a : Any = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
a : Tuple = int(input("""Enter the number to be found in the list:\n""").strip())
a : Union[str, Any] = ite_ternary_search(collection, target)
a : Optional[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 338 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
a : Any = logging.get_logger(__name__)
a : int = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class UpperCamelCase_ ( lowerCAmelCase_ ):
def __init__( self , A=None , A=None , *A , **A ) -> Union[str, Any]:
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
if config is None:
assert isinstance(self.model , __lowerCAmelCase ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f''' {self.model.__class__}'''
)
UpperCAmelCase : Union[str, Any] = self.model.config
else:
UpperCAmelCase : List[Any] = config
UpperCAmelCase : str = data_args
UpperCAmelCase : str = self.config.tgt_vocab_size if isinstance(self.config , __lowerCAmelCase ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
""" padding..""" )
if self.args.label_smoothing == 0:
UpperCAmelCase : int = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
UpperCAmelCase : List[Any] = label_smoothed_nll_loss
def _lowercase( self , A ) -> Tuple:
if self.optimizer is None:
UpperCAmelCase : Optional[int] = ["""bias""", """LayerNorm.weight"""]
UpperCAmelCase : Dict = [
{
"""params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"""weight_decay""": self.args.weight_decay,
},
{
"""params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"""weight_decay""": 0.0,
},
]
UpperCAmelCase : Optional[Any] = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
UpperCAmelCase : Optional[int] = Adafactor
UpperCAmelCase : List[str] = {"""scale_parameter""": False, """relative_step""": False}
else:
UpperCAmelCase : Tuple = AdamW
UpperCAmelCase : Tuple = {
"""betas""": (self.args.adam_betaa, self.args.adam_betaa),
"""eps""": self.args.adam_epsilon,
}
UpperCAmelCase : Union[str, Any] = self.args.learning_rate
if self.sharded_ddp:
UpperCAmelCase : Dict = OSS(
params=__lowerCAmelCase , optim=__lowerCAmelCase , **__lowerCAmelCase , )
else:
UpperCAmelCase : List[str] = optimizer_cls(__lowerCAmelCase , **__lowerCAmelCase )
if self.lr_scheduler is None:
UpperCAmelCase : List[Any] = self._get_lr_scheduler(__lowerCAmelCase )
else: # ignoring --lr_scheduler
logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.""" )
def _lowercase( self , A ) -> List[str]:
UpperCAmelCase : List[Any] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
UpperCAmelCase : int = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
UpperCAmelCase : str = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
UpperCAmelCase : List[Any] = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=__lowerCAmelCase )
return scheduler
def _lowercase( self ) -> List[Any]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _lowercase( self , A , A , A ) -> List[str]:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
UpperCAmelCase : Dict = model(**__lowerCAmelCase , use_cache=__lowerCAmelCase )[0]
UpperCAmelCase : Tuple = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
UpperCAmelCase , UpperCAmelCase : Any = model(**__lowerCAmelCase , labels=__lowerCAmelCase , use_cache=__lowerCAmelCase )[:2]
else:
# compute label smoothed loss
UpperCAmelCase : int = model(**__lowerCAmelCase , use_cache=__lowerCAmelCase )[0]
UpperCAmelCase : Dict = torch.nn.functional.log_softmax(__lowerCAmelCase , dim=-1 )
UpperCAmelCase , UpperCAmelCase : int = self.loss_fn(__lowerCAmelCase , __lowerCAmelCase , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _lowercase( self , A , A ) -> Tuple:
UpperCAmelCase : Any = inputs.pop("""labels""" )
UpperCAmelCase , UpperCAmelCase : str = self._compute_loss(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return loss
def _lowercase( self , A , A , A , A = None , ) -> List[Any]:
UpperCAmelCase : Dict = self._prepare_inputs(__lowerCAmelCase )
UpperCAmelCase : Dict = {
"""max_length""": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"""num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
UpperCAmelCase : Union[str, Any] = self.model.generate(
inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , **__lowerCAmelCase , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
UpperCAmelCase : str = self._pad_tensors_to_max_len(__lowerCAmelCase , gen_kwargs["""max_length"""] )
UpperCAmelCase : int = inputs.pop("""labels""" )
with torch.no_grad():
# compute loss on predict data
UpperCAmelCase , UpperCAmelCase : List[Any] = self._compute_loss(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Optional[Any] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
UpperCAmelCase : str = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
UpperCAmelCase : Optional[Any] = self._pad_tensors_to_max_len(__lowerCAmelCase , gen_kwargs["""max_length"""] )
return (loss, logits, labels)
def _lowercase( self , A , A ) -> Union[str, Any]:
# If PAD token is not defined at least EOS token has to be defined
UpperCAmelCase : Dict = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"""Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
f''' padded to `max_length`={max_length}''' )
UpperCAmelCase : int = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
UpperCAmelCase : Dict = tensor
return padded_tensor
| 358 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Optional[int] = 0
def __eq__( self , A ) -> Optional[Any]:
return self.position == cell.position
def _lowercase( self ) -> Tuple:
print(self.position )
class UpperCamelCase_ :
def __init__( self , A=(5, 5) ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = np.zeros(A )
UpperCAmelCase : int = world_size[0]
UpperCAmelCase : List[str] = world_size[1]
def _lowercase( self ) -> List[Any]:
print(self.w )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : Optional[Any] = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCAmelCase : List[Any] = cell.position[0]
UpperCAmelCase : Union[str, Any] = cell.position[1]
UpperCAmelCase : Optional[int] = []
for n in neughbour_cord:
UpperCAmelCase : Any = current_x + n[0]
UpperCAmelCase : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCAmelCase : str = Cell()
UpperCAmelCase : List[str] = (x, y)
UpperCAmelCase : Dict = cell
neighbours.append(A )
return neighbours
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
_open.append(_lowercase )
while _open:
UpperCAmelCase : Any = np.argmin([n.f for n in _open] )
UpperCAmelCase : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowercase ) )
if current == goal:
break
for n in world.get_neigbours(_lowercase ):
for c in _closed:
if c == n:
continue
UpperCAmelCase : List[str] = current.g + 1
UpperCAmelCase , UpperCAmelCase : List[str] = n.position
UpperCAmelCase , UpperCAmelCase : Dict = goal.position
UpperCAmelCase : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCAmelCase : Dict = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowercase )
UpperCAmelCase : Dict = []
while current.parent is not None:
path.append(current.position )
UpperCAmelCase : Optional[int] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a : List[str] = Gridworld()
# Start position and goal
a : Optional[int] = Cell()
a : Optional[Any] = (0, 0)
a : Optional[Any] = Cell()
a : str = (4, 4)
print(F'''path from {start.position} to {goal.position}''')
a : List[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a : Any = 1
print(world.w)
| 338 | 0 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def __lowerCamelCase ( _lowercase , _lowercase=False ) -> Tuple:
try:
UpperCAmelCase : Tuple = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
UpperCAmelCase : Union[str, Any] = default
else:
# KEY is set, convert it to True or False.
try:
UpperCAmelCase : List[str] = strtobool(_UpperCamelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F'''If set, {key} must be yes or no.''' )
return _value
a : Union[str, Any] = parse_flag_from_env("""RUN_SLOW""", default=False)
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
return unittest.skip("""Test was skipped""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> str:
return unittest.skipUnless(_run_slow_tests , """test is slow""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
return unittest.skipUnless(not torch.cuda.is_available() , """test requires only a CPU""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Any:
return unittest.skipUnless(torch.cuda.is_available() , """test requires a GPU""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Tuple:
return unittest.skipUnless(is_xpu_available() , """test requires a XPU""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> List[str]:
return unittest.skipUnless(is_mps_available() , """test requires a `mps` backend support in `torch`""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , """test requires the Hugging Face suite""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Dict:
return unittest.skipUnless(is_bnb_available() , """test requires the bitsandbytes library""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Union[str, Any]:
return unittest.skipUnless(is_tpu_available() , """test requires TPU""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
return unittest.skipUnless(torch.cuda.device_count() == 1 , """test requires a GPU""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> str:
return unittest.skipUnless(torch.xpu.device_count() == 1 , """test requires a XPU""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
return unittest.skipUnless(torch.cuda.device_count() > 1 , """test requires multiple GPUs""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> List[str]:
return unittest.skipUnless(torch.xpu.device_count() > 1 , """test requires multiple XPUs""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
return unittest.skipUnless(is_safetensors_available() , """test requires safetensors""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> str:
return unittest.skipUnless(is_deepspeed_available() , """test requires DeepSpeed""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Tuple:
return unittest.skipUnless(is_torch_version(""">=""" , """1.12.0""" ) , """test requires torch version >= 1.12.0""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase=None , _lowercase=None ) -> int:
if test_case is None:
return partial(_UpperCamelCase , version=_UpperCamelCase )
return unittest.skipUnless(is_torch_version(""">=""" , _UpperCamelCase ) , F'''test requires torch version >= {version}''' )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Union[str, Any]:
return unittest.skipUnless(is_tensorboard_available() , """test requires Tensorboard""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> str:
return unittest.skipUnless(is_wandb_available() , """test requires wandb""" )(_UpperCamelCase )
def __lowerCamelCase ( _lowercase ) -> Any:
return unittest.skipUnless(is_comet_ml_available() , """test requires comet_ml""" )(_UpperCamelCase )
a : Optional[int] = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def __lowerCamelCase ( _lowercase ) -> Dict:
return unittest.skipUnless(
_atleast_one_tracker_available , """test requires at least one tracker to be available and for `comet_ml` to not be installed""" , )(_UpperCamelCase )
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = True
@classmethod
def _lowercase( cls ) -> Union[str, Any]:
UpperCAmelCase : Dict = tempfile.mkdtemp()
@classmethod
def _lowercase( cls ) -> List[Any]:
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def _lowercase( self ) -> List[Any]:
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("""**/*""" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(a_ )
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> int:
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A ) -> int:
UpperCAmelCase : Any = mocks if isinstance(a_ , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Any = AcceleratorState()
UpperCAmelCase : Union[str, Any] = tensor[None].clone().to(state.device )
UpperCAmelCase : Optional[int] = gather(_UpperCamelCase ).cpu()
UpperCAmelCase : Union[str, Any] = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCamelCase ):
return False
return True
class UpperCamelCase_ :
def __init__( self , A , A , A ) -> Dict:
UpperCAmelCase : List[Any] = returncode
UpperCAmelCase : int = stdout
UpperCAmelCase : Union[str, Any] = stderr
async def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
while True:
UpperCAmelCase : Optional[Any] = await stream.readline()
if line:
callback(_UpperCamelCase )
else:
break
async def __lowerCamelCase ( _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=False , _lowercase=False ) -> _RunOutput:
if echo:
print("""\nRunning: """ , """ """.join(_UpperCamelCase ) )
UpperCAmelCase : Optional[int] = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCamelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCamelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
UpperCAmelCase : Any = []
UpperCAmelCase : List[Any] = []
def tee(_lowercase , _lowercase , _lowercase , _lowercase="" ):
UpperCAmelCase : List[Any] = line.decode("""utf-8""" ).rstrip()
sink.append(_UpperCamelCase )
if not quiet:
print(_UpperCamelCase , _UpperCamelCase , file=_UpperCamelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _lowercase : tee(_UpperCamelCase , _UpperCamelCase , sys.stdout , label="""stdout:""" ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _lowercase : tee(_UpperCamelCase , _UpperCamelCase , sys.stderr , label="""stderr:""" ) ) ),
] , timeout=_UpperCamelCase , )
return _RunOutput(await p.wait() , _UpperCamelCase , _UpperCamelCase )
def __lowerCamelCase ( _lowercase , _lowercase=None , _lowercase=None , _lowercase=1_8_0 , _lowercase=False , _lowercase=True ) -> _RunOutput:
UpperCAmelCase : str = asyncio.get_event_loop()
UpperCAmelCase : Any = loop.run_until_complete(
_stream_subprocess(_UpperCamelCase , env=_UpperCamelCase , stdin=_UpperCamelCase , timeout=_UpperCamelCase , quiet=_UpperCamelCase , echo=_UpperCamelCase ) )
UpperCAmelCase : Any = """ """.join(_UpperCamelCase )
if result.returncode > 0:
UpperCAmelCase : Any = """\n""".join(result.stderr )
raise RuntimeError(
F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
F'''The combined stderr from workers follows:\n{stderr}''' )
return result
class UpperCamelCase_ ( SCREAMING_SNAKE_CASE__ ):
pass
def __lowerCamelCase ( _lowercase , _lowercase=False ) -> List[Any]:
try:
UpperCAmelCase : List[str] = subprocess.check_output(_UpperCamelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCamelCase , """decode""" ):
UpperCAmelCase : List[Any] = output.decode("""utf-8""" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F'''Command `{' '.join(_UpperCamelCase )}` failed with the following error:\n\n{e.output.decode()}''' ) from e
| 359 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( _lowercase ) -> Image:
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = image.size
UpperCAmelCase : Tuple = 0
UpperCAmelCase : Optional[int] = image.load()
for i in range(lowerCAmelCase__ ):
for j in range(lowerCAmelCase__ ):
UpperCAmelCase : List[str] = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(lowerCAmelCase__ ):
for i in range(lowerCAmelCase__ ):
UpperCAmelCase : Dict = 2_5_5 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
a : Tuple = mean_threshold(Image.open("""path_to_image""").convert("""L"""))
image.save("""output_image_path""")
| 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Tuple = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
a : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 361 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> float:
return base * power(lowercase_ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("""Raise base to the power of exponent using recursion...""")
