



'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Union[str, Any]		      =		['''image_processor''', '''tokenizer''']
       UpperCamelCase__						:					List[str]		      =		'''FlavaImageProcessor'''
       UpperCamelCase__						:					str		      =		('''BertTokenizer''', '''BertTokenizerFast''')



       def __init__(	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=None  ,	__SCREAMING_SNAKE_CASE							:  Tuple=None  ,	**__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

           __a		 =       None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''  ,	__SCREAMING_SNAKE_CASE  ,	)
               __a		 =       kwargs.pop('''feature_extractor''')

           __a		 =       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__(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
           __a		 =       self.image_processor



       def __call__(	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[ImageInput] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Union[bool, str, PaddingStrategy] = False  ,	__SCREAMING_SNAKE_CASE							:  Union[bool, str, TruncationStrategy] = False  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	__SCREAMING_SNAKE_CASE							:  int = 0  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[bool] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[bool] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[bool] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[bool] = None  ,	__SCREAMING_SNAKE_CASE							:  bool = False  ,	__SCREAMING_SNAKE_CASE							:  bool = False  ,	__SCREAMING_SNAKE_CASE							:  bool = False  ,	__SCREAMING_SNAKE_CASE							:  bool = False  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, TensorType]] = None  ,	**__SCREAMING_SNAKE_CASE							:  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:
               __a		 =       self.tokenizer(
                   text=__SCREAMING_SNAKE_CASE  ,	add_special_tokens=__SCREAMING_SNAKE_CASE  ,	padding=__SCREAMING_SNAKE_CASE  ,	truncation=__SCREAMING_SNAKE_CASE  ,	max_length=__SCREAMING_SNAKE_CASE  ,	stride=__SCREAMING_SNAKE_CASE  ,	pad_to_multiple_of=__SCREAMING_SNAKE_CASE  ,	return_token_type_ids=__SCREAMING_SNAKE_CASE  ,	return_attention_mask=__SCREAMING_SNAKE_CASE  ,	return_overflowing_tokens=__SCREAMING_SNAKE_CASE  ,	return_special_tokens_mask=__SCREAMING_SNAKE_CASE  ,	return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,	return_length=__SCREAMING_SNAKE_CASE  ,	verbose=__SCREAMING_SNAKE_CASE  ,	return_tensors=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE  ,	)
           if images is not None:
               __a		 =       self.image_processor(
                   __SCREAMING_SNAKE_CASE  ,	return_image_mask=__SCREAMING_SNAKE_CASE  ,	return_codebook_pixels=__SCREAMING_SNAKE_CASE  ,	return_tensors=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE  ,	)

           if text is not None and images is not None:
               encoding.update(__SCREAMING_SNAKE_CASE)
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE)  ,	tensor_type=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  List[Any]  ,	*__SCREAMING_SNAKE_CASE							:  List[str]  ,	**__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	*__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	**__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

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



       @property
       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       self.tokenizer.model_input_names
           __a		 =       self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



       @property
       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

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





       @property
       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           warnings.warn(
               '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,	__SCREAMING_SNAKE_CASE  ,	)
           return self.image_processor










# 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


__snake_case		:Union[str, 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(42)

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

__snake_case		:List[Any]   	=  '''zero2'''
__snake_case		:Optional[Any]   	=  '''zero3'''
__snake_case		:str   	=  [ZEROa, ZEROa]





def   __snake_case			(    _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
    __a		 =       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
__snake_case		:List[Any]   	=  list(itertools.product(stages, models.keys()))

@slow
@require_deepspeed
@require_torch_gpu
class 					_A (			__UpperCAmelCase					):



       @parameterized.expand(__SCREAMING_SNAKE_CASE  ,	name_func=__SCREAMING_SNAKE_CASE)
       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

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



       @require_torch_multi_gpu
       @parameterized.expand(__SCREAMING_SNAKE_CASE  ,	name_func=__SCREAMING_SNAKE_CASE)
       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

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



       @parameterized.expand(__SCREAMING_SNAKE_CASE  ,	name_func=__SCREAMING_SNAKE_CASE)
       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

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



       @require_torch_multi_gpu
       @parameterized.expand(__SCREAMING_SNAKE_CASE  ,	name_func=__SCREAMING_SNAKE_CASE)
       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[str]):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  int = 10  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           __a		 =       models[model]

           __a		 =       self.run_trainer(
               stage=__SCREAMING_SNAKE_CASE  ,	model_name=__SCREAMING_SNAKE_CASE  ,	eval_steps=__SCREAMING_SNAKE_CASE  ,	num_train_epochs=1  ,	distributed=__SCREAMING_SNAKE_CASE  ,	fpaa=__SCREAMING_SNAKE_CASE  ,	)

           self.do_checks(__SCREAMING_SNAKE_CASE)

           return output_dir



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  int = 10  ,	__SCREAMING_SNAKE_CASE							:  int = 1  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           __a		 =       self.get_auto_remove_tmp_dir('''./xxx'''  ,	after=__SCREAMING_SNAKE_CASE)
           __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(__SCREAMING_SNAKE_CASE)}\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(__SCREAMING_SNAKE_CASE)

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

           return output_dir





       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=False):


           '''simple docstring'''

           __a		 =       min(2  ,	get_gpu_count()) if distributed else 1
           return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()





