

# 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 			UpperCAmelCase__				(			_A      : Optional[int]	,      _A      : str	,      _A      : List[str]							):





  '''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
lowerCamelCase           =					list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   @parameterized.expand(lowercase_,    name_func=lowercase_       )
   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    Dict:






     """simple docstring"""






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



   @require_torch_multi_gpu
   @parameterized.expand(lowercase_,    name_func=lowercase_       )
   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    Optional[int]:






     """simple docstring"""






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



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






     """simple docstring"""






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



   @require_torch_multi_gpu
   @parameterized.expand(lowercase_,    name_func=lowercase_       )
   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    Tuple:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    lowercase_       )							->    str:






     """simple docstring"""






     pass



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_ = 10,    lowercase_ = True,    lowercase_ = True,    lowercase_ = True,    )							->    Tuple:






     """simple docstring"""






     a__  =models[model]

     a__  =self.run_trainer(
         stage=lowercase_,    model_name=lowercase_,    eval_steps=lowercase_,    num_train_epochs=1,    distributed=lowercase_,    fpaa=lowercase_,    )

     self.do_checks(lowercase_       )

     return output_dir



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_ = 10,    lowercase_ = 1,    lowercase_ = True,    lowercase_ = True,    )							->    Optional[int]:






     """simple docstring"""






     a__  =self.get_auto_remove_tmp_dir('''./xxx''',    after=lowercase_       )
     a__  =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(lowercase_       )}
            --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
     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(lowercase_       )

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

     return output_dir



   def  _UpperCAmelCase (				self,    lowercase_=False       )							->    Dict:






     """simple docstring"""






     a__  =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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase           =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''DebertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaForMaskedLM''',
           '''DebertaForQuestionAnswering''',
           '''DebertaForSequenceClassification''',
           '''DebertaForTokenClassification''',
           '''DebertaModel''',
           '''DebertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaForMaskedLM''',
           '''TFDebertaForQuestionAnswering''',
           '''TFDebertaForSequenceClassification''',
           '''TFDebertaForTokenClassification''',
           '''TFDebertaModel''',
           '''TFDebertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