a = int(input("""Enter the base: """).strip())
a = int(input("""Enter the exponent: """).strip())
a = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
a = 1 / result
print(F'''{base} to the power of {exponent} is {result}''')
| 362 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = split_dict._to_yaml_list()
assert len(_lowercase ) == len(_lowercase )
UpperCAmelCase : List[Any] = SplitDict._from_yaml_list(_lowercase )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCAmelCase : List[str] = None
# the split name of split_dict takes over the name of the split info object
UpperCAmelCase : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=_lowercase ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCAmelCase : Optional[Any] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
@slow
def _lowercase( self ) -> List[str]:
UpperCAmelCase : str = XLMRobertaModel.from_pretrained("""xlm-roberta-base""" )
UpperCAmelCase : Tuple = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
UpperCAmelCase : str = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase : str = torch.tensor(
[[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase : Optional[Any] = model(_a )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , _a )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _a , atol=1e-3 ) )
@slow
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[int] = XLMRobertaModel.from_pretrained("""xlm-roberta-large""" )
UpperCAmelCase : Tuple = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
UpperCAmelCase : Optional[int] = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase : Optional[int] = torch.tensor(
[[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase : Union[str, Any] = model(_a )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , _a )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _a , atol=1e-3 ) )
| 363 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : int = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[int] = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 364 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"nielsr/canine-s": 2_0_4_8,
}
# Unicode defines 1,114,112 total “codepoints”
a = 1_1_1_4_1_1_2
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
a = 0
a = 0xE_000
a = 0xE_001
a = 0xE_002
a = 0xE_003
a = 0xE_004
# Maps special codepoints to human-readable names.
a = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
a = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class UpperCamelCase_ ( snake_case__ ):
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , A=chr(UpperCAmelCase_ ) , A=chr(UpperCAmelCase_ ) , A=chr(UpperCAmelCase_ ) , A=chr(UpperCAmelCase_ ) , A=chr(UpperCAmelCase_ ) , A=chr(UpperCAmelCase_ ) , A=False , A=2048 , **A , ) -> List[str]:
UpperCAmelCase : Any = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else bos_token
UpperCAmelCase : int = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else eos_token
UpperCAmelCase : Union[str, Any] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else sep_token
UpperCAmelCase : Tuple = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else cls_token
UpperCAmelCase : List[Any] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : List[Any] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else mask_token
super().__init__(
bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , add_prefix_space=UpperCAmelCase_ , model_max_length=UpperCAmelCase_ , **UpperCAmelCase_ , )
# Creates a mapping for looking up the IDs of special symbols.
UpperCAmelCase : Dict[str, int] = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
UpperCAmelCase : Optional[Any] = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
UpperCAmelCase : Dict[int, str] = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
UpperCAmelCase : List[Any] = UNICODE_VOCAB_SIZE
UpperCAmelCase : str = len(self._special_codepoints )
@property
def _lowercase( self ) -> str:
return self._unicode_vocab_size
def _lowercase( self , A ) -> Union[str, Any]:
return list(UpperCAmelCase_ )
def _lowercase( self , A ) -> List[str]:
try:
return ord(UpperCAmelCase_ )
except TypeError:
raise ValueError(f'''invalid token: \'{token}\'''' )
def _lowercase( self , A ) -> int:
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(UpperCAmelCase_ )
except TypeError:
raise ValueError(f'''invalid id: {index}''' )
def _lowercase( self , A ) -> Tuple:
return "".join(UpperCAmelCase_ )
def _lowercase( self , A , A = None ) -> Optional[Any]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : List[str] = [self.cls_token_id]
UpperCAmelCase : Union[str, Any] = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def _lowercase( self , A , A = None , A = False ) -> List[Any]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase_ , token_ids_a=UpperCAmelCase_ , already_has_special_tokens=UpperCAmelCase_ )
UpperCAmelCase : List[str] = [1] + ([0] * len(UpperCAmelCase_ )) + [1]
if token_ids_a is not None:
result += ([0] * len(UpperCAmelCase_ )) + [1]
return result
def _lowercase( self , A , A = None ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [self.sep_token_id]
UpperCAmelCase : List[Any] = [self.cls_token_id]
UpperCAmelCase : Optional[Any] = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def _lowercase( self , A , A = None ) -> str:
return ()
| 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class UpperCamelCase_ ( lowerCamelCase_ ):
lowercase = (DPMSolverSDEScheduler,)
lowercase = 10
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[Any] = {
"""num_train_timesteps""": 1100,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""noise_sampler_seed""": 0,
}
config.update(**lowerCAmelCase__ )
return config
def _lowercase( self ) -> str:
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def _lowercase( self ) -> str:
for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] , [0.0_0_0_2, 0.0_0_2, 0.0_2] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def _lowercase( self ) -> Union[str, Any]:
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def _lowercase( self ) -> Dict:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : int = self.scheduler_classes[0]
UpperCAmelCase : List[Any] = self.get_scheduler_config()
UpperCAmelCase : List[str] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : Optional[int] = self.dummy_model()
UpperCAmelCase : Tuple = self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : Optional[Any] = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Optional[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : List[str] = model(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : Union[str, Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : List[str] = output.prev_sample
UpperCAmelCase : Optional[int] = torch.sum(torch.abs(lowerCAmelCase__ ) )
UpperCAmelCase : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47821044921875 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59352111816406 ) < 1e-2
assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = self.scheduler_classes[0]
UpperCAmelCase : List[Any] = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCAmelCase : int = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : Dict = self.dummy_model()
UpperCAmelCase : List[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : List[Any] = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Optional[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : Any = model(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : Optional[int] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : str = output.prev_sample
UpperCAmelCase : str = torch.sum(torch.abs(lowerCAmelCase__ ) )
UpperCAmelCase : Dict = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77149200439453 ) < 1e-2
assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1663360595703 ) < 1e-2
assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7 ) < 1e-3
else:
assert abs(result_sum.item() - 119.8487548828125 ) < 1e-2
assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1 ) < 1e-3
def _lowercase( self ) -> List[str]:
UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
UpperCAmelCase : Tuple = self.get_scheduler_config()
UpperCAmelCase : Optional[Any] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
UpperCAmelCase : Any = self.dummy_model()
UpperCAmelCase : int = self.dummy_sample_deter.to(lowerCAmelCase__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
UpperCAmelCase : List[Any] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : Optional[Any] = model(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : Union[str, Any] = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : List[Any] = output.prev_sample
UpperCAmelCase : Any = torch.sum(torch.abs(lowerCAmelCase__ ) )
UpperCAmelCase : List[str] = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46957397460938 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59353637695312 ) < 1e-2
assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : List[Any] = self.scheduler_classes[0]
UpperCAmelCase : Tuple = self.get_scheduler_config()
UpperCAmelCase : Union[str, Any] = scheduler_class(**lowerCAmelCase__ , use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCAmelCase__ )
UpperCAmelCase : Dict = self.dummy_model()
UpperCAmelCase : Any = self.dummy_sample_deter.to(lowerCAmelCase__ ) * scheduler.init_noise_sigma
UpperCAmelCase : Dict = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
UpperCAmelCase : List[str] = scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : List[Any] = model(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : Any = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase : Dict = output.prev_sample
UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
UpperCAmelCase : int = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66974135742188 ) < 1e-2
assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63653564453125 ) < 1e-2
assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
else:
assert abs(result_sum.item() - 170.3135223388672 ) < 1e-2
assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
| 366 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class UpperCamelCase_ ( UpperCamelCase__ ):
lowercase = 42
lowercase = 42
class UpperCamelCase_ ( UpperCamelCase__ , UpperCamelCase__ ):
lowercase = 1
@register_to_config
def __init__( self , A = 2000 , A = 0.1_5 , A = 0.0_1 , A = 1_3_4_8.0 , A = 1e-5 , A = 1 , ) -> List[str]:
# standard deviation of the initial noise distribution
UpperCAmelCase : Union[str, Any] = sigma_max
# setable values
UpperCAmelCase : str = None
self.set_sigmas(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def _lowercase( self , A , A = None ) -> List[Any]:
return sample
def _lowercase( self , A , A = None , A = None ) -> Tuple:
UpperCAmelCase : int = sampling_eps if sampling_eps is not None else self.config.sampling_eps
UpperCAmelCase : List[str] = torch.linspace(1 , __lowerCamelCase , __lowerCamelCase , device=__lowerCamelCase )
def _lowercase( self , A , A = None , A = None , A = None ) -> Optional[int]:
UpperCAmelCase : Optional[int] = sigma_min if sigma_min is not None else self.config.sigma_min
UpperCAmelCase : List[Any] = sigma_max if sigma_max is not None else self.config.sigma_max
UpperCAmelCase : Tuple = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(__lowerCamelCase , __lowerCamelCase )
UpperCAmelCase : Optional[Any] = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
UpperCAmelCase : List[Any] = torch.exp(torch.linspace(math.log(__lowerCamelCase ) , math.log(__lowerCamelCase ) , __lowerCamelCase ) )
UpperCAmelCase : Optional[int] = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def _lowercase( self , A , A ) -> str:
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def _lowercase( self , A , A , A , A = None , A = True , ) -> Any:
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler""" )
UpperCAmelCase : Any = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
UpperCAmelCase : int = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
UpperCAmelCase : str = timesteps.to(self.discrete_sigmas.device )
UpperCAmelCase : List[str] = self.discrete_sigmas[timesteps].to(sample.device )
UpperCAmelCase : List[Any] = self.get_adjacent_sigma(__lowerCamelCase , __lowerCamelCase ).to(sample.device )
UpperCAmelCase : Any = torch.zeros_like(__lowerCamelCase )
UpperCAmelCase : Optional[int] = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
UpperCAmelCase : int = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
UpperCAmelCase : Any = diffusion.unsqueeze(-1 )
UpperCAmelCase : Optional[Any] = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
UpperCAmelCase : List[Any] = randn_tensor(
sample.shape , layout=sample.layout , generator=__lowerCamelCase , device=sample.device , dtype=sample.dtype )
UpperCAmelCase : List[str] = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
UpperCAmelCase : Dict = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=__lowerCamelCase , prev_sample_mean=__lowerCamelCase )
def _lowercase( self , A , A , A = None , A = True , ) -> str:
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler""" )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
UpperCAmelCase : Any = randn_tensor(sample.shape , layout=sample.layout , generator=__lowerCamelCase ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
UpperCAmelCase : Tuple = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
UpperCAmelCase : Optional[int] = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
UpperCAmelCase : int = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
UpperCAmelCase : Dict = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
UpperCAmelCase : List[Any] = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
UpperCAmelCase : int = step_size.unsqueeze(-1 )
UpperCAmelCase : Union[str, Any] = sample + step_size * model_output
UpperCAmelCase : str = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__lowerCamelCase )
def _lowercase( self , A , A , A , ) -> Union[str, Any]:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
UpperCAmelCase : List[Any] = timesteps.to(original_samples.device )
UpperCAmelCase : List[Any] = self.discrete_sigmas.to(original_samples.device )[timesteps]
UpperCAmelCase : Tuple = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(__lowerCamelCase ) * sigmas[:, None, None, None]
)
UpperCAmelCase : Optional[int] = noise + original_samples
return noisy_samples
def __len__( self ) -> Any:
return self.config.num_train_timesteps
| 367 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : str = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'levit'
def __init__( self , A=224 , A=3 , A=3 , A=2 , A=1 , A=16 , A=[128, 256, 384] , A=[4, 8, 12] , A=[4, 4, 4] , A=[16, 16, 16] , A=0 , A=[2, 2, 2] , A=[2, 2, 2] , A=0.0_2 , **A , ) -> int:
super().__init__(**A )
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = kernel_size
UpperCAmelCase : Optional[int] = stride
UpperCAmelCase : Dict = padding
UpperCAmelCase : List[Any] = hidden_sizes
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : Any = key_dim
UpperCAmelCase : str = drop_path_rate
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = attention_ratio
UpperCAmelCase : Optional[Any] = mlp_ratio
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : int = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
| 338 | 0 |
'''simple docstring'''
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class UpperCamelCase_ ( A_ ):
lowercase = 'microsoft/speecht5_tts'
lowercase = (
'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
'text to read (in English) and returns a waveform object containing the sound.'
)
lowercase = 'text_reader'
lowercase = SpeechTaProcessor
lowercase = SpeechTaForTextToSpeech
lowercase = SpeechTaHifiGan
lowercase = ['text']
lowercase = ['audio']
def _lowercase( self ) -> Any:
if self.post_processor is None:
UpperCAmelCase : Optional[int] = """microsoft/speecht5_hifigan"""
super().setup()
def _lowercase( self , A , A=None ) -> Dict:
UpperCAmelCase : int = self.pre_processor(text=_lowerCamelCase , return_tensors="""pt""" , truncation=_lowerCamelCase )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("""Datasets needs to be installed if not passing speaker embeddings.""" )
UpperCAmelCase : List[Any] = load_dataset("""Matthijs/cmu-arctic-xvectors""" , split="""validation""" )
UpperCAmelCase : List[str] = torch.tensor(embeddings_dataset[7305]["""xvector"""] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def _lowercase( self , A ) -> str:
with torch.no_grad():
return self.model.generate_speech(**_lowerCamelCase )
def _lowercase( self , A ) -> Any:
with torch.no_grad():
return self.post_processor(_lowerCamelCase ).cpu().detach()
| 368 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : List[str] = """Hello, World!"""
a : List[Any] = """en_XX"""
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Dict = Path("""data_bin""" )
UpperCAmelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowercase ).parent ) , checkpoint_file=Path(_lowercase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowercase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowercase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowercase )
UpperCAmelCase : List[str] = xmod.model.encoder.sentence_encoder
UpperCAmelCase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowercase )
UpperCAmelCase : str = XmodForSequenceClassification(_lowercase ) if classification_head else XmodForMaskedLM(_lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : int = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : List[str] = model.roberta.encoder.layer[i]
UpperCAmelCase : Optional[Any] = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
UpperCAmelCase : List[Any] = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Any = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
UpperCAmelCase : Any = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[str] = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : Tuple = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
UpperCAmelCase : List[str] = xmod_layer.fca.weight
UpperCAmelCase : str = xmod_layer.fca.bias
# output
UpperCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
UpperCAmelCase : Dict = xmod_layer.fca.weight
UpperCAmelCase : Dict = xmod_layer.fca.bias
UpperCAmelCase : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase : Union[str, Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : str = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : List[str] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : List[Any] = bert_output.adapter_modules[lang_code]
UpperCAmelCase : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any = from_adapter.fca.weight
UpperCAmelCase : int = from_adapter.fca.bias
UpperCAmelCase : Dict = from_adapter.fca.weight
UpperCAmelCase : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Tuple = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].dense.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].dense.bias
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].out_proj.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
UpperCAmelCase : Dict = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : str = xmod.model.encoder.lm_head.weight
UpperCAmelCase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : Any = xmod.encode(_lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowercase )
UpperCAmelCase : Optional[int] = model(_lowercase )[0]
if classification_head:
UpperCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase ) )
else:
UpperCAmelCase : Optional[Any] = xmod.model(_lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Dict = torch.allclose(_lowercase , _lowercase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowercase ).mkdir(parents=_lowercase , exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
a : List[str] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 338 | 0 |
from __future__ import annotations
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
if len(_snake_case ) == 0:
return False
UpperCAmelCase : Dict = len(_snake_case ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , _snake_case )
else:
return binary_search(a_list[midpoint + 1 :] , _snake_case )
if __name__ == "__main__":
a : str = input("""Enter numbers separated by comma:\n""").strip()
a : Union[str, Any] = [int(item.strip()) for item in user_input.split(""",""")]
a : Optional[int] = int(input("""Enter the number to be found in the list:\n""").strip())
a : str = "" if binary_search(sequence, target) else "not "
print(F'''{target} was {not_str}found in {sequence}''')
| 369 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338 | 0 |
import os
import numpy
import onnx
def __lowerCamelCase ( _lowercase , _lowercase ) -> Union[str, Any]:
UpperCAmelCase : Dict = a.name
UpperCAmelCase : Optional[int] = b.name
UpperCAmelCase : Union[str, Any] = ""
UpperCAmelCase : Dict = ""
UpperCAmelCase : str = a == b
UpperCAmelCase : str = name_a
UpperCAmelCase : List[str] = name_b
return res
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(_lowercase , _lowercase )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , _lowercase , _lowercase )
_graph_replace_input_with(node_proto.attribute[1].g , _lowercase , _lowercase )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , _lowercase , _lowercase )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> List[str]:
for n in graph_proto.node:
_node_replace_input_with(_lowercase , _lowercase , _lowercase )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
UpperCAmelCase : Dict = list(model.graph.initializer )
UpperCAmelCase : Optional[int] = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
UpperCAmelCase : List[str] = inits[i].name
UpperCAmelCase : str = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , _lowercase , _lowercase )
def __lowerCamelCase ( _lowercase ) -> Any:
UpperCAmelCase : Any = os.path.dirname(_lowercase )
UpperCAmelCase : Optional[int] = os.path.basename(_lowercase )
UpperCAmelCase : List[Any] = onnx.load(os.path.join(_lowercase , _lowercase ) )
UpperCAmelCase : Optional[Any] = list(model.graph.initializer )
UpperCAmelCase : str = set()
UpperCAmelCase : List[str] = {}
UpperCAmelCase : Tuple = []
UpperCAmelCase : str = 0
for i in range(len(_lowercase ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(_lowercase ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(_lowercase )
dup_set.add(_lowercase )
UpperCAmelCase : Union[str, Any] = inits[j].data_type
UpperCAmelCase : Tuple = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 1_1:
mem_size *= 8
else:
print("""unexpected data type: """ , _lowercase )
total_reduced_size += mem_size
UpperCAmelCase : str = inits[i].name
UpperCAmelCase : List[Any] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(_lowercase )
else:
UpperCAmelCase : Any = [name_j]
ind_to_replace.append((j, i) )
print("""total reduced size: """ , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 , """GB""" )
UpperCAmelCase : List[Any] = sorted(_lowercase )
_remove_dup_initializers_from_model(_lowercase , _lowercase , _lowercase )
UpperCAmelCase : Optional[int] = "optimized_" + model_file_name
UpperCAmelCase : List[Any] = os.path.join(_lowercase , _lowercase )
onnx.save(_lowercase , _lowercase )
return new_model
| 370 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> Any:
