


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

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

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase):
						# 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											= 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
_UpperCAmelCase :       Union[str, Any]										= list(itertools.product(stages, models.keys()))




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







				"""simple docstring"""



				@parameterized.expand(_snake_case				,	name_func=_snake_case						)
				def     _snake_case     (self				,	__lowercase				,	__lowercase						):
										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     _snake_case     (self				,	__lowercase				,	__lowercase						):
										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     _snake_case     (self				,	__lowercase				,	__lowercase						):
										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     _snake_case     (self				,	__lowercase				,	__lowercase						):
										self.run_and_check(
										    stage=_snake_case				,	model=_snake_case				,	distributed=_snake_case				,	fpaa=_snake_case				,	)




				def     _snake_case     (self				,	__lowercase						):
										pass




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase = 10				,	__lowercase = True				,	__lowercase = True				,	__lowercase = True				,	):
										__lowerCAmelCase											= models[model]

										__lowerCAmelCase											= 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     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase = 10				,	__lowercase = 1				,	__lowercase = True				,	__lowercase = True				,	):
										__lowerCAmelCase											= self.get_auto_remove_tmp_dir('''./xxx'''				,	after=_snake_case						)
										__lowerCAmelCase											= 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
										__lowerCAmelCase											= F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
										__lowerCAmelCase											= [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
										__lowerCAmelCase											= self.get_launcher(_snake_case						)

										__lowerCAmelCase											= 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     _snake_case     (self				,	__lowercase=False						):
										__lowerCAmelCase											= min(2				,	get_gpu_count()						) if distributed else 1
										return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	Optional[int]    = {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['WhisperTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	str    = [
					    'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'WhisperForConditionalGeneration',
					    'WhisperModel',
					    'WhisperPreTrainedModel',
					    'WhisperForAudioClassification',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFWhisperForConditionalGeneration',
					    'TFWhisperModel',
					    'TFWhisperPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'FlaxWhisperForConditionalGeneration',
					    'FlaxWhisperModel',
					    'FlaxWhisperPreTrainedModel',
					    'FlaxWhisperForAudioClassification',
					]


if TYPE_CHECKING:
					from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
					from .feature_extraction_whisper import WhisperFeatureExtractor
					from .processing_whisper import WhisperProcessor
					from .tokenization_whisper import WhisperTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_whisper_fast import WhisperTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_whisper import (
										    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WhisperForAudioClassification,
										    WhisperForConditionalGeneration,
										    WhisperModel,
										    WhisperPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_whisper import (
										    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFWhisperForConditionalGeneration,
										    TFWhisperModel,
										    TFWhisperPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_whisper import (
										    FlaxWhisperForAudioClassification,
										    FlaxWhisperForConditionalGeneration,
										    FlaxWhisperModel,
										    FlaxWhisperPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)