"""simple docstring"""







import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Tuple  )							->      Tuple:

							'''simple docstring'''





							if isinstance(lowerCamelCase__      ,							lowerCamelCase__  ):
														__snake_case      :							Tuple						=			np.full((len(lowerCamelCase__  ), sequence_length, 2)      ,							lowerCamelCase__  )
							else:
														__snake_case      :							Optional[Any]						=			np.full((len(lowerCamelCase__  ), sequence_length)      ,							lowerCamelCase__  )

							for i, tensor in enumerate(lowerCamelCase__  ):
														if padding_side == "right":
																					if isinstance(lowerCamelCase__      ,							lowerCamelCase__  ):
																												__snake_case      :							Union[str, Any]						=			tensor[:sequence_length]
																					else:
																												__snake_case      :							str						=			tensor[:sequence_length]
														else:
																					if isinstance(lowerCamelCase__      ,							lowerCamelCase__  ):
																												__snake_case      :							str						=			tensor[:sequence_length]
																					else:
																												__snake_case      :							Union[str, Any]						=			tensor[:sequence_length]

							return out_tensor.tolist()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]  )							->      Any:

							'''simple docstring'''





							__snake_case      :							int						=			ord(lowerCamelCase__  )
							if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
														return True
							__snake_case      :							List[Any]						=			unicodedata.category(lowerCamelCase__  )
							if cat.startswith('P'  ):
														return True
							return False

@dataclass
class 		UpperCamelCase      (   a__						):
				UpperCAmelCase    :				Optional[int]   =		42
				UpperCAmelCase    :				List[Any]   =		True
				UpperCAmelCase    :				Union[str, Any]   =		None
				UpperCAmelCase    :				Any   =		None
				UpperCAmelCase    :				Union[str, Any]   =		-100
				UpperCAmelCase    :				Union[str, Any]   =		"""pt"""



				def  _lowercase					(self    :				Tuple     ,  _A    :				List[Any])   ->     Optional[Any]:
											import torch

											__snake_case      :							Any						=			'''label''' if '''label''' in features[0].keys() else '''labels'''
											__snake_case      :							int						=			[feature[label_name] for feature in features] if label_name in features[0].keys() else None
											__snake_case      :							List[str]						=			self.tokenizer.pad(
											    _lowerCamelCase     ,  padding=self.padding     ,  max_length=self.max_length     ,  pad_to_multiple_of=self.pad_to_multiple_of     ,  return_tensors='pt' if labels is None else None     ,  )

											if labels is None:
																		return batch

											__snake_case      :							Dict						=			torch.tensor(batch['entity_ids']).shape[1]
											__snake_case      :							List[str]						=			self.tokenizer.padding_side
											if padding_side == "right":
																		__snake_case      :							Dict						=			[
																		    list(_lowerCamelCase) + [self.label_pad_token_id] * (sequence_length - len(_lowerCamelCase)) for label in labels
																		]
											else:
																		__snake_case      :							str						=			[
																		    [self.label_pad_token_id] * (sequence_length - len(_lowerCamelCase)) + list(_lowerCamelCase) for label in labels
																		]

											__snake_case      :							List[Any]						=			[feature['''ner_tags'''] for feature in features]
											__snake_case      :							Dict						=			padding_tensor(_lowerCamelCase     ,  -1     ,  _lowerCamelCase     ,  _lowerCamelCase)
											__snake_case      :							Any						=			[feature['''original_entity_spans'''] for feature in features]
											__snake_case      :							Dict						=			padding_tensor(_lowerCamelCase     ,  (-1, -1)     ,  _lowerCamelCase     ,  _lowerCamelCase)
											__snake_case      :							Union[str, Any]						=			{k: torch.tensor(_lowerCamelCase     ,  dtype=torch.intaa) for k, v in batch.items()}

											return batch






"""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()





