





# 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(42)

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

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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:Tuple   )      ->   Optional[int]:
 # 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					:	Union[str, Any]      =       parameterized.to_safe_name("""_""".join(str(SCREAMING_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        snake_case_    (		__lowercase						):






  @parameterized.expand(_snake_case       ,  name_func=_snake_case      )
  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					int       ,  _snake_case			:					Optional[int]      )->Dict:






   '''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 				UpperCAmelCase__						(							self			:					int       ,  _snake_case			:					Dict       ,  _snake_case			:					int      )->Optional[Any]:






   '''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 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					str       ,  _snake_case			:					List[Any]      )->Optional[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 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					Dict       ,  _snake_case			:					List[str]      )->int:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					Any      )->Any:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					str       ,  _snake_case			:					str       ,  _snake_case			:					int = 10       ,  _snake_case			:					bool = True       ,  _snake_case			:					bool = True       ,  _snake_case			:					bool = True       ,  )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       models[model]

   __lowerCAmelCase					:	List[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 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					str       ,  _snake_case			:					str       ,  _snake_case			:					int = 10       ,  _snake_case			:					int = 1       ,  _snake_case			:					bool = True       ,  _snake_case			:					bool = True       ,  )->str:






   '''simple docstring'''

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

   __lowerCAmelCase					:	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 				UpperCAmelCase__						(							self			:					int       ,  _snake_case			:					Tuple=False      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       min(2       ,  get_gpu_count()      ) if distributed else 1
   return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        snake_case_    (		__lowercase						):
  A_      							=   ['image_processor', 'tokenizer']
  A_      							=   'ChineseCLIPImageProcessor'
  A_      							=   ('BertTokenizer', 'BertTokenizerFast')






  def __init__(							self			:					Optional[Any]       ,  _snake_case			:					List[Any]=None       ,  _snake_case			:					str=None       ,  **_snake_case			:					int      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       None
   if "feature_extractor" in kwargs:
    warnings.warn(
        """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
        """ instead."""       ,  _snake_case       ,  )
    __lowerCAmelCase					:	List[str]      =       kwargs.pop("""feature_extractor"""      )

   __lowerCAmelCase					:	Union[str, Any]      =       image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError("""You need to specify an `image_processor`."""      )
   if tokenizer is None:
    raise ValueError("""You need to specify a `tokenizer`."""      )

   super().__init__(_snake_case       ,  _snake_case      )
   __lowerCAmelCase					:	Any      =       self.image_processor






  def __call__(							self			:					Optional[Any]       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					List[str]=None       ,  **_snake_case			:					Any      )->Union[str, Any]:






   '''simple docstring'''

   if text is None and images is None:
    raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

   if text is not None:
    __lowerCAmelCase					:	List[Any]      =       self.tokenizer(_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )

   if images is not None:
    __lowerCAmelCase					:	Tuple      =       self.image_processor(_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )

   if text is not None and images is not None:
    __lowerCAmelCase					:	Optional[Any]      =       image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**_snake_case      )       ,  tensor_type=_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  *_snake_case			:					Union[str, Any]       ,  **_snake_case			:					int      )->Optional[Any]:






   '''simple docstring'''

   return self.tokenizer.batch_decode(*_snake_case       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  *_snake_case			:					Dict       ,  **_snake_case			:					Any      )->Dict:






   '''simple docstring'''

   return self.tokenizer.decode(*_snake_case       ,  **_snake_case      )






  @property
  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.tokenizer.model_input_names
   __lowerCAmelCase					:	Tuple      =       self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->Union[str, Any]:






   '''simple docstring'''

   warnings.warn(
       """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,  _snake_case       ,  )
   return self.image_processor_class