




import os
from collections import deque

import torch
from torch.utils.data import Dataset







class 						lowercase							(   lowerCAmelCase__		):

     def __init__(  self		,     _a=""		,     _a="train"				)						->							Tuple:
           assert os.path.isdir(__a				)

           _A							:     List[str]			=						[]
           _A							:     int			=						os.listdir(__a				)
           for story_filename in story_filenames_list:
                 if "summary" in story_filename:
                       continue
                 _A							:     List[str]			=						os.path.join(__a		,     __a				)
                 if not os.path.isfile(__a				):
                       continue
                 self.documents.append(__a				)

     def __len__(  self				)						->							Optional[Any]:
           return len(self.documents				)



     def __getitem__(  self		,     _a				)						->							str:
           _A							:     int			=						self.documents[idx]
           _A							:     List[Any]			=						document_path.split("""/"""				)[-1]
           with open(__a		,     encoding="""utf-8"""				) as source:
                 _A							:     Dict			=						source.read()
                 _A			,   _A							:     Any			=						process_story(__a				)
           return document_name, story_lines, summary_lines







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Any			=						list(filter(lambda snake_case_     : len(_UpperCamelCase     ) != 0,[line.strip() for line in raw_story.split("""\n"""     )]     )     )

      # for some unknown reason some lines miss a period, add it
      _A							:     Any			=						[_add_missing_period(_UpperCamelCase     ) for line in nonempty_lines]

      # gather article lines
      _A							:     int			=						[]
      _A							:     Any			=						deque(_UpperCamelCase     )
      while True:
            try:
                  _A							:     str			=						lines.popleft()
                  if element.startswith("""@highlight"""     ):
                        break
                  story_lines.append(_UpperCamelCase     )
            except IndexError:
                  # if "@highlight" is absent from the file we pop
                  # all elements until there is None, raising an exception.
                  return story_lines, []

    # gather summary lines
      _A							:     str			=						list(filter(lambda snake_case_     : not t.startswith("""@highlight"""     ),_UpperCamelCase     )     )

      return story_lines, summary_lines







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Dict			=						[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
      if line.startswith("""@highlight"""     ):
            return line
      if line[-1] in END_TOKENS:
            return line
      return line + "."







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):




      if len(_UpperCamelCase     ) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(_UpperCamelCase     ))     )
            return sequence







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Union[str, Any]			=						torch.ones_like(_UpperCamelCase     )
      _A							:     Optional[Any]			=						sequence == pad_token_id
      _A							:     int			=						0
      return mask







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     List[Any]			=						[tokenizer.encode(_UpperCamelCase     ) for line in story_lines]
      _A							:     Dict			=						[token for sentence in story_lines_token_ids for token in sentence]
      _A							:     int			=						[tokenizer.encode(_UpperCamelCase     ) for line in summary_lines]
      _A							:     Dict			=						[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[Any]			=						[]
      for sequence in batch:
            _A							:     Optional[Any]			=						-1
            _A							:     Any			=						[]
            for s in sequence:
                  if s == separator_token_id:
                        sentence_num += 1
                  embeddings.append(sentence_num % 2     )
            batch_embeddings.append(_UpperCamelCase     )
      return torch.tensor(_UpperCamelCase     )





"""simple docstring"""


import numpy

# List of input, output pairs
A     :   Any			    =			(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
A     :   str			    =			(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A     :   Union[str, Any]			    =			[2, 4, 1, 5]
A     :   int			    =			len(train_data)
A     :   Dict			    =			0.009


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase="train"      ):

			'''simple docstring'''




			return calculate_hypothesis_value(_UpperCamelCase    , _UpperCamelCase      ) - output(
			    _UpperCamelCase    , _UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=m      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			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 _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			summation_of_cost_derivative(_UpperCamelCase    , _UpperCamelCase      ) / m
			return cost_derivative_value


def _lowerCamelCase	(		):

			'''simple docstring'''




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


def _lowerCamelCase	(		):

			'''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()




