




'''simple docstring'''




import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


UpperCAmelCase_				 =					"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

UpperCAmelCase_				 =					"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

UpperCAmelCase_				 =					"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,     _KWARGS_DESCRIPTION      )
class   lowerCAmelCase_      (       datasets.Metric      ):







  '''simple docstring'''







  def 							SCREAMING_SNAKE_CASE__	(  self			:		List[str]       ):






   """simple docstring"""






   return datasets.MetricInfo(
       description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
           {
               """predictions""": datasets.Sequence(datasets.Value("""string"""  ,						id="""token"""       )  ,						id="""sequence"""       ),
               """references""": datasets.Sequence(
                   datasets.Sequence(datasets.Value("""string"""  ,						id="""token"""       )  ,						id="""sequence"""       )  ,						id="""references"""       ),
           }       )  ,						codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]  ,						reference_urls=[
           """https://en.wikipedia.org/wiki/BLEU""",
           """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
       ]  ,						)



  def 							SCREAMING_SNAKE_CASE__	(  self			:		Tuple  ,						_UpperCAmelCase			:		Any  ,						_UpperCAmelCase			:		List[Any]  ,						_UpperCAmelCase			:		Any=4  ,						_UpperCAmelCase			:		Tuple=False       ):






   """simple docstring"""






   UpperCAmelCase__								=			compute_bleu(
       reference_corpus=_lowerCAmelCase  ,						translation_corpus=_lowerCAmelCase  ,						max_order=_lowerCAmelCase  ,						smooth=_lowerCAmelCase       )
   (UpperCAmelCase__)								=			score
   return {
       "bleu": bleu,
       "precisions": precisions,
       "brevity_penalty": bp,
       "length_ratio": ratio,
       "translation_length": translation_length,
       "reference_length": reference_length,
   }






import numpy

# List of input, output pairs
_UpperCAmelCase		:  List[str]	=   (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_UpperCAmelCase		:  Optional[Any]	=   (((515, 22, 13), 555), ((61, 35, 49), 150))
_UpperCAmelCase		:  Tuple	=   [2, 4, 1, 5]
_UpperCAmelCase		:  Union[str, Any]	=   len(train_data)
_UpperCAmelCase		:  Dict	=   0.0_0_9



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase="train"      ):
  return calculate_hypothesis_value(lowerCamelCase,	lowerCamelCase      ) - output(
      lowerCamelCase,	lowerCamelCase      )



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:str							=  0
  for i in range(len(lowerCamelCase      ) - 1      ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  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__     (   lowerCamelCase,	lowerCamelCase=m      ):
  lowercase			:Union[str, Any]							=  0
  for i in range(lowerCamelCase      ):
    if index == -1:
      summation_value += _error(lowerCamelCase      )
    else:
      summation_value += _error(lowerCamelCase      ) * train_data[i][0][index]
  return summation_value



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:int							=  summation_of_cost_derivative(lowerCamelCase,	lowerCamelCase      ) / m
  return cost_derivative_value



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



def 					UpperCAmelCase__     (   ):
  for i in range(len(lowerCamelCase      )      ):
    print(("Actual output value:", output(lowerCamelCase,	"test"      ))      )
    print(("Hypothesis output:", calculate_hypothesis_value(lowerCamelCase,	"test"      ))      )


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



