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 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						["""image_processor""", """tokenizer"""]
 UpperCAmelCase							    =						"""FlavaImageProcessor"""
 UpperCAmelCase							    =						("""BertTokenizer""", """BertTokenizerFast""")


 def __init__(						self		: Dict			,						lowerCAmelCase__		: Any=None			,						lowerCAmelCase__		: Dict=None			,						**lowerCAmelCase__		: Any    )    ->      str:
   UpperCAmelCase					      =  None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."			,						__UpperCamelCase			,						)
     UpperCAmelCase					      =  kwargs.pop("feature_extractor"    )

   UpperCAmelCase					      =  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__(__UpperCamelCase			,						__UpperCamelCase    )
   UpperCAmelCase					      =  self.image_processor


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

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

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


 def _UpperCamelCase	(						self		: Any			,						*lowerCAmelCase__		: Optional[int]			,						**lowerCAmelCase__		: Union[str, Any]    )    ->      Optional[int]:
   return self.tokenizer.batch_decode(*__UpperCamelCase			,						**__UpperCamelCase    )


 def _UpperCamelCase	(						self		: Optional[Any]			,						*lowerCAmelCase__		: Any			,						**lowerCAmelCase__		: Optional[Any]    )    ->      Any:
   return self.tokenizer.decode(*__UpperCamelCase			,						**__UpperCamelCase    )


 @property
 def _UpperCamelCase	(						self		: str    )    ->      Any:
   UpperCAmelCase					      =  self.tokenizer.model_input_names
   UpperCAmelCase					      =  self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )


 @property
 def _UpperCamelCase	(						self		: int    )    ->      Optional[Any]:
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,						__UpperCamelCase			,						)
   return self.image_processor_class


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Union[str, Any]:
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,						__UpperCamelCase			,						)
   return self.image_processor






import numpy

# List of input, output pairs
lowerCAmelCase__       = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase__       = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__       = [2, 4, 1, 5]
lowerCAmelCase__       = len(train_data)
lowerCAmelCase__       = 0.0_0_9
def      _lowerCAmelCase(	__A   ,       __A="train"			):
  return calculate_hypothesis_value(__A   ,       __A			) - output(
      __A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  for i in range(len(__A			) - 1			):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _lowerCAmelCase(	__A   ,       __A			):
  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      _lowerCAmelCase(	__A   ,       __A=m			):
  UpperCAmelCase					      =  0
  for i in range(__A			):
    if index == -1:
      summation_value += _error(__A			)
    else:
      summation_value += _error(__A			) * train_data[i][0][index]
  return summation_value
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  summation_of_cost_derivative(__A   ,       __A			) / m
  return cost_derivative_value
def      _lowerCAmelCase(	):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  UpperCAmelCase					      =  0.000002
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  0
  while True:
    j += 1
    UpperCAmelCase					      =  [0, 0, 0, 0]
    for i in range(0   ,       len(__A			)			):
      UpperCAmelCase					      =  get_cost_derivative(i - 1			)
      UpperCAmelCase					      =  (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        __A   ,       __A   ,       atol=__A   ,       rtol=__A   ,       ):
      break
    UpperCAmelCase					      =  temp_parameter_vector
  print(("Number of iterations:", j)			)
def      _lowerCAmelCase(	):
  for i in range(len(__A			)			):
    print(("Actual output value:", output(__A   ,       "test"			))			)
    print(("Hypothesis output:", calculate_hypothesis_value(__A   ,       "test"			))			)


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



