


'''simple docstring'''
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							assert isinstance(UpperCamelCase_						,  UpperCamelCase_ )
							assert dataset.num_rows == 4
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_1", "col_2", "col_3"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"						,  [False, True] )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path / "cache"
							A_			    =		{"col_1": "string", "col_2": "int64", "col_3": "float64"}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														A_			    =		ParquetDatasetReader(UpperCamelCase_						,  cache_dir=UpperCamelCase_						,  keep_in_memory=UpperCamelCase_ ).read()
							_check_parquet_dataset(UpperCamelCase_						,  UpperCamelCase_ )







@pytest.mark.parametrize(
    "features"						,  [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]						,  )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path / "cache"
							A_			    =		{"col_1": "string", "col_2": "int64", "col_3": "float64"}
							A_			    =		features.copy() if features else default_expected_features
							A_			    =		(
							    Features({feature: Value(UpperCamelCase_ ) for feature, dtype in features.items()} ) if features is not None else None
							)
							A_			    =		ParquetDatasetReader(UpperCamelCase_						,  features=UpperCamelCase_						,  cache_dir=UpperCamelCase_ ).read()
							_check_parquet_dataset(UpperCamelCase_						,  UpperCamelCase_ )







@pytest.mark.parametrize("split"						,  [None, NamedSplit("train" ), "train", "test"] )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path / "cache"
							A_			    =		{"col_1": "string", "col_2": "int64", "col_3": "float64"}
							A_			    =		ParquetDatasetReader(UpperCamelCase_						,  cache_dir=UpperCamelCase_						,  split=UpperCamelCase_ ).read()
							_check_parquet_dataset(UpperCamelCase_						,  UpperCamelCase_ )
							assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"						,  [str, list] )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							if issubclass(UpperCamelCase_						,  UpperCamelCase_ ):
														A_			    =		parquet_path
							elif issubclass(UpperCamelCase_						,  UpperCamelCase_ ):
														A_			    =		[parquet_path]
							A_			    =		tmp_path / "cache"
							A_			    =		{"col_1": "string", "col_2": "int64", "col_3": "float64"}
							A_			    =		ParquetDatasetReader(UpperCamelCase_						,  cache_dir=UpperCamelCase_ ).read()
							_check_parquet_dataset(UpperCamelCase_						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=("train",) ):
							assert isinstance(UpperCamelCase_						,  UpperCamelCase_ )
							for split in splits:
														A_			    =		dataset_dict[split]
														assert dataset.num_rows == 4
														assert dataset.num_columns == 3
														assert dataset.column_names == ["col_1", "col_2", "col_3"]
														for feature, expected_dtype in expected_features.items():
																					assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"						,  [False, True] )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path / "cache"
							A_			    =		{"col_1": "string", "col_2": "int64", "col_3": "float64"}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														A_			    =		ParquetDatasetReader(
														    {"train": parquet_path}						,  cache_dir=UpperCamelCase_						,  keep_in_memory=UpperCamelCase_ ).read()
							_check_parquet_datasetdict(UpperCamelCase_						,  UpperCamelCase_ )







@pytest.mark.parametrize(
    "features"						,  [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]						,  )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path / "cache"
							A_			    =		{"col_1": "string", "col_2": "int64", "col_3": "float64"}
							A_			    =		features.copy() if features else default_expected_features
							A_			    =		(
							    Features({feature: Value(UpperCamelCase_ ) for feature, dtype in features.items()} ) if features is not None else None
							)
							A_			    =		ParquetDatasetReader({"train": parquet_path}						,  features=UpperCamelCase_						,  cache_dir=UpperCamelCase_ ).read()
							_check_parquet_datasetdict(UpperCamelCase_						,  UpperCamelCase_ )