# For consistency across different places the DisjunctiveConstraint is called,
# dc.token_ids is a list of integers. It is also initialized only by integers.
UpperCAmelCase : int = [[1, 2, 4], [1, 2, 3, 4]]
UpperCAmelCase : List[str] = DisjunctiveConstraint(snake_case_ )
self.assertTrue(isinstance(dc.token_ids , snake_case_ ) )
with self.assertRaises(snake_case_ ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(snake_case_ ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def _lowercase( self ) -> Optional[Any]:
# We can't have constraints that are complete subsets of another. This leads to a preverse
# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
UpperCAmelCase : Union[str, Any] = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(snake_case_ ):
DisjunctiveConstraint(snake_case_ ) # fails here
def _lowercase( self ) -> str:
UpperCAmelCase : List[Any] = [[1, 2, 3], [1, 2, 4]]
UpperCAmelCase : Tuple = DisjunctiveConstraint(snake_case_ )
UpperCAmelCase : int = dc.update(1 )
UpperCAmelCase : int = stepped is True and completed is False and reset is False
self.assertTrue(snake_case_ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
UpperCAmelCase : int = dc.update(2 )
UpperCAmelCase : int = stepped is True and completed is False and reset is False
self.assertTrue(snake_case_ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCAmelCase : str = dc.update(3 )
UpperCAmelCase : Union[str, Any] = stepped is True and completed is True and reset is False
self.assertTrue(snake_case_ )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def _lowercase( self ) -> int:
UpperCAmelCase : Tuple = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
UpperCAmelCase : str = DisjunctiveConstraint(snake_case_ )
UpperCAmelCase : Dict = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
UpperCAmelCase : List[str] = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCAmelCase : Optional[Any] = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
UpperCAmelCase : Union[str, Any] = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
UpperCAmelCase : Union[str, Any] = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
UpperCAmelCase : Optional[Any] = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCAmelCase : str = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 371 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a : List[Any] = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = BlenderbotSmallTokenizer
def __init__( self , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , A=True , **A , ) -> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=A , merges=A , add_prefix_space=A , trim_offsets=A , ) , bos_token=A , eos_token=A , unk_token=A , **A , )
UpperCAmelCase : Optional[Any] = add_prefix_space
def _lowercase( self , A , A=None ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 338 | 0 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 350 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : int = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPegasusOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[str] = [
"""BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BigBirdPegasusForCausalLM""",
"""BigBirdPegasusForConditionalGeneration""",
"""BigBirdPegasusForQuestionAnswering""",
"""BigBirdPegasusForSequenceClassification""",
"""BigBirdPegasusModel""",
"""BigBirdPegasusPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
a : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 351 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 0 |
'''simple docstring'''
class UpperCamelCase_ :
def __init__( self , A ) -> List[Any]:
UpperCAmelCase : Any = val
UpperCAmelCase : Optional[int] = None
UpperCAmelCase : int = None
def _lowercase( self , A ) -> Dict:
if self.val:
if val < self.val:
if self.left is None:
UpperCAmelCase : str = Node(__SCREAMING_SNAKE_CASE )
else:
self.left.insert(__SCREAMING_SNAKE_CASE )
elif val > self.val:
if self.right is None:
UpperCAmelCase : Any = Node(__SCREAMING_SNAKE_CASE )
else:
self.right.insert(__SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase : Dict = val
def __lowerCamelCase ( _lowercase , _lowercase ) -> List[str]:
# Recursive traversal
if root:
inorder(root.left , _lowercase )
res.append(root.val )
inorder(root.right , _lowercase )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
# Build BST
if len(_lowercase ) == 0:
return arr
UpperCAmelCase : Any = Node(arr[0] )
for i in range(1 , len(_lowercase ) ):
root.insert(arr[i] )
# Traverse BST in order.
UpperCAmelCase : Dict = []
inorder(_lowercase , _lowercase )
return res
if __name__ == "__main__":
print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))
| 352 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase : str = [[0 for i in range(_lowercase )] for j in range(_lowercase )]
# since string of zero length match pattern of zero length
UpperCAmelCase : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowercase ):
UpperCAmelCase : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowercase ):
UpperCAmelCase : Optional[Any] = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _lowercase ):
for j in range(1 , _lowercase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase : Union[str, Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase : List[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase : Optional[int] = dp[i - 1][j]
else:
UpperCAmelCase : Any = 0
else:
UpperCAmelCase : str = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a : List[str] = """aab"""
a : Optional[int] = """c*a*b"""
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 338 | 0 |
'''simple docstring'''
import os
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Any = len(grid[0] )
UpperCAmelCase : List[str] = len(lowerCamelCase__ )
UpperCAmelCase : List[str] = 0
UpperCAmelCase : str = 0
UpperCAmelCase : int = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(lowerCamelCase__ ):
for j in range(n_rows - 3 ):
UpperCAmelCase : Optional[int] = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
UpperCAmelCase : Tuple = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
UpperCAmelCase : Dict = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
UpperCAmelCase : List[Any] = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
UpperCAmelCase : Optional[Any] = max(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
if max_product > largest:
UpperCAmelCase : str = max_product
return largest
def __lowerCamelCase ( ) -> List[str]:
UpperCAmelCase : Any = []
with open(os.path.dirname(lowerCamelCase__ ) + """/grid.txt""" ) as file:
for line in file:
grid.append(line.strip("""\n""" ).split(""" """ ) )
UpperCAmelCase : Optional[int] = [[int(lowerCamelCase__ ) for i in grid[j]] for j in range(len(lowerCamelCase__ ) )]
return largest_product(lowerCamelCase__ )
if __name__ == "__main__":
print(solution())
| 353 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : List[str] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def __lowerCamelCase ( _lowercase = 1_0_0 ) -> int:
UpperCAmelCase : int = 1
UpperCAmelCase : str = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase : Tuple = pre_numerator
UpperCAmelCase : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1
UpperCAmelCase : Union[str, Any] = cur_numerator
UpperCAmelCase : Optional[int] = e_cont * pre_numerator + temp
return sum_digits(_lowercase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 338 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a : Tuple = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a : Tuple = {
# fairseq:
"""wmt19-ru-en""": {"""length_penalty""": 1.1},
"""wmt19-en-ru""": {"""length_penalty""": 1.1_5},
"""wmt19-en-de""": {"""length_penalty""": 1.0},
"""wmt19-de-en""": {"""length_penalty""": 1.1},
# allenai:
"""wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
"""wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
"""wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
"""wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
"""wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}
# this remaps the different models to their organization names
a : int = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a : Tuple = """facebook"""
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a : Any = """allenai"""
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = dict((re.sub(R"""@@$""" , """""" , _lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(R"""$""" , """</w>""" , _lowerCAmelCase ), v) for k, v in d.items() )
UpperCAmelCase : Optional[Any] = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
UpperCAmelCase : Dict = d[k] # restore
return da
def __lowerCamelCase ( _lowercase , _lowercase ) -> Union[str, Any]:
assert os.path.exists(_lowerCAmelCase )
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
UpperCAmelCase : Optional[int] = basename(_lowerCAmelCase )
UpperCAmelCase : Optional[int] = dirname(_lowerCAmelCase )
UpperCAmelCase : Union[str, Any] = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
UpperCAmelCase : Union[str, Any] = cls.hub_models()
UpperCAmelCase : Dict = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
UpperCAmelCase : int = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
UpperCAmelCase : Optional[int] = hub_utils.from_pretrained(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , archive_map=_lowerCAmelCase , **_lowerCAmelCase )
UpperCAmelCase : List[str] = vars(chkpt["""args"""]["""model"""] )
UpperCAmelCase : Optional[int] = args["""source_lang"""]
UpperCAmelCase : str = args["""target_lang"""]
UpperCAmelCase : List[Any] = dirname(_lowerCAmelCase )
UpperCAmelCase : int = basename(_lowerCAmelCase )
# dicts
UpperCAmelCase : int = os.path.join(_lowerCAmelCase , F'''dict.{src_lang}.txt''' )
UpperCAmelCase : Any = os.path.join(_lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
UpperCAmelCase : int = Dictionary.load(_lowerCAmelCase )
UpperCAmelCase : str = rewrite_dict_keys(src_dict.indices )
UpperCAmelCase : int = len(_lowerCAmelCase )
UpperCAmelCase : Dict = os.path.join(_lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(_lowerCAmelCase , ensure_ascii=_lowerCAmelCase , indent=_lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
UpperCAmelCase : Optional[int] = True
for k in src_vocab.keys():
if not k.islower():
UpperCAmelCase : Any = False
break
UpperCAmelCase : List[Any] = Dictionary.load(_lowerCAmelCase )
UpperCAmelCase : Optional[int] = rewrite_dict_keys(tgt_dict.indices )
UpperCAmelCase : int = len(_lowerCAmelCase )
UpperCAmelCase : Any = os.path.join(_lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(_lowerCAmelCase , ensure_ascii=_lowerCAmelCase , indent=_lowerCAmelCase ) )
# merges_file (bpecodes)
UpperCAmelCase : Any = os.path.join(_lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
UpperCAmelCase : str = os.path.join(_lowerCAmelCase , _lowerCAmelCase )
if os.path.exists(_lowerCAmelCase ):
break
with open(_lowerCAmelCase , encoding="""utf-8""" ) as fin:
UpperCAmelCase : Tuple = fin.read()
UpperCAmelCase : int = re.sub(R""" \d+$""" , """""" , _lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(_lowerCAmelCase )
# model config
UpperCAmelCase : List[str] = os.path.join(_lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
UpperCAmelCase : Dict = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
UpperCAmelCase : Optional[int] = 5
UpperCAmelCase : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
UpperCAmelCase : Union[str, Any] = best_score_hparams[model_dir]["""length_penalty"""]
else:
UpperCAmelCase : Optional[int] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(_lowerCAmelCase , ensure_ascii=_lowerCAmelCase , indent=_lowerCAmelCase ) )
# tokenizer config
UpperCAmelCase : Dict = os.path.join(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : str = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1_0_2_4,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(_lowerCAmelCase , ensure_ascii=_lowerCAmelCase , indent=_lowerCAmelCase ) )
# model
UpperCAmelCase : Dict = chkpt["""models"""][0]
UpperCAmelCase : Tuple = model.state_dict()
# rename keys to start with 'model.'
UpperCAmelCase : List[str] = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
UpperCAmelCase : Any = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : int = FSMTConfig.from_pretrained(_lowerCAmelCase )
UpperCAmelCase : Optional[Any] = FSMTForConditionalGeneration(_lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
# save
UpperCAmelCase : Dict = os.path.join(_lowerCAmelCase , _lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(_lowerCAmelCase , _lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fsmt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
a : List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 354 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A=0.0_1 , A=1000 ) -> List[str]:
UpperCAmelCase : List[Any] = p_stop
UpperCAmelCase : Optional[int] = max_length
def __iter__( self ) -> Union[str, Any]:
UpperCAmelCase : Dict = 0
UpperCAmelCase : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase : Any = random.random() < self.p_stop
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self , A , A , A=False , A=True ) -> Union[str, Any]:
UpperCAmelCase : List[str] = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
UpperCAmelCase : List[str] = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def _lowercase( self ) -> Union[str, Any]:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : Tuple = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : int = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(A , A )
def _lowercase( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def _lowercase( self ) -> Any:
# Check the shards when the dataset is a round multiple of total batch size.
UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : str = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
UpperCAmelCase : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
UpperCAmelCase : Tuple = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def _lowercase( self ) -> List[Any]:
# Check the shards when the dataset is a round multiple of batch size.
UpperCAmelCase : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : int = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Dict = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Dict = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase : List[str] = [BatchSamplerShard(A , 2 , A , even_batches=A ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def _lowercase( self , A , A , A , A=False , A=2 , A=False ) -> Tuple:
random.seed(A )
UpperCAmelCase : Dict = list(A )
UpperCAmelCase : Any = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
UpperCAmelCase : Dict = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(A )
iterable_dataset_lists.append(list(A ) )
UpperCAmelCase : Optional[Any] = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
UpperCAmelCase : List[Any] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
UpperCAmelCase : List[Any] = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def _lowercase( self ) -> str:
UpperCAmelCase : Tuple = 42
UpperCAmelCase : List[Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
UpperCAmelCase : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Dict = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
UpperCAmelCase : Any = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase : Optional[Any] = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _lowercase( self ) -> Dict:
Accelerator()
UpperCAmelCase : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 338 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
a : Optional[Any] = 'tiny-wmt19-en-ru'
# Build
# borrowed from a test
a : Dict = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
a : Any = dict(zip(vocab, range(len(vocab))))
a : str = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']
with tempfile.TemporaryDirectory() as tmpdirname:
a : Dict = Path(tmpdirname)
a : Any = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
a : List[str] = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
a : Tuple = build_dir / VOCAB_FILES_NAMES['merges_file']
with open(src_vocab_file, """w""") as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, """w""") as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, """w""") as fp:
fp.write("""\n""".join(merges))
a : Dict = FSMTTokenizer(
langs=["""en""", """ru"""],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
a : Any = FSMTConfig(
langs=["""ru""", """en"""],
src_vocab_size=1_0_0_0,
tgt_vocab_size=1_0_0_0,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
a : Optional[int] = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
a : Any = tokenizer(["""Making tiny model"""], return_tensors="""pt""")
a : Union[str, Any] = tiny_model(**batch)
print("""test output:""", len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
UpperCAmelCase : Dict = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
UpperCAmelCase : int = [1_4_4, 1_9_2, 2_4_0]
UpperCAmelCase : int = [1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
elif "mobilevit_xs" in mobilevit_name:
UpperCAmelCase : List[Any] = [9_6, 1_2_0, 1_4_4]
UpperCAmelCase : Dict = [1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
elif "mobilevit_xxs" in mobilevit_name:
UpperCAmelCase : Tuple = [6_4, 8_0, 9_6]
UpperCAmelCase : List[Any] = [1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
UpperCAmelCase : Optional[int] = 0.05
UpperCAmelCase : List[str] = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
UpperCAmelCase : List[str] = 5_1_2
UpperCAmelCase : Union[str, Any] = 1_6
UpperCAmelCase : List[str] = 2_1
UpperCAmelCase : Optional[int] = 'pascal-voc-id2label.json'
else:
UpperCAmelCase : Tuple = 1_0_0_0
UpperCAmelCase : str = 'imagenet-1k-id2label.json'
UpperCAmelCase : str = 'huggingface/label-files'
UpperCAmelCase : Optional[int] = json.load(open(hf_hub_download(_lowercase , _lowercase , repo_type="""dataset""" ) , """r""" ) )
UpperCAmelCase : Union[str, Any] = {int(_lowercase ): v for k, v in idalabel.items()}
UpperCAmelCase : List[Any] = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def __lowerCamelCase ( _lowercase , _lowercase=False ) -> int:
for i in range(1 , 6 ):
if F'''layer_{i}.''' in name:
UpperCAmelCase : str = name.replace(F'''layer_{i}.''' , F'''encoder.layer.{i - 1}.''' )
if "conv_1." in name:
UpperCAmelCase : Tuple = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
UpperCAmelCase : Optional[int] = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
UpperCAmelCase : int = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
UpperCAmelCase : List[Any] = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
UpperCAmelCase : Optional[Any] = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
UpperCAmelCase : Dict = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
UpperCAmelCase : Any = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
UpperCAmelCase : Optional[Any] = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
UpperCAmelCase : Dict = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F'''.{i}.{j}.''' in name:
UpperCAmelCase : Tuple = name.replace(F'''.{i}.{j}.''' , F'''.{i}.layer.{j}.''' )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F'''.{i}.{j}.''' in name:
UpperCAmelCase : List[str] = name.replace(F'''.{i}.{j}.''' , F'''.{i}.''' )
if "expand_1x1" in name:
UpperCAmelCase : Dict = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
UpperCAmelCase : Union[str, Any] = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
UpperCAmelCase : Optional[Any] = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F'''.global_rep.{i}.weight''' in name:
UpperCAmelCase : List[Any] = name.replace(F'''.global_rep.{i}.weight''' , """.layernorm.weight""" )
if F'''.global_rep.{i}.bias''' in name:
UpperCAmelCase : Dict = name.replace(F'''.global_rep.{i}.bias''' , """.layernorm.bias""" )
if ".global_rep." in name:
UpperCAmelCase : Tuple = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
UpperCAmelCase : Optional[Any] = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
UpperCAmelCase : int = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
UpperCAmelCase : Optional[Any] = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
UpperCAmelCase : Tuple = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
UpperCAmelCase : int = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
UpperCAmelCase : Optional[Any] = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
UpperCAmelCase : Tuple = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
UpperCAmelCase : Any = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
UpperCAmelCase : Union[str, Any] = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
UpperCAmelCase : str = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
UpperCAmelCase : Optional[int] = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
UpperCAmelCase : List[str] = 'mobilevit.' + name
return name
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=False ) -> Optional[Any]:
if base_model:
UpperCAmelCase : str = ''
else:
UpperCAmelCase : List[Any] = 'mobilevit.'
for key in orig_state_dict.copy().keys():
UpperCAmelCase : int = orig_state_dict.pop(_lowercase )
if key[:8] == "encoder.":
UpperCAmelCase : Dict = key[8:]
if "qkv" in key:
UpperCAmelCase : Tuple = key.split(""".""" )
UpperCAmelCase : List[str] = int(key_split[0][6:] ) - 1
UpperCAmelCase : Any = int(key_split[3] )
UpperCAmelCase : Optional[int] = model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}''' )
UpperCAmelCase : int = layer.transformer.layer[transformer_num].attention.attention.all_head_size
UpperCAmelCase : Dict = (
F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
)
if "weight" in key:
UpperCAmelCase : Union[str, Any] = val[:dim, :]
UpperCAmelCase : List[Any] = val[dim : dim * 2, :]
UpperCAmelCase : str = val[-dim:, :]
else:
UpperCAmelCase : Dict = val[:dim]
UpperCAmelCase : List[Any] = val[dim : dim * 2]
UpperCAmelCase : List[Any] = val[-dim:]
else:
UpperCAmelCase : int = val
return orig_state_dict
def __lowerCamelCase ( ) -> Optional[Any]:
UpperCAmelCase : List[str] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
UpperCAmelCase : int = Image.open(requests.get(_lowercase , stream=_lowercase ).raw )
return im
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=False ) -> Dict:
UpperCAmelCase : Optional[int] = get_mobilevit_config(_lowercase )
# load original state_dict
UpperCAmelCase : Union[str, Any] = torch.load(_lowercase , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
UpperCAmelCase : Union[str, Any] = MobileViTForSemanticSegmentation(_lowercase ).eval()
else:
UpperCAmelCase : Tuple = MobileViTForImageClassification(_lowercase ).eval()
UpperCAmelCase : int = convert_state_dict(_lowercase , _lowercase )
model.load_state_dict(_lowercase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Optional[int] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 3_2 )
UpperCAmelCase : int = image_processor(images=prepare_img() , return_tensors="""pt""" )
UpperCAmelCase : List[str] = model(**_lowercase )
UpperCAmelCase : Any = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 2_1, 3_2, 3_2)
if mobilevit_name == "deeplabv3_mobilevit_s":
UpperCAmelCase : Tuple = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
UpperCAmelCase : Optional[Any] = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
UpperCAmelCase : Union[str, Any] = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8624, -9.5964], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
] )
else:
raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}''' )
assert torch.allclose(logits[0, :3, :3, :3] , _lowercase , atol=1e-4 )
else:
assert logits.shape == (1, 1_0_0_0)
if mobilevit_name == "mobilevit_s":
UpperCAmelCase : Any = torch.tensor([-0.9866, 0.2392, -1.1241] )
elif mobilevit_name == "mobilevit_xs":
UpperCAmelCase : Optional[Any] = torch.tensor([-2.4761, -0.9399, -1.9587] )
elif mobilevit_name == "mobilevit_xxs":
UpperCAmelCase : List[str] = torch.tensor([-1.9364, -1.2327, -0.4653] )
else:
raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}''' )
assert torch.allclose(logits[0, :3] , _lowercase , atol=1e-4 )
Path(_lowercase ).mkdir(exist_ok=_lowercase )
print(F'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(_lowercase )
if push_to_hub:
UpperCAmelCase : List[str] = {
'mobilevit_s': 'mobilevit-small',
'mobilevit_xs': 'mobilevit-x-small',
'mobilevit_xxs': 'mobilevit-xx-small',
'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
}
print("""Pushing to the hub...""" )
UpperCAmelCase : Optional[int] = model_mapping[mobilevit_name]
image_processor.push_to_hub(_lowercase , organization="""apple""" )
model.push_to_hub(_lowercase , organization="""apple""" )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\',"""
""" \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
a : Tuple = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 356 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( _lowercase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_lowercase , _lowercase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> Dict:
return "".join([hex(_lowercase )[2:].zfill(2 ).upper() for byte in list(_lowercase )] )
def __lowerCamelCase ( _lowercase ) -> Dict:
if (len(_lowercase ) % 2) != 0:
raise ValueError(
"""Base16 encoded data is invalid:
Data does not have an even number of hex digits.""" )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(_lowercase ) <= set("""0123456789ABCDEF""" ):
raise ValueError(
"""Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.""" )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 1_6 ) for i in range(0 , len(_lowercase ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 357 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a : Optional[int] = 1_0
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
for i in range(_lowercase , _lowercase ):
if array[i] == target:
return i
return -1
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
UpperCAmelCase : Tuple = 0
UpperCAmelCase : List[str] = len(_lowercase )
while left <= right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : Union[str, Any] = (left + right) // 3 + 1
UpperCAmelCase : Union[str, Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCAmelCase : Any = one_third - 1
elif array[two_third] < target:
UpperCAmelCase : Tuple = two_third + 1
else:
UpperCAmelCase : int = one_third + 1
UpperCAmelCase : List[Any] = two_third - 1
else:
return -1
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCAmelCase : str = (left + right) // 3 + 1
UpperCAmelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowercase , one_third - 1 , _lowercase , _lowercase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowercase , _lowercase , _lowercase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowercase , _lowercase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Any = input("""Enter numbers separated by comma:\n""").strip()
a : Any = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
a : Tuple = int(input("""Enter the number to be found in the list:\n""").strip())
a : Union[str, Any] = ite_ternary_search(collection, target)
a : Optional[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 338 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
a : Optional[int] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
a : Dict = 2_5_0_0_0_4
a : Any = 2_5_0_0_2_0
@require_sentencepiece
@require_tokenizers
class UpperCamelCase_ ( lowerCamelCase__ , unittest.TestCase ):
lowercase = MBartTokenizer
lowercase = MBartTokenizerFast
lowercase = True
lowercase = True
def _lowercase( self ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase : List[str] = MBartTokenizer(lowercase__ , keep_accents=lowercase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Union[str, Any] = MBartTokenizer(lowercase__ , keep_accents=lowercase__ )
UpperCAmelCase : Dict = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowercase__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
UpperCAmelCase : Optional[Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
lowercase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
UpperCAmelCase : Any = tokenizer.convert_tokens_to_ids(lowercase__ )
self.assertListEqual(
lowercase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
UpperCAmelCase : Dict = tokenizer.convert_ids_to_tokens(lowercase__ )
self.assertListEqual(
lowercase__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def _lowercase( self ) -> Tuple:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
UpperCAmelCase : Any = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
UpperCAmelCase : Tuple = self.rust_tokenizer_class.from_pretrained(lowercase__ , **lowercase__ )
UpperCAmelCase : Tuple = self.tokenizer_class.from_pretrained(lowercase__ , **lowercase__ )
UpperCAmelCase : List[Any] = tempfile.mkdtemp()
UpperCAmelCase : Optional[Any] = tokenizer_r.save_pretrained(lowercase__ )
UpperCAmelCase : Any = tokenizer_p.save_pretrained(lowercase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
UpperCAmelCase : Dict = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(lowercase__ , lowercase__ )
# Checks everything loads correctly in the same way
UpperCAmelCase : Dict = tokenizer_r.from_pretrained(lowercase__ )
UpperCAmelCase : Union[str, Any] = tokenizer_p.from_pretrained(lowercase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase__ , lowercase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase__ )
# Save tokenizer rust, legacy_format=True
UpperCAmelCase : Union[str, Any] = tempfile.mkdtemp()
UpperCAmelCase : int = tokenizer_r.save_pretrained(lowercase__ , legacy_format=lowercase__ )
UpperCAmelCase : Optional[Any] = tokenizer_p.save_pretrained(lowercase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase__ , lowercase__ )
# Checks everything loads correctly in the same way
UpperCAmelCase : Dict = tokenizer_r.from_pretrained(lowercase__ )
UpperCAmelCase : Tuple = tokenizer_p.from_pretrained(lowercase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase__ , lowercase__ ) )
shutil.rmtree(lowercase__ )
# Save tokenizer rust, legacy_format=False
UpperCAmelCase : Union[str, Any] = tempfile.mkdtemp()
UpperCAmelCase : Optional[Any] = tokenizer_r.save_pretrained(lowercase__ , legacy_format=lowercase__ )
UpperCAmelCase : str = tokenizer_p.save_pretrained(lowercase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(lowercase__ )
UpperCAmelCase : Optional[int] = tokenizer_p.from_pretrained(lowercase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase__ , lowercase__ ) )
shutil.rmtree(lowercase__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = 'facebook/mbart-large-en-ro'
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
lowercase = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
@classmethod
def _lowercase( cls ) -> List[str]:
UpperCAmelCase : Any = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""en_XX""" , tgt_lang="""ro_RO""" )
UpperCAmelCase : int = 1
return cls
def _lowercase( self ) -> int:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""] , 250020 )
def _lowercase( self ) -> str:
UpperCAmelCase : Union[str, Any] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowercase__ )
def _lowercase( self ) -> Union[str, Any]:
self.assertIn(lowercase__ , self.tokenizer.all_special_ids )
UpperCAmelCase : Union[str, Any] = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
UpperCAmelCase : int = self.tokenizer.decode(lowercase__ , skip_special_tokens=lowercase__ )
UpperCAmelCase : Tuple = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowercase__ )
self.assertEqual(lowercase__ , lowercase__ )
self.assertNotIn(self.tokenizer.eos_token , lowercase__ )
def _lowercase( self ) -> int:
UpperCAmelCase : int = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , lowercase__ )
UpperCAmelCase : int = 10
UpperCAmelCase : int = self.tokenizer(lowercase__ , max_length=lowercase__ , truncation=lowercase__ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , lowercase__ )
self.assertEqual(len(lowercase__ ) , lowercase__ )
def _lowercase( self ) -> Any:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [250026, 250001] )
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
UpperCAmelCase : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowercase__ )
UpperCAmelCase : Any = MBartTokenizer.from_pretrained(lowercase__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowercase__ )
@require_torch
def _lowercase( self ) -> str:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowercase__ , return_tensors="""pt""" )
UpperCAmelCase : Any = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def _lowercase( self ) -> Tuple:
UpperCAmelCase : Optional[int] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowercase__ , truncation=lowercase__ , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
UpperCAmelCase : Tuple = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(lowercase__ , lowercase__ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
UpperCAmelCase : Any = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowercase__ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def _lowercase( self ) -> List[str]:
UpperCAmelCase : int = self.tokenizer(self.src_text , padding=lowercase__ , truncation=lowercase__ , max_length=3 , return_tensors="""pt""" )
UpperCAmelCase : Any = self.tokenizer(
text_target=self.tgt_text , padding=lowercase__ , truncation=lowercase__ , max_length=10 , return_tensors="""pt""" )
UpperCAmelCase : Optional[Any] = targets["""input_ids"""]
UpperCAmelCase : Dict = shift_tokens_right(lowercase__ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _lowercase( self ) -> str:
UpperCAmelCase : List[str] = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""ar_AR""" )
self.assertEqual(
nested_simplify(lowercase__ ) , {
# A, test, EOS, en_XX
"""input_ids""": [[62, 3034, 2, 250004]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 250001,
} , )
| 358 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Optional[int] = 0
def __eq__( self , A ) -> Optional[Any]:
return self.position == cell.position
def _lowercase( self ) -> Tuple:
print(self.position )
class UpperCamelCase_ :
def __init__( self , A=(5, 5) ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = np.zeros(A )
UpperCAmelCase : int = world_size[0]
UpperCAmelCase : List[str] = world_size[1]
def _lowercase( self ) -> List[Any]:
print(self.w )
def _lowercase( self , A ) -> Dict:
UpperCAmelCase : Optional[Any] = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCAmelCase : List[Any] = cell.position[0]
UpperCAmelCase : Union[str, Any] = cell.position[1]
UpperCAmelCase : Optional[int] = []
for n in neughbour_cord:
UpperCAmelCase : Any = current_x + n[0]
UpperCAmelCase : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCAmelCase : str = Cell()
UpperCAmelCase : List[str] = (x, y)
UpperCAmelCase : Dict = cell
neighbours.append(A )
return neighbours
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> int:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
_open.append(_lowercase )
while _open:
UpperCAmelCase : Any = np.argmin([n.f for n in _open] )
UpperCAmelCase : Optional[int] = _open[min_f]
_closed.append(_open.pop(_lowercase ) )
if current == goal:
break
for n in world.get_neigbours(_lowercase ):
for c in _closed:
if c == n:
continue
UpperCAmelCase : List[str] = current.g + 1
UpperCAmelCase , UpperCAmelCase : List[str] = n.position
UpperCAmelCase , UpperCAmelCase : Dict = goal.position
UpperCAmelCase : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCAmelCase : Dict = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(_lowercase )
UpperCAmelCase : Dict = []
while current.parent is not None:
path.append(current.position )
UpperCAmelCase : Optional[int] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a : List[str] = Gridworld()
# Start position and goal
a : Optional[int] = Cell()
a : Optional[Any] = (0, 0)
a : Optional[Any] = Cell()
a : str = (4, 4)
print(F'''path from {start.position} to {goal.position}''')
a : List[Any] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a : Any = 1
print(world.w)
| 338 | 0 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
a : List[str] = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.',
'The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'
' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'
' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'
' body.',
'Amnesty International releases its annual report on the death penalty. The report catalogs the use of'
' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'
' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'
' punishment.',
]
a : List[str] = [
'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
' had informed his Lufthansa training school of an episode of severe depression, airline says .',
'Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'
' Israel and the United States opposed the move, which could open the door to war crimes investigations against'
' Israelis .',
'Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'
' death . Organization claims that governments around the world are using the threat of terrorism to advance'
' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'
' sentences up by 28% .',
]
def __lowerCamelCase ( ) -> Union[str, Any]:
UpperCAmelCase : List[str] = calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , bootstrap_aggregation=_UpperCAmelCase , rouge_keys=["""rouge2""", """rougeL"""] )
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase : Dict = calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , bootstrap_aggregation=_UpperCAmelCase , rouge_keys=["""rouge2"""] )
assert (
pd.DataFrame(no_aggregation["""rouge2"""] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["""rouge2"""] ).fmeasure.mean()
)
def __lowerCamelCase ( ) -> int:
UpperCAmelCase : Optional[Any] = 'rougeLsum'
UpperCAmelCase : Tuple = calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , newline_sep=_UpperCAmelCase , rouge_keys=[k] )[k]
UpperCAmelCase : Dict = calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , newline_sep=_UpperCAmelCase , rouge_keys=[k] )[k]
assert score > score_no_sep
def __lowerCamelCase ( ) -> Dict:
UpperCAmelCase : Any = ['rouge1', 'rouge2', 'rougeL']
UpperCAmelCase : int = calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , newline_sep=_UpperCAmelCase , rouge_keys=_UpperCAmelCase )
UpperCAmelCase : int = calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , newline_sep=_UpperCAmelCase , rouge_keys=_UpperCAmelCase )
assert score_sep == score_no_sep
def __lowerCamelCase ( ) -> List[Any]:
UpperCAmelCase : List[Any] = [
'Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.',
'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .',
]
