'''simple docstring'''






import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					XLMTokenizer
  __lowerCAmelCase				      =					False







  def    lowercase_  (							self					):

    '''simple docstring'''







    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __UpperCAmelCase:					Optional[Any]		= [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """w</w>""",
        """r</w>""",
        """t</w>""",
        """lo""",
        """low""",
        """er</w>""",
        """low</w>""",
        """lowest</w>""",
        """newer</w>""",
        """wider</w>""",
        """<unk>""",
    ]
    __UpperCAmelCase:					Any		= dict(zip(snake_case_   ,    range(len(snake_case_					)					)					)					)
    __UpperCAmelCase:					Union[str, Any]		= ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

    __UpperCAmelCase:					Any		= os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["""vocab_file"""]					)
    __UpperCAmelCase:					Optional[Any]		= os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["""merges_file"""]					)
    with open(self.vocab_file   ,    """w"""					) as fp:
      fp.write(json.dumps(snake_case_					)					)
    with open(self.merges_file   ,    """w"""					) as fp:
      fp.write("""\n""".join(snake_case_					)					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= """lower newer"""
    __UpperCAmelCase:					Dict		= """lower newer"""
    return input_text, output_text







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= XLMTokenizer(self.vocab_file   ,    self.merges_file					)

    __UpperCAmelCase:					Dict		= """lower"""
    __UpperCAmelCase:					List[Any]		= ["""low""", """er</w>"""]
    __UpperCAmelCase:					List[Any]		= tokenizer.tokenize(snake_case_					)
    self.assertListEqual(snake_case_   ,    snake_case_					)

    __UpperCAmelCase:					int		= tokens + ["""<unk>"""]
    __UpperCAmelCase:					Dict		= [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_					)   ,    snake_case_					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""					)

    __UpperCAmelCase:					List[Any]		= tokenizer.encode("""sequence builders"""   ,    add_special_tokens=snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= tokenizer.encode("""multi-sequence build"""   ,    add_special_tokens=snake_case_					)

    __UpperCAmelCase:					Union[str, Any]		= tokenizer.build_inputs_with_special_tokens(snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= tokenizer.build_inputs_with_special_tokens(snake_case_   ,    snake_case_					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]
'''simple docstring'''






import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_	   =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_	   =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_	   =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_	   =			len(train_data)
SCREAMING_SNAKE_CASE_	   =			0.009






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]="train"     )							->	int:
  return calculate_hypothesis_value(_lowercase   ,    _lowercase     ) - output(
      _lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				Dict     )							->	Optional[Any]:
  __UpperCAmelCase:					List[Any]		= 0
  for i in range(len(_lowercase     ) - 1     ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Optional[Any]:
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None






def  UpperCamelCase__					(					_lowercase    :				Dict   ,    _lowercase    :				str     )							->	Dict:
  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__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Union[str, Any]=m     )							->	Optional[Any]:
  __UpperCAmelCase:					int		= 0
  for i in range(_lowercase     ):
    if index == -1:
      summation_value += _error(_lowercase     )
    else:
      summation_value += _error(_lowercase     ) * train_data[i][0][index]
  return summation_value






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	Optional[int]:
  __UpperCAmelCase:					Any		= summation_of_cost_derivative(_lowercase   ,    _lowercase     ) / m
  return cost_derivative_value






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






def  UpperCamelCase__					(					)							->	Dict:
  for i in range(len(_lowercase     )     ):
    print(("""Actual output value:""", output(_lowercase   ,    """test"""     ))     )
    print(("""Hypothesis output:""", calculate_hypothesis_value(_lowercase   ,    """test"""     ))     )


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