


# 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


a_					:    Tuple          =    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)

a_					:    Union[str, Any]          =    {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

a_					:    Optional[int]          =    "zero2"
a_					:    Dict          =    "zero3"
a_					:    Optional[Any]          =    [ZEROa, ZEROa]






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :str		)   ->  Union[str, Any]:




     '''simple docstring'''



     _a			      =							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
a_					:    str          =    list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class  a     (     __lowerCamelCase       ):
 @parameterized.expand(UpperCamelCase_      ,				name_func=UpperCamelCase_						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							List[str]:
      self.run_and_check(
          stage=UpperCamelCase_      ,				model=UpperCamelCase_      ,				distributed=UpperCamelCase_      ,				fpaa=UpperCamelCase_      ,				)
 @require_torch_multi_gpu
 @parameterized.expand(UpperCamelCase_      ,				name_func=UpperCamelCase_						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Dict:
      self.run_and_check(
          stage=UpperCamelCase_      ,				model=UpperCamelCase_      ,				distributed=UpperCamelCase_      ,				fpaa=UpperCamelCase_      ,				)
 @parameterized.expand(UpperCamelCase_      ,				name_func=UpperCamelCase_						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Dict:
      self.run_and_check(
          stage=UpperCamelCase_      ,				model=UpperCamelCase_      ,				distributed=UpperCamelCase_      ,				fpaa=UpperCamelCase_      ,				)
 @require_torch_multi_gpu
 @parameterized.expand(UpperCamelCase_      ,				name_func=UpperCamelCase_						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Dict:
      self.run_and_check(
          stage=UpperCamelCase_      ,				model=UpperCamelCase_      ,				distributed=UpperCamelCase_      ,				fpaa=UpperCamelCase_      ,				)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      # XXX: run_asr is premature and doesn't save any results
      # so all we check for now is that the process didn't fail
      pass
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 10      ,				__magic_name__ = True      ,				__magic_name__ = True      ,				__magic_name__ = True      ,				)					->							Union[str, Any]:
      _a			      =							models[model]

      _a			      =							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 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 10      ,				__magic_name__ = 1      ,				__magic_name__ = True      ,				__magic_name__ = True      ,				)					->							Optional[int]:
      _a			      =							self.get_auto_remove_tmp_dir('./xxx'      ,				after=UpperCamelCase_						)
      _a			      =							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
      _a			      =							f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
      _a			      =							[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
      _a			      =							self.get_launcher(UpperCamelCase_						)

      _a			      =							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 							__UpperCAmelCase		( self      ,				__magic_name__=False						)					->							Union[str, Any]:
      # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
      # - it won't be able to handle that
      # 2. for now testing with just 2 gpus max (since some quality tests may give different
      # results with mode gpus because we use very little data)
      _a			      =							min(2      ,				get_gpu_count()						) if distributed else 1
      return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_					:    str          =    {
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[str]          =    [
     "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecAudioForAudioFrameClassification",
     "Data2VecAudioForCTC",
     "Data2VecAudioForSequenceClassification",
     "Data2VecAudioForXVector",
     "Data2VecAudioModel",
     "Data2VecAudioPreTrainedModel",
 ]
 a_					:    str          =    [
     "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecTextForCausalLM",
     "Data2VecTextForMaskedLM",
     "Data2VecTextForMultipleChoice",
     "Data2VecTextForQuestionAnswering",
     "Data2VecTextForSequenceClassification",
     "Data2VecTextForTokenClassification",
     "Data2VecTextModel",
     "Data2VecTextPreTrainedModel",
 ]
 a_					:    Tuple          =    [
     "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecVisionForImageClassification",
     "Data2VecVisionForMaskedImageModeling",
     "Data2VecVisionForSemanticSegmentation",
     "Data2VecVisionModel",
     "Data2VecVisionPreTrainedModel",
 ]

if is_tf_available():
 a_					:    Dict          =    [
     "TFData2VecVisionForImageClassification",
     "TFData2VecVisionForSemanticSegmentation",
     "TFData2VecVisionModel",
     "TFData2VecVisionPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

 a_					:    List[Any]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

