


# 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


lowerCAmelCase       =							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)

lowerCAmelCase       =							{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowerCAmelCase       =							'zero2'
lowerCAmelCase       =							'zero3'
lowerCAmelCase       =							[ZEROa, ZEROa]
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      parameterized.to_safe_name('''_'''.join(str(SCREAMING_SNAKE_CASE     ) for x in param.args     )     )
		return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowerCAmelCase       =							list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class        _a							(			UpperCamelCase__			):

							@parameterized.expand(UpperCamelCase_    ,						name_func=UpperCamelCase_							)
							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Dict							)			->   Optional[Any]:





									"""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       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Optional[Any]							)			->   List[Any]:





									"""simple docstring"""







									self.run_and_check(
									    stage=UpperCamelCase_    ,						model=UpperCamelCase_    ,						distributed=UpperCamelCase_    ,						fpaa=UpperCamelCase_    ,						)

							@parameterized.expand(UpperCamelCase_    ,						name_func=UpperCamelCase_							)
							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	Dict							)			->   Dict:





									"""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       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int							)			->   str:





									"""simple docstring"""







									self.run_and_check(
									    stage=UpperCamelCase_    ,						model=UpperCamelCase_    ,						distributed=UpperCamelCase_    ,						fpaa=UpperCamelCase_    ,						)

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	Optional[Any]							)			->   Tuple:





									"""simple docstring"""







									pass

							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	int = 10    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	bool = True    ,						)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      models[model]

									lowercase__            =      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       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	int = 10    ,						UpperCamelCase_:	int = 1    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	bool = True    ,						)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      self.get_auto_remove_tmp_dir('''./xxx'''    ,						after=UpperCamelCase_							)
									lowercase__            =      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
									lowercase__            =      f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
									lowercase__            =      [f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
									lowercase__            =      self.get_launcher(UpperCamelCase_							)

									lowercase__            =      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       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	Union[str, Any]=False							)			->   str:





									"""simple docstring"""







									lowercase__            =      min(2    ,						get_gpu_count()							) if distributed else 1
									return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()






import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase       =							'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase       =							direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase       =							transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCAmelCase       =							re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


lowerCAmelCase       =							{
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      None

		# source code of `config_class`
		lowercase__            =      inspect.getsource(SCREAMING_SNAKE_CASE     )
		lowercase__            =      _re_checkpoint.findall(SCREAMING_SNAKE_CASE     )

		# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
		# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
		for ckpt_name, ckpt_link in checkpoints:
				# allow the link to end with `/`
				if ckpt_link.endswith('''/'''     ):
						lowercase__            =      ckpt_link[:-1]

				# verify the checkpoint name corresponds to the checkpoint link
				lowercase__            =      f'https://huggingface.co/{ckpt_name}'
				if ckpt_link == ckpt_link_from_name:
						lowercase__            =      ckpt_name
						break

		return checkpoint
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      []

		for config_class in list(CONFIG_MAPPING.values()     ):
				# Skip deprecated models
				if "models.deprecated" in config_class.__module__:
						continue
				lowercase__            =      get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE     )

				lowercase__            =      config_class.__name__
				if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
						configs_without_checkpoint.append(SCREAMING_SNAKE_CASE     )

		if len(SCREAMING_SNAKE_CASE     ) > 0:
				lowercase__            =      '''\n'''.join(sorted(SCREAMING_SNAKE_CASE     )     )
				raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'     )


if __name__ == "__main__":
			check_config_docstrings_have_checkpoints()