UpperCAmelCase : int = [
'Margot Frank, died in 1945, a month earlier than previously thought.',
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'
' the final seconds on board Flight 9525.',
]
assert calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , newline_sep=_UpperCAmelCase ) == calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , newline_sep=_UpperCAmelCase )
def __lowerCamelCase ( ) -> Any:
UpperCAmelCase : Any = [
'" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '
]
UpperCAmelCase : Optional[int] = [
' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'
]
UpperCAmelCase : int = calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , rouge_keys=["""rougeLsum"""] , newline_sep=_UpperCAmelCase )['rougeLsum']
UpperCAmelCase : Union[str, Any] = calculate_rouge(_UpperCAmelCase , _UpperCAmelCase , rouge_keys=["""rougeLsum"""] )['rougeLsum']
assert new_score > prev_score
def __lowerCamelCase ( ) -> int:
UpperCAmelCase : Optional[Any] = Path("""examples/seq2seq/test_data/wmt_en_ro""" )
UpperCAmelCase : Optional[Any] = calculate_rouge_path(data_dir.joinpath("""test.source""" ) , data_dir.joinpath("""test.target""" ) )
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase : int = calculate_rouge_path(
data_dir.joinpath("""test.source""" ) , data_dir.joinpath("""test.target""" ) , bootstrap_aggregation=_UpperCAmelCase )
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
| 359 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Optional[int] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Dict = ["""NllbTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : int = ["""NllbTokenizerFast"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
a : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
# initialize config
if "resnet-50" in model_name:
UpperCAmelCase : Union[str, Any] = ResNetConfig.from_pretrained("""microsoft/resnet-50""" )
elif "resnet-101" in model_name:
UpperCAmelCase : Optional[Any] = ResNetConfig.from_pretrained("""microsoft/resnet-101""" )
else:
raise ValueError("""Model name should include either resnet50 or resnet101""" )
UpperCAmelCase : Optional[Any] = DetrConfig(use_timm_backbone=_lowercase , backbone_config=_lowercase )
# set label attributes
UpperCAmelCase : Optional[Any] = """panoptic""" in model_name
if is_panoptic:
UpperCAmelCase : Tuple = 2_5_0
else:
UpperCAmelCase : str = 9_1
UpperCAmelCase : List[Any] = """huggingface/label-files"""
UpperCAmelCase : Optional[Any] = """coco-detection-id2label.json"""
UpperCAmelCase : Optional[int] = json.load(open(hf_hub_download(_lowercase , _lowercase , repo_type="""dataset""" ) , """r""" ) )
UpperCAmelCase : Any = {int(_lowercase ): v for k, v in idalabel.items()}
UpperCAmelCase : Dict = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase : Optional[Any] = []
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""") )
rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""") )
rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""") )
rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""") )
rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
F'''encoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
F'''decoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
) )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
) )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
] )
return rename_keys
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> List[Any]:
UpperCAmelCase : Union[str, Any] = state_dict.pop(_lowercase )
UpperCAmelCase : Any = val
def __lowerCamelCase ( _lowercase , _lowercase=False ) -> Tuple:
UpperCAmelCase : Dict = """"""
if is_panoptic:
UpperCAmelCase : Any = """detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
UpperCAmelCase : List[Any] = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase : Tuple = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase : str = in_proj_weight[:2_5_6, :]
UpperCAmelCase : Optional[int] = in_proj_bias[:2_5_6]
UpperCAmelCase : Any = in_proj_weight[2_5_6:5_1_2, :]
UpperCAmelCase : int = in_proj_bias[2_5_6:5_1_2]
UpperCAmelCase : Dict = in_proj_weight[-2_5_6:, :]
UpperCAmelCase : List[Any] = in_proj_bias[-2_5_6:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
UpperCAmelCase : int = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase : List[str] = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase : int = in_proj_weight[:2_5_6, :]
UpperCAmelCase : Tuple = in_proj_bias[:2_5_6]
UpperCAmelCase : Any = in_proj_weight[2_5_6:5_1_2, :]
UpperCAmelCase : Tuple = in_proj_bias[2_5_6:5_1_2]
UpperCAmelCase : Dict = in_proj_weight[-2_5_6:, :]
UpperCAmelCase : Union[str, Any] = in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
UpperCAmelCase : Optional[int] = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCAmelCase : Optional[Any] = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
UpperCAmelCase : List[Any] = in_proj_weight_cross_attn[:2_5_6, :]
UpperCAmelCase : int = in_proj_bias_cross_attn[:2_5_6]
UpperCAmelCase : List[Any] = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
UpperCAmelCase : Tuple = in_proj_bias_cross_attn[2_5_6:5_1_2]
UpperCAmelCase : Optional[int] = in_proj_weight_cross_attn[-2_5_6:, :]
UpperCAmelCase : int = in_proj_bias_cross_attn[-2_5_6:]
def __lowerCamelCase ( ) -> Tuple:
UpperCAmelCase : Optional[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCAmelCase : List[str] = Image.open(requests.get(_lowercase , stream=_lowercase ).raw )
return im
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase=None , _lowercase=False ) -> List[Any]:
UpperCAmelCase , UpperCAmelCase : str = get_detr_config(_lowercase )
# load original model from torch hub
UpperCAmelCase : Any = {
"""detr-resnet-50""": """detr_resnet50""",
"""detr-resnet-101""": """detr_resnet101""",
}
logger.info(F'''Converting model {model_name}...''' )
UpperCAmelCase : int = torch.hub.load("""facebookresearch/detr""" , model_name_to_original_name[model_name] , pretrained=_lowercase ).eval()
UpperCAmelCase : Optional[int] = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(_lowercase ):
if is_panoptic:
UpperCAmelCase : Union[str, Any] = """detr.""" + src
rename_key(_lowercase , _lowercase , _lowercase )
# query, key and value matrices need special treatment
read_in_q_k_v(_lowercase , is_panoptic=_lowercase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCAmelCase : Tuple = """detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
UpperCAmelCase : Optional[int] = state_dict.pop(_lowercase )
UpperCAmelCase : List[Any] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
UpperCAmelCase : Optional[int] = state_dict.pop(_lowercase )
UpperCAmelCase : str = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
UpperCAmelCase : Optional[int] = state_dict.pop(_lowercase )
UpperCAmelCase : Dict = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
UpperCAmelCase : str = state_dict.pop(_lowercase )
UpperCAmelCase : Dict = val
# finally, create HuggingFace model and load state dict
UpperCAmelCase : List[Any] = DetrForSegmentation(_lowercase ) if is_panoptic else DetrForObjectDetection(_lowercase )
model.load_state_dict(_lowercase )
model.eval()
# verify our conversion on an image
UpperCAmelCase : Optional[int] = """coco_panoptic""" if is_panoptic else """coco_detection"""
UpperCAmelCase : Union[str, Any] = DetrImageProcessor(format=_lowercase )
UpperCAmelCase : List[str] = processor(images=prepare_img() , return_tensors="""pt""" )
UpperCAmelCase : List[Any] = encoding["""pixel_values"""]
UpperCAmelCase : Tuple = detr(_lowercase )
UpperCAmelCase : Optional[Any] = model(_lowercase )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1e-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
processor.save_pretrained(_lowercase )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("""Uploading PyTorch model and image processor to the hub...""" )
model.push_to_hub(F'''nielsr/{model_name}''' )
processor.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
a : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""detr-resnet-50""",
type=str,
choices=["""detr-resnet-50""", """detr-resnet-101"""],
help="""Name of the DETR model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
a : Optional[Any] = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 361 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class UpperCamelCase_ ( nn.Module ):
lowercase = 42
lowercase = jnp.floataa
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Union[str, Any] = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , A ) -> Any:
UpperCAmelCase : int = hidden_states.shape
UpperCAmelCase : str = jax.image.resize(
_a , shape=(batch, height * 2, width * 2, channels) , method="""nearest""" , )
UpperCAmelCase : Dict = self.conv(_a )
return hidden_states
class UpperCamelCase_ ( nn.Module ):
lowercase = 42
lowercase = jnp.floataa
def _lowercase( self ) -> str:
UpperCAmelCase : Optional[int] = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , A ) -> Dict:
UpperCAmelCase : int = self.conv(_a )
return hidden_states
class UpperCamelCase_ ( nn.Module ):
lowercase = 42
lowercase = None
lowercase = 0.0
lowercase = None
lowercase = jnp.floataa
def _lowercase( self ) -> str:
UpperCAmelCase : Any = self.in_channels if self.out_channels is None else self.out_channels
UpperCAmelCase : List[Any] = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
UpperCAmelCase : Any = nn.Conv(
_a , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
UpperCAmelCase : Union[str, Any] = nn.Dense(_a , dtype=self.dtype )
UpperCAmelCase : str = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
UpperCAmelCase : List[str] = nn.Dropout(self.dropout_prob )
UpperCAmelCase : List[str] = nn.Conv(
_a , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
UpperCAmelCase : Dict = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
UpperCAmelCase : Any = None
if use_nin_shortcut:
UpperCAmelCase : Union[str, Any] = nn.Conv(
_a , kernel_size=(1, 1) , strides=(1, 1) , padding="""VALID""" , dtype=self.dtype , )
def __call__( self , A , A , A=True ) -> List[Any]:
UpperCAmelCase : Optional[int] = hidden_states
UpperCAmelCase : Dict = self.norma(_a )
UpperCAmelCase : str = nn.swish(_a )
UpperCAmelCase : List[str] = self.conva(_a )
UpperCAmelCase : List[Any] = self.time_emb_proj(nn.swish(_a ) )
UpperCAmelCase : Any = jnp.expand_dims(jnp.expand_dims(_a , 1 ) , 1 )
UpperCAmelCase : Dict = hidden_states + temb
UpperCAmelCase : int = self.norma(_a )
UpperCAmelCase : List[Any] = nn.swish(_a )
UpperCAmelCase : Tuple = self.dropout(_a , _a )
UpperCAmelCase : Optional[int] = self.conva(_a )
if self.conv_shortcut is not None:
UpperCAmelCase : str = self.conv_shortcut(_a )
return hidden_states + residual
| 362 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="""my_dataset""" )} ),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"""train""": SplitInfo()} ),
] , )
def __lowerCamelCase ( _lowercase ) -> List[str]:
UpperCAmelCase : Optional[int] = split_dict._to_yaml_list()
assert len(_lowercase ) == len(_lowercase )
UpperCAmelCase : List[Any] = SplitDict._from_yaml_list(_lowercase )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCAmelCase : List[str] = None
# the split name of split_dict takes over the name of the split info object
UpperCAmelCase : int = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"""split_info""" , [SplitInfo(), SplitInfo(dataset_name=_lowercase ), SplitInfo(dataset_name="""my_dataset""" )] )
def __lowerCamelCase ( _lowercase ) -> List[str]:
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
UpperCAmelCase : Optional[Any] = asdict(SplitDict({"""train""": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 338 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : int = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
],
'processing_clipseg': ['CLIPSegProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[str] = [
'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPSegModel',
'CLIPSegPreTrainedModel',
'CLIPSegTextModel',
'CLIPSegVisionModel',
'CLIPSegForImageSegmentation',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 363 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 338 | 0 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCamelCase_ :
def __init__( self , A ) -> None:
UpperCAmelCase : str = data
# Initialize hash values
UpperCAmelCase : Optional[int] = [
0X6a09e667,
0Xbb67ae85,
0X3c6ef372,
0Xa54ff53a,
0X510e527f,
0X9b05688c,
0X1f83d9ab,
0X5be0cd19,
]
# Initialize round constants
UpperCAmelCase : Tuple = [
0X428a2f98,
0X71374491,
0Xb5c0fbcf,
0Xe9b5dba5,
0X3956c25b,
0X59f111f1,
0X923f82a4,
0Xab1c5ed5,
0Xd807aa98,
0X12835b01,
0X243185be,
0X550c7dc3,
0X72be5d74,
0X80deb1fe,
0X9bdc06a7,
0Xc19bf174,
0Xe49b69c1,
0Xefbe4786,
0X0fc19dc6,
0X240ca1cc,
0X2de92c6f,
0X4a7484aa,
0X5cb0a9dc,
0X76f988da,
0X983e5152,
0Xa831c66d,
0Xb00327c8,
0Xbf597fc7,
0Xc6e00bf3,
0Xd5a79147,
0X06ca6351,
0X14292967,
0X27b70a85,
0X2e1b2138,
0X4d2c6dfc,
0X53380d13,
0X650a7354,
0X766a0abb,
0X81c2c92e,
0X92722c85,
0Xa2bfe8a1,
0Xa81a664b,
0Xc24b8b70,
0Xc76c51a3,
0Xd192e819,
0Xd6990624,
0Xf40e3585,
0X106aa070,
0X19a4c116,
0X1e376c08,
0X2748774c,
0X34b0bcb5,
0X391c0cb3,
0X4ed8aa4a,
0X5b9cca4f,
0X682e6ff3,
0X748f82ee,
0X78a5636f,
0X84c87814,
0X8cc70208,
0X90befffa,
0Xa4506ceb,
0Xbef9a3f7,
0Xc67178f2,
]
UpperCAmelCase : str = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def _lowercase( A ) -> bytes:
UpperCAmelCase : List[Any] = b"\x80" + (b"\x00" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCAmelCase : Any = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def _lowercase( self ) -> None:
UpperCAmelCase : Optional[Any] = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCAmelCase : Union[str, Any] = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCAmelCase : Optional[Any] = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCAmelCase : List[Any] = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCAmelCase : Optional[int] = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCAmelCase : str = (
words[index - 16] + sa + words[index - 7] + sa
) % 0X100000000
# Compression
UpperCAmelCase : Optional[Any] = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCAmelCase : Optional[int] = (e & f) ^ ((~e & 0Xffffffff) & g)
UpperCAmelCase : Optional[Any] = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0X100000000
UpperCAmelCase : Optional[int] = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCAmelCase : Optional[int] = (a & b) ^ (a & c) ^ (b & c)
UpperCAmelCase : Dict = (sa + maj) % 0X100000000
UpperCAmelCase : List[Any] = (
g,
f,
e,
((d + tempa) % 0X100000000),
c,
b,
a,
((tempa + tempa) % 0X100000000),
)
UpperCAmelCase : List[Any] = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCAmelCase : int = [
((element + mutated_hash_values[index]) % 0X100000000)
for index, element in enumerate(self.hashes )
]
UpperCAmelCase : Tuple = "".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def _lowercase( self , A , A ) -> int:
return 0Xffffffff & (value << (32 - rotations)) | (value >> rotations)
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> None:
import hashlib
UpperCAmelCase : List[Any] = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def __lowerCamelCase ( ) -> None:
"""simple docstring"""
import doctest
doctest.testmod()
UpperCAmelCase : Dict = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCAmelCase : List[str] = parser.parse_args()
UpperCAmelCase : Optional[int] = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCAmelCase : List[str] = f.read()
else:
UpperCAmelCase : List[Any] = bytes(__snake_case , """utf-8""" )
print(SHAaaa(__snake_case ).hash )
if __name__ == "__main__":
main()
| 364 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'detr'
lowercase = ['past_key_values']
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , A=True , A=None , A=3 , A=100 , A=6 , A=2048 , A=8 , A=6 , A=2048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.0_2 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(A , A ):
UpperCAmelCase : Any = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : List[Any] = config_class.from_dict(A )
# set timm attributes to None
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = None, None, None
UpperCAmelCase : Dict = use_timm_backbone
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = num_channels
UpperCAmelCase : int = num_queries
UpperCAmelCase : List[str] = d_model
UpperCAmelCase : Tuple = encoder_ffn_dim
UpperCAmelCase : Optional[Any] = encoder_layers
UpperCAmelCase : Any = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : str = dropout
UpperCAmelCase : Tuple = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : List[Any] = init_std
UpperCAmelCase : str = init_xavier_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : int = decoder_layerdrop
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = auxiliary_loss
UpperCAmelCase : str = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[str] = use_pretrained_backbone
UpperCAmelCase : Optional[int] = dilation
# Hungarian matcher
UpperCAmelCase : Union[str, Any] = class_cost
UpperCAmelCase : Optional[Any] = bbox_cost
UpperCAmelCase : List[Any] = giou_cost
# Loss coefficients
UpperCAmelCase : int = mask_loss_coefficient
UpperCAmelCase : Optional[int] = dice_loss_coefficient
UpperCAmelCase : Dict = bbox_loss_coefficient
UpperCAmelCase : Any = giou_loss_coefficient
UpperCAmelCase : Any = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def _lowercase( self ) -> int:
return self.encoder_attention_heads
@property
def _lowercase( self ) -> int:
return self.d_model
@classmethod
def _lowercase( cls , A , **A ) -> Dict:
return cls(backbone_config=A , **A )
def _lowercase( self ) -> Dict[str, any]:
