

import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    'files'     ,							[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]     ,							)
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('dset_infos_dir'   )
							if "full:README.md" in files:
														with open(dataset_infos_dir / 'README.md'     ,							'w'   ) as f:
																					f.write('---\ndataset_info:\n  dataset_size: 42\n---'   )
							if "empty:README.md" in files:
														with open(dataset_infos_dir / 'README.md'     ,							'w'   ) as f:
																					f.write(''   )
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / 'dataset_infos.json'     ,							'w'   ) as f:
																					f.write('{"default": {"dataset_size": 42}}'   )
							snake_case_           =					DatasetInfosDict.from_directory(UpperCamelCase__   )
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    'dataset_info'     ,							[
        DatasetInfo(),
        DatasetInfo(
            description='foo'     ,							features=Features({'a': Value('int32'   )}   )     ,							builder_name='builder'     ,							config_name='config'     ,							version='1.0.0'     ,							splits=[{'name': 'train'}]     ,							download_size=42     ,							),
    ]     ,							)
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(UpperCamelCase__   )
							dataset_info.write_to_directory(UpperCamelCase__   )
							snake_case_           =					DatasetInfo.from_directory(UpperCamelCase__   )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(UpperCamelCase__     ,							'dataset_info.json'   )   )





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					DatasetInfo(
							    description='foo'     ,							citation='bar'     ,							homepage='https://foo.bar'     ,							license='CC0'     ,							features=Features({'a': Value('int32'   )}   )     ,							post_processed={}     ,							supervised_keys=()     ,							task_templates=[]     ,							builder_name='builder'     ,							config_name='config'     ,							version='1.0.0'     ,							splits=[{'name': 'train', 'num_examples': 42}]     ,							download_checksums={}     ,							download_size=1337     ,							post_processing_size=442     ,							dataset_size=1234     ,							size_in_bytes=1337 + 442 + 1234     ,							)
							snake_case_           =					dataset_info._to_yaml_dict()
							assert sorted(UpperCamelCase__   ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML   )
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]     ,							(list, dict, int, str)   )
							snake_case_           =					yaml.safe_dump(UpperCamelCase__   )
							snake_case_           =					yaml.safe_load(UpperCamelCase__   )
							assert dataset_info_yaml_dict == reloaded





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					DatasetInfo()
							snake_case_           =					dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    'dataset_infos_dict'     ,							[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}   ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}   ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'     ,							features=Features({'a': Value('int32'   )}   )     ,							builder_name='builder'     ,							config_name='config'     ,							version='1.0.0'     ,							splits=[{'name': 'train'}]     ,							download_size=42     ,							)
            }   ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42   ),
                'v2': DatasetInfo(dataset_size=1337   ),
            }   ),
    ]     ,							)
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(UpperCamelCase__   )
							dataset_infos_dict.write_to_directory(UpperCamelCase__   )
							snake_case_           =					DatasetInfosDict.from_directory(UpperCamelCase__   )

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														snake_case_           =					config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														snake_case_           =					DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()   )
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(UpperCamelCase__     ,							'README.md'   )   )







from __future__ import annotations







class 				lowercase       :




				def __init__(      self   ,     snake_case ):
											snake_case_           =					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(snake_case ) != 0:
																		snake_case_           =					len(rows[0] )
																		if cols == 0:
																									raise error
																		for row in rows:
																									if len(snake_case ) != cols:
																																raise error
																									for value in row:
																																if not isinstance(snake_case   ,     (int, float) ):
																																							raise error
																		snake_case_           =					rows
											else:
																		snake_case_           =					[]




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




				@property
				def 					a							(      self ):
											return len(self.rows )




				@property
				def 					a							(      self ):
											return len(self.rows[0] )




				@property
				def 					a							(      self ):
											return (self.num_rows, self.num_columns)




				@property
				def 					a							(      self ):
											return self.order[0] == self.order[1]




				def 					a							(      self ):
											snake_case_           =					[
											    [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(snake_case )




				def 					a							(      self ):
											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 					a							(      self ):
											return bool(self.determinant() )




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					[
											    [
											        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(snake_case ).determinant()




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




				def 					a							(      self ):
											return Matrix(
											    [
											        [self.get_minor(snake_case   ,     snake_case ) for column in range(self.num_columns )]
											        for row in range(self.num_rows )
											    ] )




				def 					a							(      self ):
											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 					a							(      self ):
											snake_case_           =					[
											    [self.cofactors().rows[column][row] for column in range(self.num_columns )]
											    for row in range(self.num_rows )
											]
											return Matrix(snake_case )




				def 					a							(      self ):
											snake_case_           =					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 ):
											return str(self.rows )




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




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					TypeError('Row must be a list containing all ints and/or floats' )
											if not isinstance(snake_case   ,     snake_case ):
																		raise type_error
											for value in row:
																		if not isinstance(snake_case   ,     (int, float) ):
																									raise type_error
											if len(snake_case ) != 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(snake_case )
											else:
																		snake_case_           =					self.rows[0:position] + [row] + self.rows[position:]




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




				def __eq__(      self   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		return NotImplemented
											return self.rows == other.rows




				def __ne__(      self   ,     snake_case ):
											return not self == other




				def __neg__(      self ):
											return self * -1




				def __add__(      self   ,     snake_case ):
											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   ,     snake_case ):
											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   ,     snake_case ):
											if isinstance(snake_case   ,     (int, float) ):
																		return Matrix(
																		    [[int(element * other ) for element in row] for row in self.rows] )
											elif isinstance(snake_case   ,     snake_case ):
																		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(snake_case   ,     snake_case ) 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   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		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_           =					self
											for _ in range(other - 1 ):
																		result *= self
											return result




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     snake_case ):
											return sum(row[i] * column[i] for i in range(len(snake_case ) ) )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




