





'''simple docstring'''





import warnings
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 SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    ["image_processor", "tokenizer"]
					A__				 =    "FlavaImageProcessor"
					A__				 =    ("BertTokenizer", "BertTokenizerFast")





					def __init__( self	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       None
										if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."	,  __A	,  )
															lowerCamelCase__       =       kwargs.pop("feature_extractor"	)

										lowerCamelCase__       =       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__(__A	,  __A	)
										lowerCamelCase__       =       self.image_processor





					def __call__( self	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = True	,  _lowerCAmelCase = False	,  _lowerCAmelCase = False	,  _lowerCAmelCase = None	,  _lowerCAmelCase = 0	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = False	,  _lowerCAmelCase = False	,  _lowerCAmelCase = False	,  _lowerCAmelCase = False	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):

										if text is None and images is None:
															raise ValueError("You have to specify either text or images. Both cannot be none."	)

										if text is not None:
															lowerCamelCase__       =       self.tokenizer(
															    text=__A	,  add_special_tokens=__A	,  padding=__A	,  truncation=__A	,  max_length=__A	,  stride=__A	,  pad_to_multiple_of=__A	,  return_token_type_ids=__A	,  return_attention_mask=__A	,  return_overflowing_tokens=__A	,  return_special_tokens_mask=__A	,  return_offsets_mapping=__A	,  return_length=__A	,  verbose=__A	,  return_tensors=__A	,  **__A	,  )
										if images is not None:
															lowerCamelCase__       =       self.image_processor(
															    __A	,  return_image_mask=__A	,  return_codebook_pixels=__A	,  return_tensors=__A	,  **__A	,  )

										if text is not None and images is not None:
															encoding.update(__A	)
															return encoding
										elif text is not None:
															return encoding
										else:
															return BatchEncoding(data=dict(**__A	)	,  tensor_type=__A	)





					def 					__magic_name__ ( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										return self.tokenizer.batch_decode(*__A	,  **__A	)





					def 					__magic_name__ ( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										return self.tokenizer.decode(*__A	,  **__A	)





					@property
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.tokenizer.model_input_names
										lowerCamelCase__       =       self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





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






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








def    __UpperCamelCase      (    a) ->float:
					return 10 - x * x





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

					lowerCamelCase__       =       a
					while (b - a) >= 0.0_1:
										# Find middle point
										lowerCamelCase__       =       (a + b) / 2
										# Check if middle point is root
										if equation(a) == 0.0:
															break
										# Decide the side to repeat the steps
										if equation(a) * equation(a) < 0:
															lowerCamelCase__       =       c
										else:
															lowerCamelCase__       =       c
					return c


if __name__ == "__main__":
						import doctest

						doctest.testmod()

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