@pytest.mark.parametrize("split"						,  [None, NamedSplit("train" ), "train", "test"] )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							if split:
														A_			    =		{split: parquet_path}
							else:
														A_			    =		"train"
														A_			    =		{"train": parquet_path, "test": parquet_path}
							A_			    =		tmp_path / "cache"
							A_			    =		{"col_1": "string", "col_2": "int64", "col_3": "float64"}
							A_			    =		ParquetDatasetReader(UpperCamelCase_						,  cache_dir=UpperCamelCase_ ).read()
							_check_parquet_datasetdict(UpperCamelCase_						,  UpperCamelCase_						,  splits=list(path.keys() ) )
							assert all(dataset[split].split == split for split in path.keys() )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		ParquetDatasetWriter(UpperCamelCase_						,  tmp_path / "foo.parquet" )
							assert writer.write() > 0
							A_			    =		pq.ParquetFile(tmp_path / "foo.parquet" )
							A_			    =		pf.read()
							assert dataset.data.table == output_table







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		str(shared_datadir / "test_image_rgb.jpg" )
							A_			    =		{"image": [image_path]}
							A_			    =		Features({"image": Image()} )
							A_			    =		Dataset.from_dict(UpperCamelCase_						,  features=UpperCamelCase_ )
							A_			    =		ParquetDatasetWriter(UpperCamelCase_						,  tmp_path / "foo.parquet" )
							assert writer.write() > 0

							A_			    =		Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
							assert dataset.features == reloaded_dataset.features

							A_			    =		ParquetDatasetReader(str(tmp_path / "foo.parquet" )						,  streaming=UpperCamelCase_ ).read()
							assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    "feature, expected"						,  [
        (Features({"foo": Value("int32" )} ), None),
        (Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]						,  )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							assert get_writer_batch_size(UpperCamelCase_ ) == expected









'''simple docstring'''
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE					:				List[Any]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Tuple									=		[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]







def       a_				(	UpperCamelCase_ ):
							if k == "embeddings.weight":
														return "shared.weight"

							for parlai_name, hf_name in PATTERNS:
														A_			    =		k.replace(UpperCamelCase_						,  UpperCamelCase_ )

							if k.startswith("encoder" ):
														A_			    =		k.replace(".attn"						,  ".self_attn" )
														A_			    =		k.replace("norm1"						,  "self_attn_layer_norm" )
														A_			    =		k.replace("norm2"						,  "final_layer_norm" )
							elif k.startswith("decoder" ):
														A_			    =		k.replace("norm1"						,  "self_attn_layer_norm" )
														A_			    =		k.replace("norm2"						,  "encoder_attn_layer_norm" )
														A_			    =		k.replace("norm3"						,  "final_layer_norm" )
							return k







def       a_				(	UpperCamelCase_ ):
							A_			    =		[
							    "model.encoder.layernorm_embedding.weight",
							    "model.encoder.layernorm_embedding.bias",
							    "model.decoder.layernorm_embedding.weight",
							    "model.decoder.layernorm_embedding.bias",
							]
							for k in keys:
														A_			    =		sd.pop(UpperCamelCase_ )
														A_			    =		k.replace("layernorm_embedding"						,  "layer_norm" )
														assert new_k not in sd
														A_			    =		v


__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		['''START''']







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		torch.load(UpperCamelCase_						,  map_location="cpu" )
							A_			    =		model["model"]
							A_			    =		BlenderbotConfig.from_json_file(UpperCamelCase_ )
							A_			    =		BlenderbotForConditionalGeneration(UpperCamelCase_ )
							A_			    =		m.model.state_dict().keys()
							A_			    =		[]
							A_			    =		{}
							for k, v in sd.items():
														if k in IGNORE_KEYS:
																					continue

														A_			    =		rename_state_dict_key(UpperCamelCase_ )
														if new_k not in valid_keys:
																					failures.append([k, new_k] )
														else:
																					A_			    =		v
							if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
														rename_layernorm_keys(UpperCamelCase_ )
							m.model.load_state_dict(UpperCamelCase_						,  strict=UpperCamelCase_ )
							m.half()
							m.save_pretrained(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
							parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
							parser.add_argument(
							    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
							)
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





