




"""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


UpperCamelCase__						=			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(4_2)

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

UpperCamelCase__						=			'''zero2'''
UpperCamelCase__						=			'''zero3'''
UpperCamelCase__						=			[ZEROa, ZEROa]





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					Tuple      ,       snake_case			:					Dict    ):
					# customize the test name generator function as we want both params to appear in the sub-test
					# name, as by default it shows only the first param
					_lowerCAmelCase:Tuple									=  parameterized.to_safe_name('''_'''.join(str(snake_case    ) for x in param.args    )    )
					return F'{func.__name__}_{param_based_name}'


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





@slow
@require_deepspeed
@require_torch_gpu
class   a__			(       UpperCamelCase_  ):





						@parameterized.expand(a__     ,name_func=a__)
						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Optional[Any]     ,a__      :	Optional[int])						->       List[str]:




											"""simple docstring"""







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





						@require_torch_multi_gpu
						@parameterized.expand(a__     ,name_func=a__)
						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Tuple     ,a__      :	Union[str, Any])						->       Optional[int]:




											"""simple docstring"""







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





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




											"""simple docstring"""







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





						@require_torch_multi_gpu
						@parameterized.expand(a__     ,name_func=a__)
						def       __UpperCamelCase     (							self      :	Optional[Any]     ,a__      :	List[str]     ,a__      :	List[str])						->       str:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Tuple)						->       Union[str, Any]:




											"""simple docstring"""







											pass





						def       __UpperCamelCase     (							self      :	Any     ,a__      :	str     ,a__      :	str     ,a__      :	int = 10     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	bool = True     ,)						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  models[model]

											_lowerCAmelCase:str									=  self.run_trainer(
											    stage=a__     ,model_name=a__     ,eval_steps=a__     ,num_train_epochs=1     ,distributed=a__     ,fpaa=a__     ,)

											self.do_checks(a__)

											return output_dir





						def       __UpperCamelCase     (							self      :	str     ,a__      :	str     ,a__      :	str     ,a__      :	int = 10     ,a__      :	int = 1     ,a__      :	bool = True     ,a__      :	bool = True     ,)						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.get_auto_remove_tmp_dir('''./xxx'''     ,after=a__)
											_lowerCAmelCase:int									=  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(a__)}\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
											_lowerCAmelCase:List[Any]									=  F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
											_lowerCAmelCase:int									=  [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
											_lowerCAmelCase:List[str]									=  self.get_launcher(a__)

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

											return output_dir







						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	List[str]=False)						->       int:




											"""simple docstring"""







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












"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__						=			logging.get_logger(__name__)




class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	Dict     ,*a__      :	List[str]     ,**a__      :	str)						->       None:




											"""simple docstring"""







											warnings.warn(
											    '''The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
											    ''' Please use PoolFormerImageProcessor instead.'''     ,a__     ,)
											super().__init__(*a__     ,**a__)






