

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    SqueezeBertTokenizer
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    SqueezeBertTokenizerFast
			SCREAMING_SNAKE_CASE					:       Any						      =    True







			def     snake_case__(					self			:    Optional[Any]							,       **_UpperCamelCase			:    Union[str, Any]							)				->Optional[Any]:
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,       **_UpperCamelCase							)







			@slow
			def     snake_case__(					self			:    Tuple							)				->Any:
									snake_case_       =     SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''							)

									snake_case_       =     tokenizer.encode('''sequence builders'''							,       add_special_tokens=_UpperCamelCase							)
									snake_case_       =     tokenizer.encode('''multi-sequence build'''							,       add_special_tokens=_UpperCamelCase							)

									snake_case_       =     tokenizer.build_inputs_with_special_tokens(_UpperCamelCase							)
									snake_case_       =     tokenizer.build_inputs_with_special_tokens(_UpperCamelCase							,       _UpperCamelCase							)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    ["image_processor", "tokenizer"]
			SCREAMING_SNAKE_CASE					:       List[Any]						      =    "AutoImageProcessor"
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    "AutoTokenizer"







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    List[str]=None							,       _UpperCamelCase			:    Any=None							,       **_UpperCamelCase			:    Tuple							)				->str:
									snake_case_       =     None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.'''							,       _UpperCamelCase							,       )
															snake_case_       =     kwargs.pop('''feature_extractor'''							)

									snake_case_       =     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__(_UpperCamelCase							,       _UpperCamelCase							)
									snake_case_       =     self.image_processor
									snake_case_       =     False







			def __call__(					self			:    int							,       *_UpperCamelCase			:    Tuple							,       **_UpperCamelCase			:    Any							)				->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,       **_UpperCamelCase							)

									snake_case_       =     kwargs.pop('''images'''							,       _UpperCamelCase							)
									snake_case_       =     kwargs.pop('''text'''							,       _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_       =     args[0]
															snake_case_       =     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:
															snake_case_       =     self.image_processor(_UpperCamelCase							,       *_UpperCamelCase							,       **_UpperCamelCase							)
									if text is not None:
															snake_case_       =     self.tokenizer(_UpperCamelCase							,       **_UpperCamelCase							)

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







			def     snake_case__(					self			:    List[Any]							,       *_UpperCamelCase			:    Union[str, Any]							,       **_UpperCamelCase			:    Tuple							)				->Any:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			def     snake_case__(					self			:    Optional[Any]							,       *_UpperCamelCase			:    List[Any]							,       **_UpperCamelCase			:    Union[str, Any]							)				->List[str]:
									return self.tokenizer.decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			@contextmanager
			def     snake_case__(					self			:    Any							)				->Any:
									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.'''							)
									snake_case_       =     True
									snake_case_       =     self.tokenizer
									yield
									snake_case_       =     self.image_processor
									snake_case_       =     False







			@property
			def     snake_case__(					self			:    Optional[int]							)				->int:
									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor_class







			@property
			def     snake_case__(					self			:    Union[str, Any]							)				->List[Any]:
									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor