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 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		["""image_processor""", """tokenizer"""]
						UpperCamelCase_     =		"""BlipImageProcessor"""
						UpperCamelCase_     =		("""BertTokenizer""", """BertTokenizerFast""")


						def __init__(       self	:				List[str]    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							False
												super().__init__(UpperCamelCase__    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Any							=							self.image_processor


						def __call__(       self	:				int    ,		UpperCamelCase__	:				ImageInput = None    ,		UpperCamelCase__	:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,		UpperCamelCase__	:				bool = True    ,		UpperCamelCase__	:				Union[bool, str, PaddingStrategy] = False    ,		UpperCamelCase__	:				Union[bool, str, TruncationStrategy] = None    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				int = 0    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				bool = True    ,		UpperCamelCase__	:				Optional[Union[str, TensorType]] = None    ,		**UpperCamelCase__	:				Any    ,		):





												'''simple docstring'''





												if images is None and text is None:
																		raise ValueError('''You have to specify either images or text.'''						)

												# Get only text
												if images is None:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							self.tokenizer
																		SCREAMING_SNAKE_CASE							:    List[str]							=							self.tokenizer(
																		    text=UpperCamelCase__    ,		add_special_tokens=UpperCamelCase__    ,		padding=UpperCamelCase__    ,		truncation=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		stride=UpperCamelCase__    ,		pad_to_multiple_of=UpperCamelCase__    ,		return_attention_mask=UpperCamelCase__    ,		return_overflowing_tokens=UpperCamelCase__    ,		return_special_tokens_mask=UpperCamelCase__    ,		return_offsets_mapping=UpperCamelCase__    ,		return_token_type_ids=UpperCamelCase__    ,		return_length=UpperCamelCase__    ,		verbose=UpperCamelCase__    ,		return_tensors=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
																		return text_encoding

												# add pixel_values
												SCREAMING_SNAKE_CASE							:    Any							=							self.image_processor(UpperCamelCase__    ,		return_tensors=UpperCamelCase__						)

												if text is not None:
																		SCREAMING_SNAKE_CASE							:    Dict							=							self.tokenizer(
																		    text=UpperCamelCase__    ,		add_special_tokens=UpperCamelCase__    ,		padding=UpperCamelCase__    ,		truncation=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		stride=UpperCamelCase__    ,		pad_to_multiple_of=UpperCamelCase__    ,		return_attention_mask=UpperCamelCase__    ,		return_overflowing_tokens=UpperCamelCase__    ,		return_special_tokens_mask=UpperCamelCase__    ,		return_offsets_mapping=UpperCamelCase__    ,		return_token_type_ids=UpperCamelCase__    ,		return_length=UpperCamelCase__    ,		verbose=UpperCamelCase__    ,		return_tensors=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
												else:
																		SCREAMING_SNAKE_CASE							:    Any							=							None

												if text_encoding is not None:
																		encoding_image_processor.update(UpperCamelCase__						)

												return encoding_image_processor


						def        __A       (       self	:				Tuple    ,		*UpperCamelCase__	:				List[str]    ,		**UpperCamelCase__	:				Dict						):





												'''simple docstring'''





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


						def        __A       (       self	:				Optional[Any]    ,		*UpperCamelCase__	:				List[str]    ,		**UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





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



						@property
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.tokenizer.model_input_names
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)






def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float(moles / volume						) * nfactor						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (volume)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (pressure)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((pressure * volume) / (0.0821 * moles)						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





