import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						__magic_name__    (       __lowerCamelCase    ):
 UpperCAmelCase							    =						['''image_processor''', '''tokenizer''']
 UpperCAmelCase							    =						'''LayoutLMv3ImageProcessor'''
 UpperCAmelCase							    =						('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')


 def __init__(						self		: int			,						lowerCAmelCase__		: Tuple=None			,						lowerCAmelCase__		: Dict=None			,						**lowerCAmelCase__		: Union[str, Any]    )    ->      Optional[Any]:
   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."			,						a_			,						)
     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__(a_			,						a_    )


 def __call__(						self		: Dict			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: Optional[Any] = None			,						lowerCAmelCase__		: List[Any] = None			,						lowerCAmelCase__		: Union[str, Any] = None			,						lowerCAmelCase__		: int = None			,						lowerCAmelCase__		: List[Any] = True			,						lowerCAmelCase__		: Optional[Any] = False			,						lowerCAmelCase__		: List[str] = None			,						lowerCAmelCase__		: Union[str, Any] = None			,						lowerCAmelCase__		: Tuple = 0			,						lowerCAmelCase__		: Dict = None			,						lowerCAmelCase__		: Optional[int] = None			,						lowerCAmelCase__		: Optional[Any] = None			,						lowerCAmelCase__		: int = False			,						lowerCAmelCase__		: int = False			,						lowerCAmelCase__		: Dict = False			,						lowerCAmelCase__		: Union[str, Any] = False			,						lowerCAmelCase__		: int = True			,						lowerCAmelCase__		: str = None			,						**lowerCAmelCase__		: str			,						)    ->      List[str]:
   # verify input
   if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."    )

   if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."    )

   # first, apply the image processor
   UpperCAmelCase					      =  self.image_processor(images=a_			,						return_tensors=a_    )

   # second, apply the tokenizer
   if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(a_			,						a_    ):
       UpperCAmelCase					      =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
     UpperCAmelCase					      =  features["words"]

   UpperCAmelCase					      =  self.tokenizer(
       text=text if text is not None else features["words"]			,						text_pair=text_pair if text_pair is not None else None			,						boxes=boxes if boxes is not None else features["boxes"]			,						word_labels=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_			,						)

   # add pixel values
   UpperCAmelCase					      =  features.pop("pixel_values"    )
   if return_overflowing_tokens is True:
     UpperCAmelCase					      =  self.get_overflowing_images(a_			,						encoded_inputs["overflow_to_sample_mapping"]    )
   UpperCAmelCase					      =  images

   return encoded_inputs


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Any    )    ->      Tuple:
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   UpperCAmelCase					      =  []
   for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]    )

   if len(a_    ) != len(a_    ):
     raise ValueError(
         "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
         f" {len(a_    )} and {len(a_    )}"    )

   return images_with_overflow


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


 def _UpperCamelCase	(						self		: Optional[int]			,						*lowerCAmelCase__		: str			,						**lowerCAmelCase__		: int    )    ->      Optional[int]:
   return self.tokenizer.decode(*a_			,						**a_    )


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      Dict:
   return ["input_ids", "bbox", "attention_mask", "pixel_values"]


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