UpperCAmelCase : Any = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCAmelCase : Any = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-5
@property
def _lowercase( self ) -> int:
return 12
| 338 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
a = logging.get_logger(__name__) # pylint: disable=invalid-name
a = """
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
>>> repo = \"openai/shap-e-img2img\"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
>>> image = load_image(image_url).convert(\"RGB\")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
```
"""
@dataclass
class UpperCamelCase_ ( __snake_case ):
lowercase = 42
class UpperCamelCase_ ( __snake_case ):
def __init__( self , A , A , A , A , A , ) -> int:
super().__init__()
self.register_modules(
prior=lowerCamelCase_ , image_encoder=lowerCamelCase_ , image_processor=lowerCamelCase_ , scheduler=lowerCamelCase_ , renderer=lowerCamelCase_ , )
def _lowercase( self , A , A , A , A , A , A ) -> List[Any]:
if latents is None:
UpperCAmelCase : Dict = randn_tensor(lowerCamelCase_ , generator=lowerCamelCase_ , device=lowerCamelCase_ , dtype=lowerCamelCase_ )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
UpperCAmelCase : int = latents.to(lowerCamelCase_ )
UpperCAmelCase : Optional[int] = latents * scheduler.init_noise_sigma
return latents
def _lowercase( self , A=0 ) -> int:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
UpperCAmelCase : Optional[int] = torch.device(f'''cuda:{gpu_id}''' )
UpperCAmelCase : Dict = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase_ , lowerCamelCase_ )
@property
def _lowercase( self ) -> str:
if self.device != torch.device("""meta""" ) or not hasattr(self.image_encoder , """_hf_hook""" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(lowerCamelCase_ , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def _lowercase( self , A , A , A , A , ) -> Dict:
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and isinstance(image[0] , torch.Tensor ):
UpperCAmelCase : int = torch.cat(lowerCamelCase_ , axis=0 ) if image[0].ndim == 4 else torch.stack(lowerCamelCase_ , axis=0 )
if not isinstance(lowerCamelCase_ , torch.Tensor ):
UpperCAmelCase : Dict = self.image_processor(lowerCamelCase_ , return_tensors="""pt""" ).pixel_values[0].unsqueeze(0 )
UpperCAmelCase : Any = image.to(dtype=self.image_encoder.dtype , device=lowerCamelCase_ )
UpperCAmelCase : Dict = self.image_encoder(lowerCamelCase_ )["""last_hidden_state"""]
UpperCAmelCase : Union[str, Any] = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
UpperCAmelCase : Dict = image_embeds.repeat_interleave(lowerCamelCase_ , dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase : Optional[Any] = torch.zeros_like(lowerCamelCase_ )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase : Union[str, Any] = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(lowerCamelCase_ )
def __call__( self , A , A = 1 , A = 25 , A = None , A = None , A = 4.0 , A = 64 , A = "pil" , A = True , ) -> str:
if isinstance(lowerCamelCase_ , PIL.Image.Image ):
UpperCAmelCase : int = 1
elif isinstance(lowerCamelCase_ , torch.Tensor ):
UpperCAmelCase : str = image.shape[0]
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
UpperCAmelCase : Optional[int] = len(lowerCamelCase_ )
else:
raise ValueError(
f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowerCamelCase_ )}''' )
UpperCAmelCase : Union[str, Any] = self._execution_device
UpperCAmelCase : Optional[Any] = batch_size * num_images_per_prompt
UpperCAmelCase : Tuple = guidance_scale > 1.0
UpperCAmelCase : Union[str, Any] = self._encode_image(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# prior
self.scheduler.set_timesteps(lowerCamelCase_ , device=lowerCamelCase_ )
UpperCAmelCase : Dict = self.scheduler.timesteps
UpperCAmelCase : List[Any] = self.prior.config.num_embeddings
UpperCAmelCase : List[str] = self.prior.config.embedding_dim
UpperCAmelCase : Union[str, Any] = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
UpperCAmelCase : List[Any] = latents.reshape(latents.shape[0] , lowerCamelCase_ , lowerCamelCase_ )
for i, t in enumerate(self.progress_bar(lowerCamelCase_ ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase : Tuple = self.scheduler.scale_model_input(lowerCamelCase_ , lowerCamelCase_ )
UpperCAmelCase : Dict = self.prior(
lowerCamelCase_ , timestep=lowerCamelCase_ , proj_embedding=lowerCamelCase_ , ).predicted_image_embedding
# remove the variance
UpperCAmelCase : Optional[int] = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
UpperCAmelCase : Optional[int] = noise_pred.chunk(2 )
UpperCAmelCase : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
UpperCAmelCase : List[Any] = self.scheduler.step(
lowerCamelCase_ , timestep=lowerCamelCase_ , sample=lowerCamelCase_ , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=lowerCamelCase_ )
UpperCAmelCase : Optional[int] = []
for i, latent in enumerate(lowerCamelCase_ ):
print()
UpperCAmelCase : List[Any] = self.renderer.decode(
latent[None, :] , lowerCamelCase_ , size=lowerCamelCase_ , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(lowerCamelCase_ )
UpperCAmelCase : Union[str, Any] = torch.stack(lowerCamelCase_ )
if output_type not in ["np", "pil"]:
raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )
UpperCAmelCase : List[Any] = images.cpu().numpy()
if output_type == "pil":
UpperCAmelCase : Optional[Any] = [self.numpy_to_pil(lowerCamelCase_ ) for image in images]
# Offload last model to CPU
if hasattr(self , """final_offload_hook""" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=lowerCamelCase_ )
| 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[str] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[Any] = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : str = len(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase : Dict = len(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase : Dict = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
UpperCAmelCase : int = True
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
UpperCAmelCase : int = True
if a[i].islower():
UpperCAmelCase : Union[str, Any] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Dict = torch.load(_lowercase , map_location="""cpu""" )
if "model" in sd.keys():
UpperCAmelCase : Any = torch.load(_lowercase , map_location="""cpu""" )["""model"""]
# pop unnecessary weights
UpperCAmelCase : Union[str, Any] = [
"""decoder.version""",
"""decoder.output_projection.weight""",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowercase )
UpperCAmelCase : Tuple = {
"""decoder.project_in_dim.weight""": """decoder.project_in.weight""",
"""decoder.project_out_dim.weight""": """decoder.project_out.weight""",
"""decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
UpperCAmelCase : List[Any] = sd.pop(_lowercase )
UpperCAmelCase : Tuple = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
UpperCAmelCase : List[str] = sd[key]
# We split QKV in separate Q,K,V
UpperCAmelCase : Dict = key.replace(""".qkv_proj.""" , """.q_proj.""" )
UpperCAmelCase : Tuple = key.replace(""".qkv_proj.""" , """.k_proj.""" )
UpperCAmelCase : int = key.replace(""".qkv_proj.""" , """.v_proj.""" )
UpperCAmelCase : Dict = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict = torch.split(_lowercase , depth // 3 , dim=0 )
UpperCAmelCase : Tuple = q
UpperCAmelCase : Tuple = k
UpperCAmelCase : Any = v
del sd[key]
return sd
@torch.no_grad()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
UpperCAmelCase : Tuple = load_checkpoint(_lowercase )
if config is not None:
UpperCAmelCase : Dict = OPTConfig.from_pretrained(_lowercase )
else:
UpperCAmelCase : int = OPTConfig()
UpperCAmelCase : List[Any] = OPTModel(_lowercase ).half().eval()
model.load_state_dict(_lowercase )
# Check results
Path(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
a : Union[str, Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 338 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __lowerCamelCase ( ) -> List[str]:
UpperCAmelCase : str = {
"""repo_name""": ["""test_repo1""", """test_repo2""", """test_repo3"""],
"""path""": ["""test_1.py""", """test_2.py""", """unit_test.py"""],
"""content""": ["""a """ * 2_0, """a """ * 3_0, """b """ * 7],
}
UpperCAmelCase : Any = Dataset.from_dict(SCREAMING_SNAKE_CASE__ )
return dataset
class UpperCamelCase_ ( lowerCamelCase_ ):
def _lowercase( self ) -> str:
UpperCAmelCase : Dict = get_dataset()
UpperCAmelCase : Any = make_duplicate_clusters(_UpperCAmelCase , 0.8_5 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def _lowercase( self ) -> str:
UpperCAmelCase : List[Any] = get_dataset()
UpperCAmelCase , UpperCAmelCase : List[str] = deduplicate_dataset(_UpperCAmelCase )
self.assertEqual(len(_UpperCAmelCase ) , 2 )
print(_UpperCAmelCase )
self.assertEqual(duplicate_clusters[0][0]["""copies"""] , 2 )
self.assertEqual(duplicate_clusters[0][0]["""is_extreme"""] , _UpperCAmelCase )
| 367 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : str = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'levit'
def __init__( self , A=224 , A=3 , A=3 , A=2 , A=1 , A=16 , A=[128, 256, 384] , A=[4, 8, 12] , A=[4, 4, 4] , A=[16, 16, 16] , A=0 , A=[2, 2, 2] , A=[2, 2, 2] , A=0.0_2 , **A , ) -> int:
super().__init__(**A )
UpperCAmelCase : Any = image_size
UpperCAmelCase : Optional[int] = num_channels
UpperCAmelCase : Tuple = kernel_size
UpperCAmelCase : Optional[int] = stride
UpperCAmelCase : Dict = padding
UpperCAmelCase : List[Any] = hidden_sizes
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = depths
UpperCAmelCase : Any = key_dim
UpperCAmelCase : str = drop_path_rate
UpperCAmelCase : List[Any] = patch_size
UpperCAmelCase : str = attention_ratio
UpperCAmelCase : Optional[Any] = mlp_ratio
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : int = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = version.parse('1.11' )
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase( self ) -> float:
return 1e-4
| 338 | 0 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class UpperCamelCase_ :
def __init__( self , A , A=13 , A=7 , A=True , A=True , A=False , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.0_2 , A=3 , A=4 , A=None , ) -> Optional[Any]:
UpperCAmelCase : Tuple = parent
UpperCAmelCase : Optional[Any] = batch_size
UpperCAmelCase : Optional[Any] = seq_length
UpperCAmelCase : Any = is_training
UpperCAmelCase : Any = use_input_mask
UpperCAmelCase : Dict = use_token_type_ids
UpperCAmelCase : Dict = use_labels
UpperCAmelCase : Tuple = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : Dict = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Any = attention_probs_dropout_prob
UpperCAmelCase : int = max_position_embeddings
UpperCAmelCase : Union[str, Any] = type_vocab_size
UpperCAmelCase : int = type_sequence_label_size
UpperCAmelCase : List[Any] = initializer_range
UpperCAmelCase : Union[str, Any] = num_labels
UpperCAmelCase : Tuple = num_choices
UpperCAmelCase : str = scope
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : Tuple = None
if self.use_input_mask:
UpperCAmelCase : int = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : List[str] = None
if self.use_token_type_ids:
UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase : Tuple = None
UpperCAmelCase : int = None
UpperCAmelCase : List[Any] = None
if self.use_labels:
UpperCAmelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase : Dict = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase : Union[str, Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase( self ) -> List[str]:
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCamelCase , initializer_range=self.initializer_range , use_stable_embedding=__lowerCamelCase , )
def _lowercase( self , A , A , A , A , A , A , A ) -> List[Any]:
UpperCAmelCase : Optional[Any] = OpenLlamaModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCAmelCase : List[Any] = model(__lowerCamelCase , attention_mask=__lowerCamelCase )
UpperCAmelCase : List[Any] = model(__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase( self , A , A , A , A , A , A , A , A , A , ) -> List[Any]:
UpperCAmelCase : Union[str, Any] = True
UpperCAmelCase : Optional[int] = OpenLlamaModel(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCAmelCase : Any = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , encoder_attention_mask=__lowerCamelCase , )
UpperCAmelCase : Any = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , )
UpperCAmelCase : Union[str, Any] = model(__lowerCamelCase , attention_mask=__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase( self , A , A , A , A , A , A , A , A , A , ) -> Optional[int]:
UpperCAmelCase : Any = OpenLlamaForCausalLM(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCAmelCase : List[Any] = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase( self , A , A , A , A , A , A , A , A , A , ) -> int:
UpperCAmelCase : Optional[int] = True
UpperCAmelCase : Optional[Any] = True
UpperCAmelCase : Dict = OpenLlamaForCausalLM(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
# first forward pass
UpperCAmelCase : Tuple = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , encoder_attention_mask=__lowerCamelCase , use_cache=__lowerCamelCase , )
UpperCAmelCase : List[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCAmelCase : List[str] = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase : List[Any] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCAmelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCAmelCase : List[str] = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCAmelCase : Dict = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , encoder_attention_mask=__lowerCamelCase , output_hidden_states=__lowerCamelCase , )["""hidden_states"""][0]
UpperCAmelCase : Optional[int] = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , encoder_attention_mask=__lowerCamelCase , past_key_values=__lowerCamelCase , output_hidden_states=__lowerCamelCase , )["""hidden_states"""][0]
# select random slice
UpperCAmelCase : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCAmelCase : List[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCAmelCase : Any = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-3 ) )
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
UpperCAmelCase
) : Union[str, Any] = config_and_inputs
UpperCAmelCase : Any = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( _a , _a , _a , unittest.TestCase ):
lowercase = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
lowercase = (OpenLlamaForCausalLM,) if is_torch_available() else ()
lowercase = (
{
"""feature-extraction""": OpenLlamaModel,
"""text-classification""": OpenLlamaForSequenceClassification,
"""text-generation""": OpenLlamaForCausalLM,
"""zero-shot""": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
def _lowercase( self ) -> List[str]:
UpperCAmelCase : Union[str, Any] = OpenLlamaModelTester(self )
UpperCAmelCase : Union[str, Any] = ConfigTester(self , config_class=__lowerCamelCase , hidden_size=37 )
def _lowercase( self ) -> int:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase : Any = type
self.model_tester.create_and_check_model(*__lowerCamelCase )
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Dict = 3
UpperCAmelCase : str = input_dict["""input_ids"""]
UpperCAmelCase : Dict = input_ids.ne(1 ).to(__lowerCamelCase )
UpperCAmelCase : Tuple = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCAmelCase : int = OpenLlamaForSequenceClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCAmelCase : int = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _lowercase( self ) -> Any:
UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Dict = 3
UpperCAmelCase : int = """single_label_classification"""
UpperCAmelCase : List[Any] = input_dict["""input_ids"""]
UpperCAmelCase : Optional[Any] = input_ids.ne(1 ).to(__lowerCamelCase )
UpperCAmelCase : List[str] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCAmelCase : Dict = OpenLlamaForSequenceClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCAmelCase : Any = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : int = 3
UpperCAmelCase : Union[str, Any] = """multi_label_classification"""
UpperCAmelCase : Optional[Any] = input_dict["""input_ids"""]
UpperCAmelCase : List[Any] = input_ids.ne(1 ).to(__lowerCamelCase )
UpperCAmelCase : Optional[Any] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCAmelCase : int = OpenLlamaForSequenceClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
UpperCAmelCase : List[Any] = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test""" )
def _lowercase( self ) -> List[Any]:
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def _lowercase( self , A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[Any] = ids_tensor([1, 10] , config.vocab_size )
UpperCAmelCase : Union[str, Any] = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
UpperCAmelCase : Optional[Any] = OpenLlamaModel(__lowerCamelCase )
original_model.to(__lowerCamelCase )
original_model.eval()
UpperCAmelCase : Tuple = original_model(__lowerCamelCase ).last_hidden_state
UpperCAmelCase : Optional[Any] = original_model(__lowerCamelCase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
UpperCAmelCase : Optional[int] = {"""type""": scaling_type, """factor""": 1_0.0}
UpperCAmelCase : Tuple = OpenLlamaModel(__lowerCamelCase )
scaled_model.to(__lowerCamelCase )
scaled_model.eval()
UpperCAmelCase : Optional[Any] = scaled_model(__lowerCamelCase ).last_hidden_state
UpperCAmelCase : Any = scaled_model(__lowerCamelCase ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-5 ) )
| 368 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : List[str] = """Hello, World!"""
