





import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
 from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
 from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
 from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


_lowerCAmelCase							:					Dict		    =		DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

_lowerCAmelCase							:					List[Any]		    =		"main"
# Default branch name
_lowerCAmelCase							:					int		    =		"f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
_lowerCAmelCase							:					List[Any]		    =		"aaaaaaa"
# This commit does not exist, so we should 404.

_lowerCAmelCase							:					Optional[int]		    =		"d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCAmelCase							:					Dict		    =		"4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"

@contextlib.contextmanager
def    __snake_case  (    )       ->  Optional[Any]:
   print("Welcome!"		)
   yield
   print("Bye!"		)

@contextlib.contextmanager
def    __snake_case  (    )       ->  Optional[Any]:
   print("Bonjour!"		)
   yield
   print("Au revoir!"		)


class     __magic_name__     (							unittest.TestCase							):





     """simple docstring"""



     def   SCREAMING_SNAKE_CASE						(							self				:str     ):





        '''simple docstring'''




        assert transformers.__spec__ is not None
        assert importlib.util.find_spec("transformers"     ) is not None




class     __magic_name__     (							unittest.TestCase							):





     """simple docstring"""



     @unittest.mock.patch("sys.stdout"	,					new_callable=io.StringIO     )
     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					snake_case				:Union[str, Any]     ):





        '''simple docstring'''




        with ContextManagers([]     ):
           print("Transformers are awesome!"     )
        # The print statement adds a new line at the end of the output
        self.assertEqual(mock_stdout.getvalue()	,					"Transformers are awesome!\n"     )



     @unittest.mock.patch("sys.stdout"	,					new_callable=io.StringIO     )
     def   SCREAMING_SNAKE_CASE						(							self				:Dict	,					snake_case				:Optional[Any]     ):





        '''simple docstring'''




        with ContextManagers([context_en()]     ):
           print("Transformers are awesome!"     )
        # The output should be wrapped with an English welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()	,					"Welcome!\nTransformers are awesome!\nBye!\n"     )



     @unittest.mock.patch("sys.stdout"	,					new_callable=io.StringIO     )
     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					snake_case				:int     ):





        '''simple docstring'''




        with ContextManagers([context_fr(), context_en()]     ):
           print("Transformers are awesome!"     )
        # The output should be wrapped with an English and French welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()	,					"Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"     )



     @require_torch
     def   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        self.assertEqual(find_labels(lowercase_     )	,					["labels"]     )
        self.assertEqual(find_labels(lowercase_     )	,					["labels", "next_sentence_label"]     )
        self.assertEqual(find_labels(lowercase_     )	,					["start_positions", "end_positions"]     )


        class     __magic_name__     (							UpperCamelCase__							):





             """simple docstring"""



             pass




        self.assertEqual(find_labels(lowercase_     )	,					["labels"]     )



     @require_tf
     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]     ):





        '''simple docstring'''




        self.assertEqual(find_labels(lowercase_     )	,					["labels"]     )
        self.assertEqual(find_labels(lowercase_     )	,					["labels", "next_sentence_label"]     )
        self.assertEqual(find_labels(lowercase_     )	,					["start_positions", "end_positions"]     )


        class     __magic_name__     (							UpperCamelCase__							):





             """simple docstring"""



             pass




        self.assertEqual(find_labels(lowercase_     )	,					["labels"]     )





     @require_flax
     def   SCREAMING_SNAKE_CASE						(							self				:Dict     ):





        '''simple docstring'''




        self.assertEqual(find_labels(lowercase_     )	,					[]     )
        self.assertEqual(find_labels(lowercase_     )	,					[]     )
        self.assertEqual(find_labels(lowercase_     )	,					[]     )


        class     __magic_name__     (							UpperCamelCase__							):





             """simple docstring"""



             pass




        self.assertEqual(find_labels(lowercase_     )	,					[]     )







