"""simple docstring"""







from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Tuple   =		"""philschmid/bart-large-cnn-samsum"""
				UpperCAmelCase    :				str   =		(
				    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
				    """and returns a summary of the text."""
				)
				UpperCAmelCase    :				List[str]   =		"""summarizer"""
				UpperCAmelCase    :				List[Any]   =		AutoTokenizer
				UpperCAmelCase    :				Any   =		AutoModelForSeqaSeqLM

				UpperCAmelCase    :				Any   =		["""text"""]
				UpperCAmelCase    :				Optional[Any]   =		["""text"""]



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				List[str])   ->     Dict:
											return self.pre_processor(_A     ,  return_tensors='pt'     ,  truncation=_A)



				def  _lowercase					(self    :				List[str]     ,  _A    :				Dict)   ->     int:
											return self.model.generate(**_A)[0]






				def  _lowercase					(self    :				Optional[int]     ,  _A    :				int)   ->     Optional[Any]:
											return self.pre_processor.decode(_A     ,  skip_special_tokens=_A     ,  clean_up_tokenization_spaces=_A)






"""simple docstring"""







import numpy

# List of input, output pairs
_a					:					Optional[int]=			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a					:					int=			(((515, 22, 13), 555), ((61, 35, 49), 150))
_a					:					List[Any]=			[2, 4, 1, 5]
_a					:					Optional[int]=			len(train_data)
_a					:					str=			0.0_0_9
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    List[str]="train"  )							->      int:

	'''simple docstring'''





	return calculate_hypothesis_value(UpperCAmelCase_      ,							UpperCAmelCase_  ) - output(
	    UpperCAmelCase_      ,							UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Tuple  )							->      Dict:

	'''simple docstring'''





	__snake_case      :							int						=			0
	for i in range(len(UpperCAmelCase_  ) - 1  ):
		hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
	hyp_val += parameter_vector[0]
	return hyp_val
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    List[Any]  )							->      List[Any]:

	'''simple docstring'''





	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Optional[int]  )							->      Any:

	'''simple docstring'''





	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    (   UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Any=m  )							->      Union[str, Any]:

	'''simple docstring'''





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

	'''simple docstring'''





	__snake_case      :							Dict						=			summation_of_cost_derivative(UpperCAmelCase_      ,							UpperCAmelCase_  ) / m
	return cost_derivative_value
def   __UpperCAmelCase    (   )							->      List[str]:

	'''simple docstring'''





	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	__snake_case      :							Dict						=			0.000_002
	__snake_case      :							Optional[int]						=			0
	__snake_case      :							str						=			0
	while True:
		j += 1
		__snake_case      :							Optional[int]						=			[0, 0, 0, 0]
		for i in range(0      ,							len(UpperCAmelCase_  )  ):
			__snake_case      :							Union[str, Any]						=			get_cost_derivative(i - 1  )
			__snake_case      :							Any						=			(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    UpperCAmelCase_      ,							UpperCAmelCase_      ,							atol=UpperCAmelCase_      ,							rtol=UpperCAmelCase_      ,							):
			break
		__snake_case      :							Optional[Any]						=			temp_parameter_vector
	print(('Number of iterations:', j)  )
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	for i in range(len(UpperCAmelCase_  )  ):
		print(('Actual output value:', output(UpperCAmelCase_      ,							'test'  ))  )
		print(('Hypothesis output:', calculate_hypothesis_value(UpperCAmelCase_      ,							'test'  ))  )


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