a : List[Any] = """en_XX"""
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Dict:
UpperCAmelCase : Dict = Path("""data_bin""" )
UpperCAmelCase : Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(_lowercase ).parent ) , checkpoint_file=Path(_lowercase ).name , _name="""xmod_base""" , arch="""xmod_base""" , task="""multilingual_masked_lm""" , data_name_or_path=str(_lowercase ) , bpe="""sentencepiece""" , sentencepiece_model=str(Path(_lowercase ).parent / """sentencepiece.bpe.model""" ) , src_dict=str(data_dir / """dict.txt""" ) , )
xmod.eval() # disable dropout
print(_lowercase )
UpperCAmelCase : List[str] = xmod.model.encoder.sentence_encoder
UpperCAmelCase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , """bottleneck""" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" , _lowercase )
UpperCAmelCase : str = XmodForSequenceClassification(_lowercase ) if classification_head else XmodForMaskedLM(_lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : int = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : Union[str, Any] = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : List[str] = model.roberta.encoder.layer[i]
UpperCAmelCase : Optional[Any] = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : Optional[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
UpperCAmelCase : List[Any] = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Any = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Optional[int] = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : List[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : Optional[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
UpperCAmelCase : Any = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[str] = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : int = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : Tuple = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
UpperCAmelCase : List[str] = xmod_layer.fca.weight
UpperCAmelCase : str = xmod_layer.fca.bias
# output
UpperCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
UpperCAmelCase : Dict = xmod_layer.fca.weight
UpperCAmelCase : Dict = xmod_layer.fca.bias
UpperCAmelCase : Any = xmod_layer.final_layer_norm.weight
UpperCAmelCase : Union[str, Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : str = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : List[str] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : List[Any] = bert_output.adapter_modules[lang_code]
UpperCAmelCase : Dict = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any = from_adapter.fca.weight
UpperCAmelCase : int = from_adapter.fca.bias
UpperCAmelCase : Dict = from_adapter.fca.weight
UpperCAmelCase : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Tuple = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].dense.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].dense.bias
UpperCAmelCase : str = xmod.model.classification_heads["""mnli"""].out_proj.weight
UpperCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
UpperCAmelCase : Dict = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : str = xmod.model.encoder.lm_head.weight
UpperCAmelCase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : Any = xmod.encode(_lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(_lowercase )
UpperCAmelCase : Optional[int] = model(_lowercase )[0]
if classification_head:
UpperCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowercase ) )
else:
UpperCAmelCase : Optional[Any] = xmod.model(_lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Dict = torch.allclose(_lowercase , _lowercase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(_lowercase ).mkdir(parents=_lowercase , exist_ok=_lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
a : List[str] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 338 | 0 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
a : Dict = data_utils.TransfoXLTokenizer
a : str = data_utils.TransfoXLCorpus
a : List[str] = data_utils
a : Optional[Any] = data_utils
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> Optional[Any]:
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(a__ , """rb""" ) as fp:
UpperCAmelCase : Union[str, Any] = pickle.load(a__ , encoding="""latin1""" )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
UpperCAmelCase : List[Any] = pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
print(F'''Save vocabulary to {pytorch_vocab_dump_path}''' )
UpperCAmelCase : Optional[int] = corpus.vocab.__dict__
torch.save(a__ , a__ )
UpperCAmelCase : str = corpus.__dict__
corpus_dict_no_vocab.pop("""vocab""" , a__ )
UpperCAmelCase : int = pytorch_dump_folder_path + """/""" + CORPUS_NAME
print(F'''Save dataset to {pytorch_dataset_dump_path}''' )
torch.save(a__ , a__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
UpperCAmelCase : Any = os.path.abspath(a__ )
UpperCAmelCase : Dict = os.path.abspath(a__ )
print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''' )
# Initialise PyTorch model
if transfo_xl_config_file == "":
UpperCAmelCase : Dict = TransfoXLConfig()
else:
UpperCAmelCase : List[Any] = TransfoXLConfig.from_json_file(a__ )
print(F'''Building PyTorch model from configuration: {config}''' )
UpperCAmelCase : int = TransfoXLLMHeadModel(a__ )
UpperCAmelCase : Any = load_tf_weights_in_transfo_xl(a__ , a__ , a__ )
# Save pytorch-model
UpperCAmelCase : List[Any] = os.path.join(a__ , a__ )
UpperCAmelCase : Optional[int] = os.path.join(a__ , a__ )
print(F'''Save PyTorch model to {os.path.abspath(a__ )}''' )
torch.save(model.state_dict() , a__ )
print(F'''Save configuration file to {os.path.abspath(a__ )}''' )
with open(a__ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
a : Dict = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
)
parser.add_argument(
"""--tf_checkpoint_path""",
default="""""",
type=str,
help="""An optional path to a TensorFlow checkpoint path to be converted.""",
)
parser.add_argument(
"""--transfo_xl_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained BERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--transfo_xl_dataset_file""",
default="""""",
type=str,
help="""An optional dataset file to be converted in a vocabulary.""",
)
a : List[str] = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 369 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( _lowercase ) -> List[Any]:
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
for _ in range(0 , i + 1 ): # printing stars
print("""* """ , end="""""" )
print()
def __lowerCamelCase ( _lowercase ) -> Dict:
for i in range(_lowercase , 0 , -1 ):
for _ in range(_lowercase , 0 , -1 ): # printing stars
print("""* """ , end="""""" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(""" """ , end="""""" )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
if n <= 0:
print(""" ... .... nothing printing :(""" )
return
floyd(_lowercase ) # upper half
reverse_floyd(_lowercase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
a : List[Any] = 1
while K:
a : int = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
a : Tuple = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 338 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
a : Optional[Any] = 3_0_0 # TEMPERATURE (unit = K)
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , ) -> float:
if donor_conc <= 0:
raise ValueError("""Donor concentration should be positive""" )
elif acceptor_conc <= 0:
raise ValueError("""Acceptor concentration should be positive""" )
elif intrinsic_conc <= 0:
raise ValueError("""Intrinsic concentration should be positive""" )
elif donor_conc <= intrinsic_conc:
raise ValueError(
"""Donor concentration should be greater than intrinsic concentration""" )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
"""Acceptor concentration should be greater than intrinsic concentration""" )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A , A=None ) -> Union[str, Any]:
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
UpperCAmelCase : Optional[Any] = None
def _lowercase( self , A ) -> List[Any]:
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
UpperCAmelCase : Tuple = self._infer_socket_ifname()
# avoid clash with the NCCL port
UpperCAmelCase : str = str(distributed_port + 1 )
UpperCAmelCase : Any = dist.new_group(ranks=A , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _lowercase( self ) -> Dict:
return dist.get_rank(group=self.process_group ) == 0
def _lowercase( self , A , A , A=torch.floataa ) -> str:
UpperCAmelCase : List[Any] = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def _lowercase( self ) -> Any:
UpperCAmelCase : List[Any] = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
UpperCAmelCase : Optional[int] = next((addr for addr in addrs if addr.startswith("""e""" )) , A )
return ifname
def _lowercase( self , A , A ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
UpperCAmelCase , UpperCAmelCase : str = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
UpperCAmelCase : int = dist.get_world_size(group=self.process_group )
# gather logic
UpperCAmelCase : int = None
if self._is_main():
UpperCAmelCase : List[str] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
UpperCAmelCase : List[Any] = question_hidden_states.shape[0]
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = []
if self._is_main():
assert len(A ) == world_size
UpperCAmelCase , UpperCAmelCase : Optional[int] = self._main_retrieve(torch.cat(A ).numpy() , A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = torch.tensor(A ), torch.tensor(A )
UpperCAmelCase : List[str] = self._chunk_tensor(A , A )
UpperCAmelCase : Union[str, Any] = self._chunk_tensor(A , A )
UpperCAmelCase : Tuple = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
UpperCAmelCase : Optional[Any] = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 338 | 0 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Dict = logging.get_logger(__name__)
a : Tuple = """▁"""
a : Optional[int] = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
a : List[Any] = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
a : Tuple = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
a : Any = {
"""ernie-m-base""": 5_1_4,
"""ernie-m-large""": 5_1_4,
}
a : Optional[Any] = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = ['input_ids']
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_INIT_CONFIGURATION
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = RESOURCE_FILES_NAMES
def __init__( self , A , A=None , A=False , A="utf8" , A="[UNK]" , A="[SEP]" , A="[PAD]" , A="[CLS]" , A="[MASK]" , A = None , **A , ) -> None:
UpperCAmelCase : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , vocab_file=_SCREAMING_SNAKE_CASE , encoding=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
UpperCAmelCase : int = do_lower_case
UpperCAmelCase : Optional[int] = sentencepiece_model_ckpt
UpperCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
UpperCAmelCase : Union[str, Any] = self.load_vocab(filepath=_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase : Dict = {self.sp_model.id_to_piece(_SCREAMING_SNAKE_CASE ): id for id in range(self.sp_model.get_piece_size() )}
UpperCAmelCase : Optional[int] = {v: k for k, v in self.vocab.items()}
def _lowercase( self , A ) -> int:
if text is None:
return None
UpperCAmelCase : Optional[Any] = self.tokenize(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Any = '''''', []
for i, ch in enumerate(_SCREAMING_SNAKE_CASE ):
if ch in self.SP_CHAR_MAPPING:
UpperCAmelCase : Tuple = self.SP_CHAR_MAPPING.get(_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase : List[str] = unicodedata.normalize("""NFKC""" , _SCREAMING_SNAKE_CASE )
if self.is_whitespace(_SCREAMING_SNAKE_CASE ):
continue
normalized_text += ch
char_mapping.extend([i] * len(_SCREAMING_SNAKE_CASE ) )
UpperCAmelCase : Dict = normalized_text, [], 0
if self.do_lower_case:
UpperCAmelCase : Union[str, Any] = text.lower()
for token in split_tokens:
if token[:1] == "▁":
UpperCAmelCase : str = token[1:]
UpperCAmelCase : Optional[Any] = text[offset:].index(_SCREAMING_SNAKE_CASE ) + offset
UpperCAmelCase : List[str] = start + len(_SCREAMING_SNAKE_CASE )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
UpperCAmelCase : Any = end
return token_mapping
@property
def _lowercase( self ) -> List[Any]:
return len(self.vocab )
def _lowercase( self ) -> List[str]:
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self ) -> str:
UpperCAmelCase : Dict = self.__dict__.copy()
UpperCAmelCase : int = None
return state
def __setstate__( self , A ) -> Union[str, Any]:
UpperCAmelCase : str = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCAmelCase : str = {}
UpperCAmelCase : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def _lowercase( self , A ) -> Optional[Any]:
return "".join((self.SP_CHAR_MAPPING.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for c in text) )
def _lowercase( self , A , A=False , A=64 , A=0.1 ) -> str:
if self.sp_model_kwargs.get("""enable_sampling""" ) is True:
UpperCAmelCase : List[str] = True
if self.sp_model_kwargs.get("""alpha""" ) is not None:
UpperCAmelCase : Tuple = self.sp_model_kwargs.get("""alpha""" )
if self.sp_model_kwargs.get("""nbest_size""" ) is not None:
UpperCAmelCase : Union[str, Any] = self.sp_model_kwargs.get("""nbest_size""" )
if not enable_sampling:
UpperCAmelCase : Optional[Any] = self.sp_model.EncodeAsPieces(_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase : List[str] = self.sp_model.SampleEncodeAsPieces(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCAmelCase : Tuple = []
for pi, piece in enumerate(_SCREAMING_SNAKE_CASE ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(_SCREAMING_SNAKE_CASE ) and pi != 0:
new_pieces.append(_SCREAMING_SNAKE_CASE )
continue
else:
continue
UpperCAmelCase : List[Any] = 0
for i, chunk in enumerate(_SCREAMING_SNAKE_CASE ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(_SCREAMING_SNAKE_CASE ) or self.is_punct(_SCREAMING_SNAKE_CASE ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : List[Any] = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
UpperCAmelCase : Union[str, Any] = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
UpperCAmelCase : Any = i
if len(_SCREAMING_SNAKE_CASE ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def _lowercase( self , A ) -> int:
UpperCAmelCase : Union[str, Any] = ''''''.join(_SCREAMING_SNAKE_CASE ).replace(_SCREAMING_SNAKE_CASE , """ """ ).strip()
return out_string
def _lowercase( self , A ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : List[str] = ''''''.join(_SCREAMING_SNAKE_CASE ).replace(_SCREAMING_SNAKE_CASE , """ """ ).strip()
return out_string
def _lowercase( self , A ) -> int:
return self.vocab.get(_SCREAMING_SNAKE_CASE , self.vocab.get(self.unk_token ) )
def _lowercase( self , A ) -> Dict:
return self.reverse_vocab.get(_SCREAMING_SNAKE_CASE , self.unk_token )
def _lowercase( self , A , A=None ) -> str:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase : List[Any] = [self.cls_token_id]
UpperCAmelCase : Optional[Any] = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def _lowercase( self , A , A=None ) -> List[Any]:
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def _lowercase( self , A , A=None , A=False ) -> Optional[Any]:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def _lowercase( self , A , A = None ) -> List[int]:
if token_ids_a is None:
# [CLS] X [SEP]
return (len(_SCREAMING_SNAKE_CASE ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(_SCREAMING_SNAKE_CASE ) + 1) + [1] * (len(_SCREAMING_SNAKE_CASE ) + 3)
def _lowercase( self , A ) -> List[Any]:
if "\u4e00" <= char <= "\u9fff":
return True
return False
def _lowercase( self , A ) -> List[str]:
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def _lowercase( self , A ) -> Tuple:
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def _lowercase( self , A ) -> Optional[Any]:
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(_SCREAMING_SNAKE_CASE ) == 1:
UpperCAmelCase : Dict = unicodedata.category(_SCREAMING_SNAKE_CASE )
if cat == "Zs":
