"""simple docstring"""





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class        _snake_case   (    UpperCamelCase_						):
						snake_case__	     =	['image_processor', 'tokenizer']
						snake_case__	     =	'AutoImageProcessor'
						snake_case__	     =	'AutoTokenizer'






						def __init__(				self					:	List[Any]							,    UpperCAmelCase					:	Tuple=None							,    UpperCAmelCase					:	List[Any]=None							,    **UpperCAmelCase					:	Optional[int]			):
													__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."							,    lowercase_							,    )
																				__lowerCamelCase    :						Tuple						      =   kwargs.pop("feature_extractor"			)

													__lowerCamelCase    :						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__(lowercase_							,    lowercase_			)
													__lowerCamelCase    :						Any						      =   self.image_processor
													__lowerCamelCase    :						int						      =   False






						def __call__(				self					:	str							,    *UpperCAmelCase					:	Dict							,    **UpperCAmelCase					:	List[str]			):




													if self._in_target_context_manager:
																				return self.current_processor(*lowercase_							,    **lowercase_			)

													__lowerCamelCase    :						List[Any]						      =   kwargs.pop("images"							,    lowercase_			)
													__lowerCamelCase    :						Dict						      =   kwargs.pop("text"							,    lowercase_			)
													if len(lowercase_			) > 0:
																				__lowerCamelCase    :						Optional[int]						      =   args[0]
																				__lowerCamelCase    :						int						      =   args[1:]

													if images is None and text is None:
																				raise ValueError("You need to specify either an `images` or `text` input to process."			)

													if images is not None:
																				__lowerCamelCase    :						List[str]						      =   self.image_processor(lowercase_							,    *lowercase_							,    **lowercase_			)
													if text is not None:
																				__lowerCamelCase    :						Optional[int]						      =   self.tokenizer(lowercase_							,    **lowercase_			)

													if text is None:
																				return inputs
													elif images is None:
																				return encodings
													else:
																				__lowerCamelCase    :						Optional[int]						      =   encodings['input_ids']
																				return inputs






						def 					lowerCamelCase__   (				self					:	str							,    *UpperCAmelCase					:	Union[str, Any]							,    **UpperCAmelCase					:	Dict			):
													return self.tokenizer.batch_decode(*lowercase_							,    **lowercase_			)






						def 					lowerCamelCase__   (				self					:	Union[str, Any]							,    *UpperCAmelCase					:	Optional[int]							,    **UpperCAmelCase					:	List[str]			):
													return self.tokenizer.decode(*lowercase_							,    **lowercase_			)






						@contextmanager
						def 					lowerCamelCase__   (				self					:	Optional[int]			):
													warnings.warn(
													    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
													    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
													    "your images inputs, or in a separate call."			)
													__lowerCamelCase    :						List[Any]						      =   True
													__lowerCamelCase    :						Union[str, Any]						      =   self.tokenizer
													yield
													__lowerCamelCase    :						List[Any]						      =   self.image_processor
													__lowerCamelCase    :						Any						      =   False






						def 					lowerCamelCase__   (				self					:	List[str]							,    UpperCAmelCase					:	List[Any]							,    UpperCAmelCase					:	Optional[int]=False							,    UpperCAmelCase					:	List[Any]=None			):




													if added_vocab is None:
																				__lowerCamelCase    :						int						      =   self.tokenizer.get_added_vocab()

													__lowerCamelCase    :						List[Any]						      =   {}

													while tokens:
																				__lowerCamelCase    :						Union[str, Any]						      =   re.search(r"<s_(.*?)>"							,    lowercase_							,    re.IGNORECASE			)
																				if start_token is None:
																											break
																				__lowerCamelCase    :						List[Any]						      =   start_token.group(1			)
																				__lowerCamelCase    :						Tuple						      =   re.search(rF"""</s_{key}>"""							,    lowercase_							,    re.IGNORECASE			)
																				__lowerCamelCase    :						Optional[int]						      =   start_token.group()
																				if end_token is None:
																											__lowerCamelCase    :						Tuple						      =   tokens.replace(lowercase_							,    ""			)
																				else:
																											__lowerCamelCase    :						Dict						      =   end_token.group()
																											__lowerCamelCase    :						List[Any]						      =   re.escape(lowercase_			)
																											__lowerCamelCase    :						Union[str, Any]						      =   re.escape(lowercase_			)
																											__lowerCamelCase    :						Optional[int]						      =   re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""							,    lowercase_							,    re.IGNORECASE			)
																											if content is not None:
																																		__lowerCamelCase    :						Union[str, Any]						      =   content.group(1			).strip()
																																		if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																									__lowerCamelCase    :						str						      =   self.tokenajson(lowercase_							,    is_inner_value=lowercase_							,    added_vocab=lowercase_			)
																																									if value:
																																																if len(lowercase_			) == 1:
																																																							__lowerCamelCase    :						str						      =   value[0]
																																																__lowerCamelCase    :						str						      =   value
																																		else:  # leaf nodes
																																									__lowerCamelCase    :						str						      =   []
																																									for leaf in content.split(r"<sep/>"			):
																																																__lowerCamelCase    :						Optional[Any]						      =   leaf.strip()
																																																if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																							__lowerCamelCase    :						Union[str, Any]						      =   leaf[1:-2]  # for categorical special tokens
																																																output[key].append(lowercase_			)
																																									if len(output[key]			) == 1:
																																																__lowerCamelCase    :						List[str]						      =   output[key][0]

