






from __future__ import annotations





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Tuple		,     __A:   list[list[int]]   )    ->				Dict:
												_A								= 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(__A   ) != 0:
																	_A								= len(rows[0]   )
																	if cols == 0:
																						raise error
																	for row in rows:
																						if len(__A   ) != cols:
																											raise error
																						for value in row:
																											if not isinstance(__A		,     (int, float)   ):
																																raise error
																	_A								= rows
												else:
																	_A								= []

							def       __A (  self:   Any   )    ->				list[list[int]]:
												return [[row[i] for row in self.rows] for i in range(len(self.rows[0]   )   )]

							@property
							def       __A (  self:   int   )    ->				int:
												return len(self.rows   )

							@property
							def       __A (  self:   int   )    ->				int:
												return len(self.rows[0]   )

							@property
							def       __A (  self:   List[str]   )    ->				tuple[int, int]:
												return (self.num_rows, self.num_columns)

							@property
							def       __A (  self:   Any   )    ->				bool:
												return self.order[0] == self.order[1]

							def       __A (  self:   int   )    ->				Matrix:
												_A								= [
												    [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(__A   )

							def       __A (  self:   Tuple   )    ->				int:
												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:   str   )    ->				bool:
												return bool(self.determinant()   )

							def       __A (  self:   List[Any]		,     __A:   int		,     __A:   int   )    ->				int:
												_A								= [
												    [
												        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(__A   ).determinant()

							def       __A (  self:   Optional[Any]		,     __A:   int		,     __A:   int   )    ->				int:
												if (row + column) % 2 == 0:
																	return self.get_minor(__A		,     __A   )
												return -1 * self.get_minor(__A		,     __A   )

							def       __A (  self:   List[Any]   )    ->				Matrix:
												return Matrix(
												    [
												        [self.get_minor(__A		,     __A   ) for column in range(self.num_columns   )]
												        for row in range(self.num_rows   )
												    ]   )

							def       __A (  self:   List[Any]   )    ->				Matrix:
												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:   Tuple   )    ->				Matrix:
												_A								= [
												    [self.cofactors().rows[column][row] for column in range(self.num_columns   )]
												    for row in range(self.num_rows   )
												]
												return Matrix(__A   )

							def       __A (  self:   List[str]   )    ->				Matrix:
												_A								= 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:   List[str]   )    ->				str:
												return str(self.rows   )

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

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

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

							def __eq__(  self:   Union[str, Any]		,     __A:   object   )    ->				bool:
												if not isinstance(__A		,     __A   ):
																	return NotImplemented
												return self.rows == other.rows

							def __ne__(  self:   Optional[int]		,     __A:   object   )    ->				bool:
												return not self == other

							def __neg__(  self:   Dict   )    ->				Matrix:
												return self * -1

							def __add__(  self:   Union[str, Any]		,     __A:   Matrix   )    ->				Matrix:
												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:   Union[str, Any]		,     __A:   Matrix   )    ->				Matrix:
												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:   Optional[Any]		,     __A:   Matrix | int | float   )    ->				Matrix:
												if isinstance(__A		,     (int, float)   ):
																	return Matrix(
																	    [[int(element * other   ) for element in row] for row in self.rows]   )
												elif isinstance(__A		,     __A   ):
																	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(__A		,     __A   ) 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:   Optional[Any]		,     __A:   int   )    ->				Matrix:
												if not isinstance(__A		,     __A   ):
																	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'''   )
												_A								= self
												for _ in range(other - 1   ):
																	result *= self
												return result



							@classmethod
							def       __A (  cls:   Dict		,     __A:   list[int]		,     __A:   list[int]   )    ->				int:
												return sum(row[i] * column[i] for i in range(len(__A   )   )   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()











import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					BlenderbotSmallTokenizer
							A_						  =					False

							def       __A (  self:   List[str]   )    ->				int:
												super().setUp()

												_A								= ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												_A								= dict(zip(__A		,     range(len(__A   )   )   )   )

												_A								= ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
												_A								= {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']   )
												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''merges_file''']   )
												with open(self.vocab_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write(json.dumps(__A   ) + '''\n'''   )
												with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write('''\n'''.join(__A   )   )

							def       __A (  self:   str		,     **__A:   Optional[Any]   )    ->				Dict:
												kwargs.update(self.special_tokens_map   )
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   str		,     __A:   List[str]   )    ->				int:
												_A								= '''adapt act apte'''
												_A								= '''adapt act apte'''
												return input_text, output_text

							def       __A (  self:   Union[str, Any]   )    ->				Any:
												_A								= BlenderbotSmallTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map   )
												_A								= '''adapt act apte'''
												_A								= ['''adapt''', '''act''', '''ap@@''', '''te''']
												_A								= tokenizer.tokenize(__A   )
												self.assertListEqual(__A		,     __A   )

												_A								= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												_A								= [0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__A   )		,     __A   )

							def       __A (  self:   Any   )    ->				List[str]:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												assert tok('''sam'''   ).input_ids == [13_84]
												_A								= '''I am a small frog.'''
												_A								= tok([src_text]		,     padding=__A		,     truncation=__A   )['''input_ids''']
												_A								= tok.batch_decode(__A		,     skip_special_tokens=__A		,     clean_up_tokenization_spaces=__A   )[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."



							def       __A (  self:   Any   )    ->				int:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												_A								= '''I am a small frog .'''
												_A								= '''.'''
												_A								= tok(__A   )['''input_ids''']
												_A								= tok(__A   )['''input_ids''']

												assert encoded[-1] == encoded_dot[0]



