
"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase_       :						Dict     					=	Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase_       :						List[str]     					=	{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

lowercase_       :						Tuple     					=	'''zero2'''
lowercase_       :						Optional[Any]     					=	'''zero3'''
lowercase_       :						List[Any]     					=	[ZEROa, ZEROa]





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							str				)  ->    str:
  _SCREAMING_SNAKE_CASE   :				Tuple							    = parameterized.to_safe_name("_".join(str(A__				) for x in param.args				)				)
  return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowercase_       :						Optional[Any]     					=	list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 			UpperCamelCase      (							__lowerCamelCase  ):
     @parameterized.expand(UpperCamelCase_  ,     name_func=UpperCamelCase_				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       self.run_and_check(
           stage=UpperCamelCase_  ,     model=UpperCamelCase_  ,     distributed=UpperCamelCase_  ,     fpaa=UpperCamelCase_  ,     )
     @require_torch_multi_gpu
     @parameterized.expand(UpperCamelCase_  ,     name_func=UpperCamelCase_				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       self.run_and_check(
           stage=UpperCamelCase_  ,     model=UpperCamelCase_  ,     distributed=UpperCamelCase_  ,     fpaa=UpperCamelCase_  ,     )
     @parameterized.expand(UpperCamelCase_  ,     name_func=UpperCamelCase_				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       self.run_and_check(
           stage=UpperCamelCase_  ,     model=UpperCamelCase_  ,     distributed=UpperCamelCase_  ,     fpaa=UpperCamelCase_  ,     )
     @require_torch_multi_gpu
     @parameterized.expand(UpperCamelCase_  ,     name_func=UpperCamelCase_				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       self.run_and_check(
           stage=UpperCamelCase_  ,     model=UpperCamelCase_  ,     distributed=UpperCamelCase_  ,     fpaa=UpperCamelCase_  ,     )
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       pass
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = 10  ,     snake_case__ = True  ,     snake_case__ = True  ,     snake_case__ = True  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = models[model]

       _SCREAMING_SNAKE_CASE   :				int							    = self.run_trainer(
           stage=UpperCamelCase_  ,     model_name=UpperCamelCase_  ,     eval_steps=UpperCamelCase_  ,     num_train_epochs=1  ,     distributed=UpperCamelCase_  ,     fpaa=UpperCamelCase_  ,     )

       self.do_checks(UpperCamelCase_				)

       return output_dir
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = 10  ,     snake_case__ = 1  ,     snake_case__ = True  ,     snake_case__ = True  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.get_auto_remove_tmp_dir("./xxx"  ,     after=UpperCamelCase_				)
       _SCREAMING_SNAKE_CASE   :				str							    = F'''\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(UpperCamelCase_				)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '''.split()

       if fpaa:
         args.extend(["--fp16"]				)

       # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
       # hence the separate config files
       _SCREAMING_SNAKE_CASE   :				int							    = F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
       _SCREAMING_SNAKE_CASE   :				int							    = [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.get_launcher(UpperCamelCase_				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = launcher + script + args + ds_args
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       execute_subprocess_async(UpperCamelCase_  ,     env=self.get_env()				)

       return output_dir




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=False				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = min(2  ,     get_gpu_count()				) if distributed else 1
       return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()





"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be trained."""}  )
     A__      =			field(
         default="""./"""      ,       metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path of training dataset."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for training."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for evaluation."""}  )
     A__      =			field(default=0.1      ,       metadata={"""help""": """Value of weight decay."""}  )
     A__      =			field(
         default=10000      ,       metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}  )
     A__      =			field(default=2e-4      ,       metadata={"""help""": """Learning rate fo training."""}  )
     A__      =			field(default="""cosine"""      ,       metadata={"""help""": """Learning rate."""}  )
     A__      =			field(
         default=750      ,       metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}  )
     A__      =			field(
         default=16      ,       metadata={"""help""": """Number of gradient accumulation steps."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}  )
     A__      =			field(default=50000      ,       metadata={"""help""": """Maximum number of training steps."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Sequence lengths used for training."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Training seed."""}  )
     A__      =			field(
         default=1024      ,       metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True the data is pretokenized."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size used for evaluation."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Length of sequences to be evaluated."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Sample from the language model's output distribution."""}  )
     A__      =			field(default=0.2      ,       metadata={"""help""": """Sampling temperature used for generation."""}  )
     A__      =			field(default=256      ,       metadata={"""help""": """Maximum number of newly generated tokens."""}  )
     A__      =			field(default=0      ,       metadata={"""help""": """Top-k parameter used for generation."""}  )
     A__      =			field(default=0.95      ,       metadata={"""help""": """Top-p parameter used for nucleus sampling."""}  )
     A__      =			field(default=10      ,       metadata={"""help""": """Number of generations to run in parallel."""}  )
     A__      =			field(
         default=200      ,       metadata={"""help""": """Number of completions to generate for each sample."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""eval_results.json"""      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""0"""      ,       metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}  )
     A__      =			field(
         default=-1      ,       metadata={
             """help""": (
                 """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
                 """ number corresponds to which GPU device id to run on."""
             )
         }      ,       )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         }      ,       )
     A__      =			field(
         default="""transformersbook/codeparrot"""      ,       metadata={"""help""": """Folder or name of dataset to process."""}  )
     A__      =			field(
         default="""codeparrot-clean"""      ,       metadata={"""help""": """Folder to save processed processed dataset."""}  )
     A__      =			field(
         default=100000      ,       metadata={"""help""": """Number of files to save per JSON output file."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(
         default=1000      ,       metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=100      ,       metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.25      ,       metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}  )
     A__      =			field(
         default=1.5      ,       metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.7      ,       metadata={"""help""": """Probability for filtering config, test and uncommon files."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True, near-duplicate samples are removed."""}  )
     A__      =			field(
         default=0.85      ,       metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2"""      ,       metadata={"""help""": """Base tokenizer to build new tokenizer from."""}  )
     A__      =			field(
         default="""transformersbook/codeparrot-train"""      ,       metadata={"""help""": """Dataset to train tokenizer on."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(default=200000      ,       metadata={"""help""": """Number of examples to train tokenizer on."""}  )
     A__      =			field(
         default=32768      ,       metadata={"""help""": """Number of examples to train the tokenizer on."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of new tokenizer."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path to the dataset to pretokenize."""}  )
     A__      =			field(
         default="""tokenized-codeparrot-train"""      ,       metadata={"""help""": """Repo name of the pretokenized data."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2-large"""      ,       metadata={"""help""": """Configuration to use for model initialization."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Tokenizer attached to model."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of the created model."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )



