
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       _lowerCAmelCase   (      __a			):
				_lowercase     						=['''image_processor''', '''tokenizer''']
				_lowercase     						='''BlipImageProcessor'''
				_lowercase     						='''AutoTokenizer'''




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Optional[int]:
							lowerCAmelCase_         =      False
							super().__init__(_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      self.image_processor




				def __call__(   self							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = True							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = 0							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = True							,							_UpperCamelCase = None							,							**_UpperCamelCase							,							)							-> BatchEncoding:
							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:
										lowerCAmelCase_         =      self.tokenizer
										lowerCAmelCase_         =      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
							lowerCAmelCase_         =      self.image_processor(_UpperCamelCase							,							return_tensors=_UpperCamelCase	)

							if text is not None:
										lowerCAmelCase_         =      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:
										lowerCAmelCase_         =      None

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

							return encoding_image_processor




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> Any:
							return self.tokenizer.batch_decode(*_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> Any:
							return self.tokenizer.decode(*_UpperCamelCase							,							**_UpperCamelCase	)




				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      self.tokenizer.model_input_names
							lowerCAmelCase_         =      self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





from collections.abc import Callable






def   lowerCamelCase__							(							__lowerCAmelCase    :    Callable[[float], float]       ,				__lowerCAmelCase    :    float       ,				__lowerCAmelCase    :    float							):




			"""simple docstring"""


			lowerCAmelCase_         =      a
			lowerCAmelCase_         =      b
			if function(__lowerCAmelCase							) == 0:  # one of the a or b is a root for the function
						return a
			elif function(__lowerCAmelCase							) == 0:
						return b
			elif (
			    function(__lowerCAmelCase							) * function(__lowerCAmelCase							) > 0
			):  # if none of these are root and they are both positive or negative,
						# then this algorithm can't find the root
						raise ValueError("could not find root in given interval."							)
			else:
						lowerCAmelCase_         =      start + (end - start) / 2.0
						while abs(start - mid							) > 10**-7:  # until precisely equals to 10^-7
									if function(__lowerCAmelCase							) == 0:
												return mid
									elif function(__lowerCAmelCase							) * function(__lowerCAmelCase							) < 0:
												lowerCAmelCase_         =      mid
									else:
												lowerCAmelCase_         =      mid
									lowerCAmelCase_         =      start + (end - start) / 2.0
						return mid






def   lowerCamelCase__							(							__lowerCAmelCase    :    float							):




			"""simple docstring"""


			return x**3 - 2 * x - 5


if __name__ == "__main__":
			print(bisection(f, 1, 10_00))

			import doctest

			doctest.testmod()



