






'''simple docstring'''




from __future__ import annotations

from typing import Any







class    A_	:
       """simple docstring"""




       def __init__(		self			:Union[str, Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:float = 0				)				->  None:


          '''simple docstring'''




          snake_case_				:					str   =       row, column
          snake_case_				:					Dict   =       [[default_value for c in range(lowerCAmelCase__				)] for r in range(lowerCAmelCase__				)]

       def __str__(		self			:Optional[Any]				)				->  str:


          '''simple docstring'''




          snake_case_				:					str   =       F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

          # Make string identifier
          snake_case_				:					Any   =       0
          for row_vector in self.array:
             for obj in row_vector:
                snake_case_				:					Dict   =       max(lowerCAmelCase__       ,							len(str(lowerCAmelCase__				)				)				)
          snake_case_				:					str   =       F'''%{max_element_length}s'''

          # Make string and return
          def single_line(lowerCAmelCase__			:list[float]				) -> str:
             nonlocal string_format_identifier
             snake_case_				:					Tuple   =       "["
             line += ", ".join(string_format_identifier % (obj,) for obj in row_vector				)
             line += "]"
             return line

          s += "\n".join(single_line(lowerCAmelCase__				) for row_vector in self.array				)
          return s

       def __repr__(		self			:Tuple				)				->  str:


          '''simple docstring'''




          return str(self				)

       def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:tuple[int, int]				)				->  bool:


          '''simple docstring'''




          if not (isinstance(lowerCAmelCase__       ,							(list, tuple)				) and len(lowerCAmelCase__				) == 2):
             return False
          elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
             return False
          else:
             return True

       def __getitem__(		self			:Optional[Any]       ,							lowerCAmelCase__			:tuple[int, int]				)				->  Any:


          '''simple docstring'''




          assert self.validate_indicies(lowerCAmelCase__				)
          return self.array[loc[0]][loc[1]]

       def __setitem__(		self			:Tuple       ,							lowerCAmelCase__			:tuple[int, int]       ,							lowerCAmelCase__			:float				)				->  None:


          '''simple docstring'''




          assert self.validate_indicies(lowerCAmelCase__				)
          snake_case_				:					Any   =       value

       def __add__(		self			:Dict       ,							lowerCAmelCase__			:Matrix				)				->  Matrix:


          '''simple docstring'''




          assert isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
          assert self.row == another.row and self.column == another.column

          # Add
          snake_case_				:					str   =       Matrix(self.row       ,							self.column				)
          for r in range(self.row				):
             for c in range(self.column				):
                snake_case_				:					Optional[int]   =       self[r, c] + another[r, c]
          return result

       def __neg__(		self			:int				)				->  Matrix:


          '''simple docstring'''




          snake_case_				:					Union[str, Any]   =       Matrix(self.row       ,							self.column				)
          for r in range(self.row				):
             for c in range(self.column				):
                snake_case_				:					Any   =       -self[r, c]
          return result

       def __sub__(		self			:Dict       ,							lowerCAmelCase__			:Matrix				)				->  Matrix:


          '''simple docstring'''




          return self + (-another)

       def __mul__(		self			:Optional[int]       ,							lowerCAmelCase__			:int | float | Matrix				)				->  Matrix:


          '''simple docstring'''




          if isinstance(lowerCAmelCase__       ,							(int, float)				):  # Scalar multiplication
             snake_case_				:					List[Any]   =       Matrix(self.row       ,							self.column				)
             for r in range(self.row				):
                for c in range(self.column				):
                   snake_case_				:					Dict   =       self[r, c] * another
             return result
          elif isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				):  # Matrix multiplication
             assert self.column == another.row
             snake_case_				:					Any   =       Matrix(self.row       ,							another.column				)
             for r in range(self.row				):
                for c in range(another.column				):
                   for i in range(self.column				):
                      result[r, c] += self[r, i] * another[i, c]
             return result
          else:
             snake_case_				:					List[Any]   =       F'''Unsupported type given for another ({type(lowerCAmelCase__				)})'''
             raise TypeError(lowerCAmelCase__				)

       def 							_A	(		self			:Optional[int]				)				->  Matrix:


          '''simple docstring'''




          snake_case_				:					str   =       Matrix(self.column       ,							self.row				)
          for r in range(self.row				):
             for c in range(self.column				):
                snake_case_				:					Union[str, Any]   =       self[r, c]
          return result






       def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:Matrix       ,							lowerCAmelCase__			:Matrix				)				->  Any:


          '''simple docstring'''




          assert isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				) and isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
          assert self.row == self.column == u.row == v.row  # u, v should be column vector
          assert u.column == v.column == 1  # u, v should be column vector

          # Calculate
          snake_case_				:					Dict   =       v.transpose()
          snake_case_				:					int   =       (v_t * self * u)[0, 0] + 1
          if numerator_factor == 0:
             return None  # It's not invertable
          return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":

  def 	__UpperCAmelCase       ( )->    None:



     """simple docstring"""



     snake_case_				:					Dict   =       Matrix(3     ,3     ,0						)
     for i in range(3						):
        snake_case_				:					Any   =       1
     print(F'''a^(-1) is {ainv}'''						)
     # u, v
     snake_case_				:					int   =       Matrix(3     ,1     ,0						)
     snake_case_				:					Optional[Any]   =       1, 2, -3
     snake_case_				:					Dict   =       Matrix(3     ,1     ,0						)
     snake_case_				:					Optional[Any]   =       4, -2, 5
     print(F'''u is {u}'''						)
     print(F'''v is {v}'''						)
     print(F'''uv^T is {u * v.transpose()}'''						)
     # Sherman Morrison
     print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__magic_name__     ,__magic_name__						)}'''						)

  def 	__UpperCAmelCase       ( )->    None:



     """simple docstring"""



     import doctest

     doctest.testmod()






  testa()












'''simple docstring'''




import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__lowerCamelCase   :					str   							=					'''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__lowerCamelCase   :					Dict   							=					'''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

__lowerCamelCase   :					int   							=					'''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION							)
class    A_	(datasets.Metric							):
							"""simple docstring"""




							def 							_A	(		self			:Optional[Any]				)				->  Optional[int]:


										'''simple docstring'''




										if version.parse(scb.__version__				) < version.parse("1.4.12"				):
													raise ImportWarning(
													    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
													    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."				)
										return datasets.MetricInfo(
										    description=_DESCRIPTION       ,							citation=_CITATION       ,							homepage="http://www.cs.umd.edu/~snover/tercom/"       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
										        {
										            "predictions": datasets.Value("string"       ,							id="sequence"				),
										            "references": datasets.Sequence(datasets.Value("string"       ,							id="sequence"				)       ,							id="references"				),
										        }				)       ,							codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]       ,							reference_urls=[
										        "https://github.com/jhclark/tercom",
										    ]       ,							)






							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:bool = False       ,							lowerCAmelCase__			:bool = False       ,							lowerCAmelCase__			:bool = False       ,							lowerCAmelCase__			:bool = False       ,							)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       len(references[0]				)
										if any(len(lowerCAmelCase__				) != references_per_prediction for refs in references				):
													raise ValueError("Sacrebleu requires the same number of references for each prediction"				)
										snake_case_				:					List[str]   =       [[refs[i] for refs in references] for i in range(lowerCAmelCase__				)]

										snake_case_				:					List[str]   =       TER(
										    normalized=lowerCAmelCase__       ,							no_punct=lowerCAmelCase__       ,							asian_support=lowerCAmelCase__       ,							case_sensitive=lowerCAmelCase__       ,							)
										snake_case_				:					Any   =       sb_ter.corpus_score(lowerCAmelCase__       ,							lowerCAmelCase__				)

										return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}




