





'''simple docstring'''






from __future__ import annotations




class  _A :

		def __init__(   self					:   Optional[int]  ,      __magic_name__					:   list[list[int]]			)					->    str:


					"""simple docstring"""

					__snake_case					:      str							      =     TypeError(
					    """Matrices must be formed from a list of zero or more lists containing at """
					    """least one and the same number of values, each of which must be of type """
					    """int or float."""			)
					if len(__magic_name__			) != 0:
								__snake_case					:      List[str]							      =     len(rows[0]			)
								if cols == 0:
											raise error
								for row in rows:
											if len(__magic_name__			) != cols:
														raise error
											for value in row:
														if not isinstance(__magic_name__  ,      (int, float)			):
																	raise error
								__snake_case					:      Any							      =     rows
					else:
								__snake_case					:      List[Any]							      =     []

		def 	lowercase__     (   self					:   int			)					->    list[list[int]]:


					"""simple docstring"""

					return [[row[i] for row in self.rows] for i in range(len(self.rows[0]			)			)]

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					return len(self.rows			)

		@property
		def 	lowercase__     (   self					:   Any			)					->    int:


					"""simple docstring"""

					return len(self.rows[0]			)

		@property
		def 	lowercase__     (   self					:   str			)					->    tuple[int, int]:


					"""simple docstring"""

					return (self.num_rows, self.num_columns)

		@property
		def 	lowercase__     (   self					:   Optional[int]			)					->    bool:


					"""simple docstring"""

					return self.order[0] == self.order[1]

		def 	lowercase__     (   self					:   List[str]			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     [
					    [0 if column_num != row_num else 1 for column_num in range(self.num_rows			)]
					    for row_num in range(self.num_rows			)
					]
					return Matrix(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    int:


					"""simple docstring"""

					if not self.is_square:
								return 0
					if self.order == (0, 0):
								return 1
					if self.order == (1, 1):
								return int(self.rows[0][0]			)
					if self.order == (2, 2):
								return int(
								    (self.rows[0][0] * self.rows[1][1])
								    - (self.rows[0][1] * self.rows[1][0])			)
					else:
								return sum(
								    self.rows[0][column] * self.cofactors().rows[0][column]
								    for column in range(self.num_columns			)			)

		def 	lowercase__     (   self					:   Any			)					->    bool:


					"""simple docstring"""

					return bool(self.determinant()			)

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    int:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     [
					    [
					        self.rows[other_row][other_column]
					        for other_column in range(self.num_columns			)
					        if other_column != column
					    ]
					    for other_row in range(self.num_rows			)
					    if other_row != row
					]
					return Matrix(__magic_name__			).determinant()

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    int:


					"""simple docstring"""

					if (row + column) % 2 == 0:
								return self.get_minor(__magic_name__  ,      __magic_name__			)
					return -1 * self.get_minor(__magic_name__  ,      __magic_name__			)

		def 	lowercase__     (   self					:   int			)					->    Matrix:


					"""simple docstring"""

					return Matrix(
					    [
					        [self.get_minor(__magic_name__  ,      __magic_name__			) for column in range(self.num_columns			)]
					        for row in range(self.num_rows			)
					    ]			)

		def 	lowercase__     (   self					:   str			)					->    Matrix:


					"""simple docstring"""

					return Matrix(
					    [
					        [
					            self.minors().rows[row][column]
					            if (row + column) % 2 == 0
					            else self.minors().rows[row][column] * -1
					            for column in range(self.minors().num_columns			)
					        ]
					        for row in range(self.minors().num_rows			)
					    ]			)

		def 	lowercase__     (   self					:   List[str]			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     [
					    [self.cofactors().rows[column][row] for column in range(self.num_columns			)]
					    for row in range(self.num_rows			)
					]
					return Matrix(__magic_name__			)

		def 	lowercase__     (   self					:   Any			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.determinant()
					if not determinant:
								raise TypeError("""Only matrices with a non-zero determinant have an inverse"""			)
					return self.adjugate() * (1 / determinant)

		def __repr__(   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					return str(self.rows			)

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


					"""simple docstring"""

					if self.num_rows == 0:
								return "[]"
					if self.num_rows == 1:
								return "[[" + ". ".join(str(self.rows[0]			)			) + "]]"
					return (
					    "["
					    + "\n ".join(
					        [
					            """[""" + """. """.join([str(__magic_name__			) for value in row]			) + """.]"""
					            for row in self.rows
					        ]			)
					    + "]"
					)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   list[int]  ,      __magic_name__					:   int | None = None			)					->    None:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     TypeError("""Row must be a list containing all ints and/or floats"""			)
					if not isinstance(__magic_name__  ,      __magic_name__			):
								raise type_error
					for value in row:
								if not isinstance(__magic_name__  ,      (int, float)			):
											raise type_error
					if len(__magic_name__			) != self.num_columns:
								raise ValueError(
								    """Row must be equal in length to the other rows in the matrix"""			)
					if position is None:
								self.rows.append(__magic_name__			)
					else:
								__snake_case					:      Optional[Any]							      =     self.rows[0:position] + [row] + self.rows[position:]

