






import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:List[str] =							['image_processor', 'tokenizer']
	__snake_case						:Dict =							'BlipImageProcessor'
	__snake_case						:Any =							'AutoTokenizer'







	def __init__(	self   :				Optional[int]							,					_lowerCAmelCase   :				Optional[Any]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				Optional[Any]      )		->			Tuple:
		"""simple docstring"""







		super().__init__(_lowerCAmelCase							,					_lowerCAmelCase      )

		# add QFormer tokenizer
		__lowercase   							=	qformer_tokenizer







	def __call__(	self   :				List[str]							,					_lowerCAmelCase   :				ImageInput = None							,					_lowerCAmelCase   :				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,					_lowerCAmelCase   :				bool = True							,					_lowerCAmelCase   :				Union[bool, str, PaddingStrategy] = False							,					_lowerCAmelCase   :				Union[bool, str, TruncationStrategy] = None							,					_lowerCAmelCase   :				Optional[int] = None							,					_lowerCAmelCase   :				int = 0							,					_lowerCAmelCase   :				Optional[int] = None							,					_lowerCAmelCase   :				Optional[bool] = None							,					_lowerCAmelCase   :				bool = False							,					_lowerCAmelCase   :				bool = False							,					_lowerCAmelCase   :				bool = False							,					_lowerCAmelCase   :				bool = False							,					_lowerCAmelCase   :				bool = False							,					_lowerCAmelCase   :				bool = True							,					_lowerCAmelCase   :				Optional[Union[str, TensorType]] = None							,					**_lowerCAmelCase   :				List[Any]							,					)		->			BatchFeature:
		"""simple docstring"""







		if images is None and text is None:
			raise ValueError("""You have to specify at least images or text."""      )

		__lowercase   							=	BatchFeature()

		if text is not None:
			__lowercase   							=	self.tokenizer(
			    text=_lowerCAmelCase							,					add_special_tokens=_lowerCAmelCase							,					padding=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					max_length=_lowerCAmelCase							,					stride=_lowerCAmelCase							,					pad_to_multiple_of=_lowerCAmelCase							,					return_attention_mask=_lowerCAmelCase							,					return_overflowing_tokens=_lowerCAmelCase							,					return_special_tokens_mask=_lowerCAmelCase							,					return_offsets_mapping=_lowerCAmelCase							,					return_token_type_ids=_lowerCAmelCase							,					return_length=_lowerCAmelCase							,					verbose=_lowerCAmelCase							,					return_tensors=_lowerCAmelCase							,					**_lowerCAmelCase							,					)
			encoding.update(_lowerCAmelCase      )
			__lowercase   							=	self.qformer_tokenizer(
			    text=_lowerCAmelCase							,					add_special_tokens=_lowerCAmelCase							,					padding=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					max_length=_lowerCAmelCase							,					stride=_lowerCAmelCase							,					pad_to_multiple_of=_lowerCAmelCase							,					return_attention_mask=_lowerCAmelCase							,					return_overflowing_tokens=_lowerCAmelCase							,					return_special_tokens_mask=_lowerCAmelCase							,					return_offsets_mapping=_lowerCAmelCase							,					return_token_type_ids=_lowerCAmelCase							,					return_length=_lowerCAmelCase							,					verbose=_lowerCAmelCase							,					return_tensors=_lowerCAmelCase							,					**_lowerCAmelCase							,					)
			__lowercase   							=	qformer_text_encoding.pop("""input_ids"""      )
			__lowercase   							=	qformer_text_encoding.pop("""attention_mask"""      )

		if images is not None:
			__lowercase   							=	self.image_processor(_lowerCAmelCase							,					return_tensors=_lowerCAmelCase      )
			encoding.update(_lowerCAmelCase      )

		return encoding







	def 			_a (	self   :				Optional[int]							,					*_lowerCAmelCase   :				Optional[int]							,					**_lowerCAmelCase   :				int      )		->			Union[str, Any]:
		"""simple docstring"""







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







	def 			_a (	self   :				List[Any]							,					*_lowerCAmelCase   :				List[str]							,					**_lowerCAmelCase   :				Union[str, Any]      )		->			Any:
		"""simple docstring"""







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







	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def 			_a (	self   :				Tuple      )		->			Union[str, Any]:
		"""simple docstring"""







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







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				str							,					**_lowerCAmelCase   :				List[str]      )		->			List[str]:
		"""simple docstring"""







		if os.path.isfile(_lowerCAmelCase      ):
			raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'      )
		os.makedirs(_lowerCAmelCase							,					exist_ok=_lowerCAmelCase      )
		__lowercase   							=	os.path.join(_lowerCAmelCase							,					"""qformer_tokenizer"""      )
		self.qformer_tokenizer.save_pretrained(_lowerCAmelCase      )
		return super().save_pretrained(_lowerCAmelCase							,					**_lowerCAmelCase      )







	@classmethod
	def 			_a (	cls   :				Any							,					_lowerCAmelCase   :				Optional[Any]							,					**_lowerCAmelCase   :				Tuple      )		->			int:
		"""simple docstring"""







		__lowercase   							=	AutoTokenizer.from_pretrained(_lowerCAmelCase							,					subfolder="""qformer_tokenizer"""      )
		__lowercase   							=	cls._get_arguments_from_pretrained(_lowerCAmelCase							,					**_lowerCAmelCase      )
		args.append(_lowerCAmelCase      )
		return cls(*_lowerCAmelCase      )




"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						float:
 return 1_0 - x * x



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 # Bolzano theory in order to find if there is a root between a and b
 if equation(lowercase__   ) * equation(lowercase__   ) >= 0:
  raise ValueError("Wrong space!"   )

 lowerCAmelCase__   :				Union[str, Any] 			=				a
 while (b - a) >= 0.01:
  # Find middle point
  lowerCAmelCase__   :				int 			=				(a + b) / 2
  # Check if middle point is root
  if equation(lowercase__   ) == 0.0:
   break
  # Decide the side to repeat the steps
  if equation(lowercase__   ) * equation(lowercase__   ) < 0:
   lowerCAmelCase__   :				str 			=				c
  else:
   lowerCAmelCase__   :				List[str] 			=				c
 return c


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(bisection(-2, 5))
    print(bisection(0, 6))






