





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

__lowerCAmelCase :Optional[int]     =    'zero2'
__lowerCAmelCase :str     =    'zero3'
__lowerCAmelCase :int     =    [ZEROa, ZEROa]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					# 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
					_snake_case       :					Tuple				       =   parameterized.to_safe_name("_".join(str(UpperCAmelCase				) for x in param.args				)				)
					return F"""{func.__name__}_{param_based_name}"""


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






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







		@parameterized.expand(__snake_case ,						name_func=__snake_case				)
		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      int:


							'''simple docstring'''

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







		@require_torch_multi_gpu
		@parameterized.expand(__snake_case ,						name_func=__snake_case				)
		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

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







		@parameterized.expand(__snake_case ,						name_func=__snake_case				)
		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

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







		@require_torch_multi_gpu
		@parameterized.expand(__snake_case ,						name_func=__snake_case				)
		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							pass







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case = 1_0 ,						__snake_case = True ,						__snake_case = True ,						__snake_case = True ,						)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					int				       =   models[model]

							_snake_case       :					Union[str, Any]				       =   self.run_trainer(
							    stage=__snake_case ,						model_name=__snake_case ,						eval_steps=__snake_case ,						num_train_epochs=1 ,						distributed=__snake_case ,						fpaa=__snake_case ,						)

							self.do_checks(__snake_case				)

							return output_dir







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case = 1_0 ,						__snake_case = 1 ,						__snake_case = True ,						__snake_case = True ,						)      ->      Dict:


							'''simple docstring'''

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

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

							# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
							# hence the separate config files
							_snake_case       :					int				       =   f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
							_snake_case       :					Any				       =   [f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
							_snake_case       :					List[str]				       =   self.get_launcher(__snake_case				)

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

							return output_dir







		def        lowercase       (    self ,						__snake_case=False				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					str				       =   min(2 ,						get_gpu_count()				) if distributed else 1
							return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()






import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


__lowerCAmelCase :str     =    logging.get_logger(__name__)






class        _a(    __A    ):







		def __init__(    self ,						*__snake_case ,						**__snake_case				)      ->      None:


							'''simple docstring'''

							warnings.warn(
							    "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
							    " Please use VideoMAEImageProcessor instead." ,						__snake_case ,						)
							super().__init__(*__snake_case ,						**__snake_case				)