'''simple docstring'''

from __future__ import annotations

from decimal import Decimal

from numpy import array





def 						UpperCamelCase				(							_lowerCamelCase					:					list[list[float]]       ):
    A__				=			Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(_lowerCamelCase       ) == 2 and len(matrix[0]       ) == 2 and len(matrix[1]       ) == 2:
        # Calculate the determinant of the matrix
        A__				=			float(
            d(matrix[0][0]       ) * d(matrix[1][1]       ) - d(matrix[1][0]       ) * d(matrix[0][1]       )       )
        if determinant == 0:
            raise ValueError("This matrix has no inverse."       )

        # Creates a copy of the matrix with swapped positions of the elements
        A__				=			[[0.0, 0.0], [0.0, 0.0]]
        A__,	A__				=			matrix[1][1], matrix[0][0]
        A__,	A__				=			-matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(_lowerCamelCase       )       ) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(_lowerCamelCase       ) == 3
        and len(matrix[0]       ) == 3
        and len(matrix[1]       ) == 3
        and len(matrix[2]       ) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        A__				=			float(
            (
                (d(matrix[0][0]       ) * d(matrix[1][1]       ) * d(matrix[2][2]       ))
                + (d(matrix[0][1]       ) * d(matrix[1][2]       ) * d(matrix[2][0]       ))
                + (d(matrix[0][2]       ) * d(matrix[1][0]       ) * d(matrix[2][1]       ))
            )
            - (
                (d(matrix[0][2]       ) * d(matrix[1][1]       ) * d(matrix[2][0]       ))
                + (d(matrix[0][1]       ) * d(matrix[1][0]       ) * d(matrix[2][2]       ))
                + (d(matrix[0][0]       ) * d(matrix[1][2]       ) * d(matrix[2][1]       ))
            )       )
        if determinant == 0:
            raise ValueError("This matrix has no inverse."       )

        # Creating cofactor matrix
        A__				=			[
            [d(0.0       ), d(0.0       ), d(0.0       )],
            [d(0.0       ), d(0.0       ), d(0.0       )],
            [d(0.0       ), d(0.0       ), d(0.0       )],
        ]
        A__				=			(d(matrix[1][1]       ) * d(matrix[2][2]       )) - (
            d(matrix[1][2]       ) * d(matrix[2][1]       )
        )
        A__				=			-(
            (d(matrix[1][0]       ) * d(matrix[2][2]       )) - (d(matrix[1][2]       ) * d(matrix[2][0]       ))
        )
        A__				=			(d(matrix[1][0]       ) * d(matrix[2][1]       )) - (
            d(matrix[1][1]       ) * d(matrix[2][0]       )
        )
        A__				=			-(
            (d(matrix[0][1]       ) * d(matrix[2][2]       )) - (d(matrix[0][2]       ) * d(matrix[2][1]       ))
        )
        A__				=			(d(matrix[0][0]       ) * d(matrix[2][2]       )) - (
            d(matrix[0][2]       ) * d(matrix[2][0]       )
        )
        A__				=			-(
            (d(matrix[0][0]       ) * d(matrix[2][1]       )) - (d(matrix[0][1]       ) * d(matrix[2][0]       ))
        )
        A__				=			(d(matrix[0][1]       ) * d(matrix[1][2]       )) - (
            d(matrix[0][2]       ) * d(matrix[1][1]       )
        )
        A__				=			-(
            (d(matrix[0][0]       ) * d(matrix[1][2]       )) - (d(matrix[0][2]       ) * d(matrix[1][0]       ))
        )
        A__				=			(d(matrix[0][0]       ) * d(matrix[1][1]       )) - (
            d(matrix[0][1]       ) * d(matrix[1][0]       )
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        A__				=			array(_lowerCamelCase       )
        for i in range(3       ):
            for j in range(3       ):
                A__				=			cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        A__				=			array(_lowerCamelCase       )
        for i in range(3       ):
            for j in range(3       ):
                inverse_matrix[i][j] /= d(_lowerCamelCase       )

        # Calculate the inverse of the matrix
        return [[float(d(_lowerCamelCase       )       ) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError("Please provide a matrix of size 2x2 or 3x3."       )



