


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 		__a				(    UpperCAmelCase      ):
		_a     :  int           =     ['image_processor', 'tokenizer']
		_a     :  List[Any]           =     'BlipImageProcessor'
		_a     :  Any           =     ('BertTokenizer', 'BertTokenizerFast')
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      False
									super().__init__(__UpperCAmelCase      ,   __UpperCAmelCase						)
									_UpperCAmelCase					=      self.image_processor
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 0      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	BatchEncoding:




									"""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:
																_UpperCAmelCase					=      self.tokenizer
																_UpperCAmelCase					=      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
									_UpperCAmelCase					=      self.image_processor(__UpperCAmelCase      ,   return_tensors=__UpperCAmelCase						)

									if text is not None:
																_UpperCAmelCase					=      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:
																_UpperCAmelCase					=      None

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

									return encoding_image_processor
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*__UpperCAmelCase      ,   **__UpperCAmelCase						)
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





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






		@property
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





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




import math
import random

def 	lowerCAmelCase__			(      a__:		float      ,  a__:		bool = False	)  ->		float:


							'''simple docstring'''





							if deriv:
														return value * (1 - value)
							return 1 / (1 + math.exp(-value	))


# Initial Value
lowerCAmelCase__  :Optional[Any]             =							0.02

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      float(2 * (random.randint(1      ,  1_0_0	)) - 1	)

							for _ in range(a__	):
														# Forward propagation
														_UpperCAmelCase					=      sigmoid_function(INITIAL_VALUE * weight	)
														# How much did we miss?
														_UpperCAmelCase					=      (expected / 1_0_0) - layer_a
														# Error delta
														_UpperCAmelCase					=      layer_1_error * sigmoid_function(a__      ,  a__	)
														# Update weight
														weight += INITIAL_VALUE * layer_1_delta

							return layer_a * 1_0_0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase__  :List[Any]             =							int(input('''Expected value: '''))
		lowerCAmelCase__  :Any             =							int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