																											__lowerCamelCase    :						List[Any]						      =   tokens[tokens.find(lowercase_			) + len(lowercase_			) :].strip()
																											if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																		return [output] + self.tokenajson(tokens[6:]							,    is_inner_value=lowercase_							,    added_vocab=lowercase_			)

													if len(lowercase_			):
																				return [output] if is_inner_value else output
													else:
																				return [] if is_inner_value else {"text_sequence": tokens}






						@property
						def 					lowerCamelCase__   (				self					:	int			):
													warnings.warn(
													    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,    lowercase_							,    )
													return self.image_processor_class







						@property
						def 					lowerCamelCase__   (				self					:	int			):
													warnings.warn(
													    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,    lowercase_							,    )
													return self.image_processor



import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowerCamelCase											=       logging.getLogger()




@unittest.skip('Temporarily disable the doc tests.'     )
@require_torch
@require_tf
@slow
class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Path			,   lowercase_						:     Union[str, None] = None			,   lowercase_						:     Union[List[str], None] = None			,   lowercase_						:     Union[str, List[str], None] = None			,   lowercase_						:     bool = True			,   )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=[file for file in os.listdir(lowercase_ ) if os.path.isfile(os.path.join(lowercase_			,   lowercase_ ) )]

             if identifier is not None:
                    _lowerCamelCase       :		Any		=[file for file in files if identifier in file]

             if n_identifier is not None:
                    if isinstance(lowercase_			,   lowercase_ ):
                           for n_ in n_identifier:
                                  _lowerCamelCase       :		Any		=[file for file in files if n_ not in file]
                    else:
                           _lowerCamelCase       :		List[Any]		=[file for file in files if n_identifier not in file]

             _lowerCamelCase       :		List[Any]		=ignore_files or []
             ignore_files.append('__init__.py' )
             _lowerCamelCase       :		List[Any]		=[file for file in files if file not in ignore_files]

             for file in files:
                    # Open all files
                    print('Testing'			,   lowercase_ )

                    if only_modules:
                           _lowerCamelCase       :		Optional[int]		=file.split('.' )[0]
                           try:
                                  _lowerCamelCase       :		Any		=getattr(lowercase_			,   lowercase_ )
                                  _lowerCamelCase       :		Optional[int]		=doctest.DocTestSuite(lowercase_ )
                                  _lowerCamelCase       :		List[Any]		=unittest.TextTestRunner().run(lowercase_ )
                                  self.assertIs(len(result.failures )			,   0 )
                           except AttributeError:
                                  logger.info(F'''{module_identifier} is not a module.''' )
                    else:
                           _lowerCamelCase       :		int		=doctest.testfile(str('..' / directory / file )			,   optionflags=doctest.ELLIPSIS )
                           self.assertIs(result.failed			,   0 )






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		int		=Path('src/transformers' )
             _lowerCamelCase       :		Optional[Any]		='modeling'
             _lowerCamelCase       :		List[str]		=[
                 'modeling_ctrl.py',
                 'modeling_tf_ctrl.py',
             ]
             self.analyze_directory(lowercase_			,   identifier=lowercase_			,   ignore_files=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=Path('src/transformers' )
             _lowerCamelCase       :		Union[str, Any]		='tokenization'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('src/transformers' )
             _lowerCamelCase       :		Any		='configuration'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=Path('src/transformers' )
             _lowerCamelCase       :		int		=['configuration', 'modeling', 'tokenization']
             self.analyze_directory(lowercase_			,   n_identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('docs/source' )
             _lowerCamelCase       :		Dict		=['favicon.ico']
             self.analyze_directory(lowercase_			,   ignore_files=lowercase_			,   only_modules=lowercase_ )


