'''simple docstring'''






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 (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					["""image_processor""", """tokenizer"""]
  __lowerCAmelCase				      =					"""BlipImageProcessor"""
  __lowerCAmelCase				      =					("""BertTokenizer""", """BertTokenizerFast""")







  def __init__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= False
    super().__init__(snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					Any		= self.image_processor







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

    '''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:					int		= self.tokenizer
      __UpperCAmelCase:					Dict		= self.tokenizer(
          text=snake_case_   ,    add_special_tokens=snake_case_   ,    padding=snake_case_   ,    truncation=snake_case_   ,    max_length=snake_case_   ,    stride=snake_case_   ,    pad_to_multiple_of=snake_case_   ,    return_attention_mask=snake_case_   ,    return_overflowing_tokens=snake_case_   ,    return_special_tokens_mask=snake_case_   ,    return_offsets_mapping=snake_case_   ,    return_token_type_ids=snake_case_   ,    return_length=snake_case_   ,    verbose=snake_case_   ,    return_tensors=snake_case_   ,    **snake_case_   ,    )
      return text_encoding

    # add pixel_values
    __UpperCAmelCase:					Any		= self.image_processor(snake_case_   ,    return_tensors=snake_case_					)

    if text is not None:
      __UpperCAmelCase:					Any		= self.tokenizer(
          text=snake_case_   ,    add_special_tokens=snake_case_   ,    padding=snake_case_   ,    truncation=snake_case_   ,    max_length=snake_case_   ,    stride=snake_case_   ,    pad_to_multiple_of=snake_case_   ,    return_attention_mask=snake_case_   ,    return_overflowing_tokens=snake_case_   ,    return_special_tokens_mask=snake_case_   ,    return_offsets_mapping=snake_case_   ,    return_token_type_ids=snake_case_   ,    return_length=snake_case_   ,    verbose=snake_case_   ,    return_tensors=snake_case_   ,    **snake_case_   ,    )
    else:
      __UpperCAmelCase:					Union[str, Any]		= None

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

    return encoding_image_processor







  def    lowercase_  (							self   ,    *snake_case_   ,    **snake_case_					):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    *snake_case_   ,    **snake_case_					):

    '''simple docstring'''







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







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.tokenizer.model_input_names
    __UpperCAmelCase:					List[Any]		= self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)
'''simple docstring'''






import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_	   =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_	   =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_	   =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_	   =			len(train_data)
SCREAMING_SNAKE_CASE_	   =			0.009






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]="train"     )							->	int:
  return calculate_hypothesis_value(_lowercase   ,    _lowercase     ) - output(
      _lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				Dict     )							->	Optional[Any]:
  __UpperCAmelCase:					List[Any]		= 0
  for i in range(len(_lowercase     ) - 1     ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Optional[Any]:
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None






def  UpperCamelCase__					(					_lowercase    :				Dict   ,    _lowercase    :				str     )							->	Dict:
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]     )
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]     )
  return None






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Union[str, Any]=m     )							->	Optional[Any]:
  __UpperCAmelCase:					int		= 0
  for i in range(_lowercase     ):
    if index == -1:
      summation_value += _error(_lowercase     )
    else:
      summation_value += _error(_lowercase     ) * train_data[i][0][index]
  return summation_value






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	Optional[int]:
  __UpperCAmelCase:					Any		= summation_of_cost_derivative(_lowercase   ,    _lowercase     ) / m
  return cost_derivative_value






def  UpperCamelCase__					(					)							->	Tuple:
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  __UpperCAmelCase:					str		= 0.00_00_02
  __UpperCAmelCase:					List[Any]		= 0
  __UpperCAmelCase:					Optional[Any]		= 0
  while True:
    j += 1
    __UpperCAmelCase:					Any		= [0, 0, 0, 0]
    for i in range(0   ,    len(_lowercase     )     ):
      __UpperCAmelCase:					str		= get_cost_derivative(i - 1     )
      __UpperCAmelCase:					List[str]		= (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        _lowercase   ,    _lowercase   ,    atol=_lowercase   ,    rtol=_lowercase   ,    ):
      break
    __UpperCAmelCase:					List[Any]		= temp_parameter_vector
  print(("""Number of iterations:""", j)     )






def  UpperCamelCase__					(					)							->	Dict:
  for i in range(len(_lowercase     )     ):
    print(("""Actual output value:""", output(_lowercase   ,    """test"""     ))     )
    print(("""Hypothesis output:""", calculate_hypothesis_value(_lowercase   ,    """test"""     ))     )


if __name__ == "__main__":
    run_gradient_descent()
    print('\nTesting gradient descent for a linear hypothesis function.\n')
    test_gradient_descent()