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   list[int]  ,      __magic_name__					:   int | None = None			)					->    None:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     TypeError(
					    """Column must be a list containing all ints and/or floats"""			)
					if not isinstance(__magic_name__  ,      __magic_name__			):
								raise type_error
					for value in column:
								if not isinstance(__magic_name__  ,      (int, float)			):
											raise type_error
					if len(__magic_name__			) != self.num_rows:
								raise ValueError(
								    """Column must be equal in length to the other columns in the matrix"""			)
					if position is None:
								__snake_case					:      str							      =     [self.rows[i] + [column[i]] for i in range(self.num_rows			)]
					else:
								__snake_case					:      List[Any]							      =     [
								    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
								    for i in range(self.num_rows			)
								]

		def __eq__(   self					:   Tuple  ,      __magic_name__					:   object			)					->    bool:


					"""simple docstring"""

					if not isinstance(__magic_name__  ,      __magic_name__			):
								return NotImplemented
					return self.rows == other.rows

		def __ne__(   self					:   List[Any]  ,      __magic_name__					:   object			)					->    bool:


					"""simple docstring"""

					return not self == other

		def __neg__(   self					:   Dict			)					->    Matrix:


					"""simple docstring"""

					return self * -1

		def __add__(   self					:   List[Any]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					if self.order != other.order:
								raise ValueError("""Addition requires matrices of the same order"""			)
					return Matrix(
					    [
					        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns			)]
					        for i in range(self.num_rows			)
					    ]			)

		def __sub__(   self					:   Optional[Any]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					if self.order != other.order:
								raise ValueError("""Subtraction requires matrices of the same order"""			)
					return Matrix(
					    [
					        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns			)]
					        for i in range(self.num_rows			)
					    ]			)

		def __mul__(   self					:   Union[str, Any]  ,      __magic_name__					:   Matrix | int | float			)					->    Matrix:


					"""simple docstring"""

					if isinstance(__magic_name__  ,      (int, float)			):
								return Matrix(
								    [[int(element * other			) for element in row] for row in self.rows]			)
					elif isinstance(__magic_name__  ,      __magic_name__			):
								if self.num_columns != other.num_rows:
											raise ValueError(
											    """The number of columns in the first matrix must """
											    """be equal to the number of rows in the second"""			)
								return Matrix(
								    [
								        [Matrix.dot_product(__magic_name__  ,      __magic_name__			) for column in other.columns()]
								        for row in self.rows
								    ]			)
					else:
								raise TypeError(
								    """A Matrix can only be multiplied by an int, float, or another matrix"""			)

		def __pow__(   self					:   Tuple  ,      __magic_name__					:   int			)					->    Matrix:


					"""simple docstring"""

					if not isinstance(__magic_name__  ,      __magic_name__			):
								raise TypeError("""A Matrix can only be raised to the power of an int"""			)
					if not self.is_square:
								raise ValueError("""Only square matrices can be raised to a power"""			)
					if other == 0:
								return self.identity()
					if other < 0:
								if self.is_invertable():
											return self.inverse() ** (-other)
								raise ValueError(
								    """Only invertable matrices can be raised to a negative power"""			)
					__snake_case					:      int							      =     self
					for _ in range(other - 1			):
								result *= self
					return result

		@classmethod
		def 	lowercase__     (   cls					:   Dict  ,      __magic_name__					:   list[int]  ,      __magic_name__					:   list[int]			)					->    int:


					"""simple docstring"""

					return sum(row[i] * column[i] for i in range(len(__magic_name__			)			)			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()



'''simple docstring'''







import comet  # From: unbabel-comet
import torch

import datasets


UpperCamelCase_      	=	datasets.logging.get_logger(__name__)

UpperCamelCase_      	=	'''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

UpperCamelCase_      	=	'''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

UpperCamelCase_      	=	'''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''




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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,							citation=_CITATION							,							homepage='https://unbabel.github.io/COMET/html/index.html'							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
													        {
													            'sources': datasets.Value('string'							,							id='sequence'      ),
													            'predictions': datasets.Value('string'							,							id='sequence'      ),
													            'references': datasets.Value('string'							,							id='sequence'      ),
													        }      )							,							codebase_urls=['https://github.com/Unbabel/COMET']							,							reference_urls=[
													        'https://github.com/Unbabel/COMET',
													        'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
													        'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
													    ]							,							)




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       List[str]      )     ->				Dict:
													if self.config_name == "default":
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'      )      )
													else:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model(self.config_name      )      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Optional[Any]=False      )     ->				Any:
													if gpus is None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = 1 if torch.cuda.is_available() else 0
													SCREAMING_SNAKE_CASE__     :Dict						    = {'src': sources, 'mt': predictions, 'ref': references}
													SCREAMING_SNAKE_CASE__     :List[str]						    = [dict(zip(UpperCamelCase_							,							UpperCamelCase_      )      ) for t in zip(*data.values()      )]
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.scorer.predict(UpperCamelCase_							,							gpus=UpperCamelCase_							,							progress_bar=UpperCamelCase_      )
													return {"mean_score": mean_score, "scores": scores}