return True
return False
def _lowercase( self , A ) -> Optional[Any]:
UpperCAmelCase : int = {}
with io.open(_SCREAMING_SNAKE_CASE , """r""" , encoding="""utf-8""" ) as f:
for index, line in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase : Dict = line.rstrip("""\n""" )
UpperCAmelCase : int = int(_SCREAMING_SNAKE_CASE )
return token_to_idx
def _lowercase( self , A , A = None ) -> Tuple[str]:
UpperCAmelCase : Dict = 0
if os.path.isdir(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase : Tuple = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
else:
UpperCAmelCase : List[Any] = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
with open(_SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda A : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
""" Please check that the vocabulary is not corrupted!""" )
UpperCAmelCase : Optional[Any] = token_index
writer.write(token + """\n""" )
index += 1
UpperCAmelCase : str = os.path.join(_SCREAMING_SNAKE_CASE , """sentencepiece.bpe.model""" )
with open(_SCREAMING_SNAKE_CASE , """wb""" ) as fi:
UpperCAmelCase : int = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (vocab_file,)
| 371 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a : List[Any] = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = BlenderbotSmallTokenizer
def __init__( self , A=None , A=None , A="<|endoftext|>" , A="<|endoftext|>" , A="<|endoftext|>" , A=False , A=True , **A , ) -> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=A , merges=A , add_prefix_space=A , trim_offsets=A , ) , bos_token=A , eos_token=A , unk_token=A , **A , )
UpperCAmelCase : Optional[Any] = add_prefix_space
def _lowercase( self , A , A=None ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase( self , A , A = None ) -> List[int]:
UpperCAmelCase : Any = [self.sep_token_id]
UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 338 | 0 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 350 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , A , A , A = None , A = None , A = False , **A , ) -> Tuple:
super().__init__(features=A , cache_dir=A , keep_in_memory=A , **A )
UpperCAmelCase : Any = Sql(
cache_dir=A , features=A , sql=A , con=A , **A , )
def _lowercase( self ) -> Dict:
UpperCAmelCase : Any = None
UpperCAmelCase : Any = None
UpperCAmelCase : int = None
UpperCAmelCase : int = None
self.builder.download_and_prepare(
download_config=A , download_mode=A , verification_mode=A , base_path=A , )
# Build dataset for splits
UpperCAmelCase : str = self.builder.as_dataset(
split="""train""" , verification_mode=A , in_memory=self.keep_in_memory )
return dataset
class UpperCamelCase_ :
def __init__( self , A , A , A , A = None , A = None , **A , ) -> str:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
UpperCAmelCase : Dict = dataset
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Any = con
UpperCAmelCase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase : Optional[Any] = num_proc
UpperCAmelCase : str = to_sql_kwargs
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.to_sql_kwargs.pop("""sql""" , A )
UpperCAmelCase : str = self.to_sql_kwargs.pop("""con""" , A )
UpperCAmelCase : Union[str, Any] = self.to_sql_kwargs.pop("""index""" , A )
UpperCAmelCase : str = self._write(index=A , **self.to_sql_kwargs )
return written
def _lowercase( self , A ) -> Any:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = args
UpperCAmelCase : Union[str, Any] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase : int = query_table(
table=self.dataset.data , key=slice(A , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase : Any = batch.to_pandas()
UpperCAmelCase : List[Any] = df.to_sql(self.name , self.con , index=A , **A )
return num_rows or len(A )
def _lowercase( self , A , **A ) -> int:
UpperCAmelCase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase , UpperCAmelCase : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , A , A )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : str = {
"""configuration_mobilebert""": [
"""MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileBertConfig""",
"""MobileBertOnnxConfig""",
],
"""tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : str = ["""MobileBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Union[str, Any] = [
"""MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MobileBertForMaskedLM""",
"""MobileBertForMultipleChoice""",
"""MobileBertForNextSentencePrediction""",
"""MobileBertForPreTraining""",
"""MobileBertForQuestionAnswering""",
"""MobileBertForSequenceClassification""",
"""MobileBertForTokenClassification""",
"""MobileBertLayer""",
"""MobileBertModel""",
"""MobileBertPreTrainedModel""",
"""load_tf_weights_in_mobilebert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[str] = [
"""TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFMobileBertForMaskedLM""",
"""TFMobileBertForMultipleChoice""",
"""TFMobileBertForNextSentencePrediction""",
"""TFMobileBertForPreTraining""",
"""TFMobileBertForQuestionAnswering""",
"""TFMobileBertForSequenceClassification""",
"""TFMobileBertForTokenClassification""",
"""TFMobileBertMainLayer""",
"""TFMobileBertModel""",
"""TFMobileBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
a : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 351 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCamelCase_ :
lowercase = MBartConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Optional[int]:
UpperCAmelCase : Optional[int] = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : str = is_training
UpperCAmelCase : Optional[int] = use_labels
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : Union[str, Any] = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : Optional[int] = intermediate_size
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Optional[Any] = eos_token_id
UpperCAmelCase : List[str] = pad_token_id
UpperCAmelCase : List[Any] = bos_token_id
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[Any] = prepare_mbart_inputs_dict(A , A , A )
return config, inputs_dict
def _lowercase( self , A , A ) -> List[str]:
UpperCAmelCase : List[str] = TFMBartModel(config=A ).get_decoder()
UpperCAmelCase : int = inputs_dict["""input_ids"""]
UpperCAmelCase : str = input_ids[:1, :]
UpperCAmelCase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
UpperCAmelCase : List[str] = inputs_dict["""head_mask"""]
UpperCAmelCase : List[Any] = 1
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , head_mask=A , use_cache=A )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = outputs.to_tuple()
UpperCAmelCase : int = past_key_values[1]
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , ) -> List[str]:
if attention_mask is None:
UpperCAmelCase : Tuple = tf.cast(tf.math.not_equal(_lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowercase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowercase = (
{
'conversational': TFMBartForConditionalGeneration,
'feature-extraction': TFMBartModel,
'summarization': TFMBartForConditionalGeneration,
'text2text-generation': TFMBartForConditionalGeneration,
'translation': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase = True
lowercase = False
lowercase = False
def _lowercase( self , A , A , A , A , A ) -> int:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : int = TFMBartModelTester(self )
UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A )
def _lowercase( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> Dict:
UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
lowercase = [
' UN Chief Says There Is No Military Solution in Syria',
]
lowercase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
]
lowercase = 'facebook/mbart-large-en-ro'
@cached_property
def _lowercase( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase( self , **A ) -> Any:
UpperCAmelCase : Optional[int] = self.translate_src_text(**A )
self.assertListEqual(self.expected_text , A )
def _lowercase( self , **A ) -> Optional[Any]:
UpperCAmelCase : List[str] = self.tokenizer(self.src_text , **A , return_tensors="""tf""" )
UpperCAmelCase : int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCAmelCase : Any = self.tokenizer.batch_decode(A , skip_special_tokens=A )
return generated_words
@slow
def _lowercase( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 338 | 0 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
a : int = logging.get_logger(__name__)
a : Dict = '▁'
a : Optional[Any] = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
'tokenizer_config_file': 'tokenizer_config.json',
}
a : Optional[int] = {
'vocab_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
},
'spm_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_config_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
},
}
a : Tuple = {
'facebook/m2m100_418M': 1_0_2_4,
}
# fmt: off
a : Optional[Any] = {
'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}
class UpperCamelCase_ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = ['input_ids', 'attention_mask']
lowercase = []
lowercase = []
def __init__( self , A , A , A=None , A=None , A="<s>" , A="</s>" , A="</s>" , A="<pad>" , A="<unk>" , A="m2m100" , A = None , A=8 , **A , ) -> None:
UpperCAmelCase : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase : Dict = language_codes
UpperCAmelCase : str = FAIRSEQ_LANGUAGE_CODES[language_codes]
UpperCAmelCase : int = {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}
UpperCAmelCase : str = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(A )
for lang_code in fairseq_language_code
if self.get_lang_token(A ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=A , tgt_lang=A , bos_token=A , eos_token=A , sep_token=A , unk_token=A , pad_token=A , language_codes=A , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=A , **A , )
UpperCAmelCase : List[str] = vocab_file
UpperCAmelCase : List[str] = load_json(A )
UpperCAmelCase : Dict = {v: k for k, v in self.encoder.items()}
UpperCAmelCase : Union[str, Any] = spm_file
UpperCAmelCase : int = load_spm(A , self.sp_model_kwargs )
UpperCAmelCase : Union[str, Any] = len(self.encoder )
UpperCAmelCase : List[str] = {
self.get_lang_token(A ): self.encoder_size + i for i, lang_code in enumerate(A )
}
UpperCAmelCase : Union[str, Any] = {lang_code: self.encoder_size + i for i, lang_code in enumerate(A )}
UpperCAmelCase : Any = {v: k for k, v in self.lang_token_to_id.items()}
UpperCAmelCase : int = src_lang if src_lang is not None else '''en'''
UpperCAmelCase : Any = tgt_lang
UpperCAmelCase : List[Any] = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
UpperCAmelCase : List[Any] = num_madeup_words
@property
def _lowercase( self ) -> int:
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def _lowercase( self ) -> str:
return self._src_lang
@src_lang.setter
def _lowercase( self , A ) -> None:
UpperCAmelCase : Optional[int] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _lowercase( self , A ) -> List[str]:
return self.sp_model.encode(A , out_type=A )
def _lowercase( self , A ) -> List[str]:
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(A , self.encoder[self.unk_token] )
def _lowercase( self , A ) -> str:
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(A , self.unk_token )
def _lowercase( self , A ) -> List[Any]:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Union[str, Any] = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A ) + token
UpperCAmelCase : Optional[Any] = []
else:
current_sub_tokens.append(A )
out_string += self.sp_model.decode(A )
return out_string.strip()
def _lowercase( self , A , A = None , A = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
UpperCAmelCase : int = [1] * len(self.prefix_tokens )
UpperCAmelCase : Any = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(A )) + suffix_ones
return prefix_ones + ([0] * len(A )) + ([0] * len(A )) + suffix_ones
def _lowercase( self , A , A = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowercase( self ) -> Dict:
UpperCAmelCase : List[Any] = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Dict:
UpperCAmelCase : Union[str, Any] = self.__dict__.copy()
UpperCAmelCase : List[Any] = None
return state
def __setstate__( self , A ) -> None:
UpperCAmelCase : Any = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCAmelCase : int = {}
UpperCAmelCase : Tuple = load_spm(self.spm_file , self.sp_model_kwargs )
def _lowercase( self , A , A = None ) -> Tuple[str]:
UpperCAmelCase : Any = Path(A )
if not save_dir.is_dir():
raise OSError(f'''{save_directory} should be a directory''' )
UpperCAmelCase : Any = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
UpperCAmelCase : List[Any] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , A )
if os.path.abspath(self.spm_file ) != os.path.abspath(A ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , A )
elif not os.path.isfile(self.spm_file ):
with open(A , """wb""" ) as fi:
UpperCAmelCase : Dict = self.sp_model.serialized_model_proto()
fi.write(A )
return (str(A ), str(A ))
def _lowercase( self , A , A = "en" , A = None , A = "ro" , **A , ) -> BatchEncoding:
UpperCAmelCase : List[Any] = src_lang
UpperCAmelCase : Dict = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(A , A , **A )
def _lowercase( self , A , A , A , **A ) -> Any:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
UpperCAmelCase : str = src_lang
UpperCAmelCase : Any = self(A , add_special_tokens=A , **A )
UpperCAmelCase : Union[str, Any] = self.get_lang_id(A )
UpperCAmelCase : List[Any] = tgt_lang_id
return inputs
def _lowercase( self ) -> List[str]:
self.set_src_lang_special_tokens(self.src_lang )
def _lowercase( self ) -> List[str]:
self.set_tgt_lang_special_tokens(self.tgt_lang )
def _lowercase( self , A ) -> None:
UpperCAmelCase : List[str] = self.get_lang_token(A )
UpperCAmelCase : Union[str, Any] = self.lang_token_to_id[lang_token]
UpperCAmelCase : Any = [self.cur_lang_id]
UpperCAmelCase : Optional[int] = [self.eos_token_id]
def _lowercase( self , A ) -> None:
UpperCAmelCase : Optional[Any] = self.get_lang_token(A )
UpperCAmelCase : List[Any] = self.lang_token_to_id[lang_token]
UpperCAmelCase : Optional[Any] = [self.cur_lang_id]
UpperCAmelCase : Union[str, Any] = [self.eos_token_id]
def _lowercase( self , A ) -> str:
return self.lang_code_to_token[lang]
def _lowercase( self , A ) -> int:
UpperCAmelCase : List[Any] = self.get_lang_token(A )
return self.lang_token_to_id[lang_token]
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
UpperCAmelCase : Any = sentencepiece.SentencePieceProcessor(**UpperCamelCase__ )
spm.Load(str(UpperCamelCase__ ) )
return spm
def __lowerCamelCase ( _lowercase ) -> int:
with open(UpperCamelCase__ , """r""" ) as f:
return json.load(UpperCamelCase__ )
def __lowerCamelCase ( _lowercase , _lowercase ) -> Optional[int]:
with open(UpperCamelCase__ , """w""" ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ , indent=2 )
| 352 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Tuple = len(_lowercase ) + 1
UpperCAmelCase : List[Any] = len(_lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
UpperCAmelCase : str = [[0 for i in range(_lowercase )] for j in range(_lowercase )]
# since string of zero length match pattern of zero length
UpperCAmelCase : int = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowercase ):
UpperCAmelCase : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowercase ):
UpperCAmelCase : Optional[Any] = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _lowercase ):
for j in range(1 , _lowercase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
UpperCAmelCase : Union[str, Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
UpperCAmelCase : List[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
UpperCAmelCase : Optional[int] = dp[i - 1][j]
else:
UpperCAmelCase : Any = 0
else:
UpperCAmelCase : str = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a : List[str] = """aab"""
a : Optional[int] = """c*a*b"""
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 338 | 0 |
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