





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase     :     Dict							=	get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase	(   UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : Dict            =      XLMRobertaTokenizer
      _lowercase    : Optional[int]            =      XLMRobertaTokenizerFast
      _lowercase    : Dict            =      True
      _lowercase    : str            =      True







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        super().setUp()

        # We have a SentencePiece fixture for testing
        a__     : List[str]  =XLMRobertaTokenizer(lowerCAmelCase__		,				keep_accents=lowerCAmelCase__							)
        tokenizer.save_pretrained(self.tmpdirname							)







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : Any  ="<pad>"
        a__     : Any  =1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__							)		,				lowerCAmelCase__							)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__							)		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : Union[str, Any]  =list(self.get_tokenizer().get_vocab().keys()							)

        self.assertEqual(vocab_keys[0]		,				"<s>"							)
        self.assertEqual(vocab_keys[1]		,				"<pad>"							)
        self.assertEqual(vocab_keys[-1]		,				"<mask>"							)
        self.assertEqual(len(lowerCAmelCase__							)		,				1_0_0_2							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        self.assertEqual(self.get_tokenizer().vocab_size		,				1_0_0_2							)







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : Tuple  =XLMRobertaTokenizer(lowerCAmelCase__		,				keep_accents=lowerCAmelCase__							)

        a__     : str  =tokenizer.tokenize("This is a test"							)
        self.assertListEqual(lowerCAmelCase__		,				["▁This", "▁is", "▁a", "▁t", "est"]							)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowerCAmelCase__							)		,				[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]		,				)

        a__     : Dict  =tokenizer.tokenize("I was born in 92000, and this is falsé."							)
        self.assertListEqual(
            lowerCAmelCase__		,				[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "9",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "é",
                ".",
            ]		,				)
        a__     : Union[str, Any]  =tokenizer.convert_tokens_to_ids(lowerCAmelCase__							)
        self.assertListEqual(
            lowerCAmelCase__		,				[
                value + tokenizer.fairseq_offset
                for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
                #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
            ]		,				)

        a__     : Optional[int]  =tokenizer.convert_ids_to_tokens(lowerCAmelCase__							)
        self.assertListEqual(
            lowerCAmelCase__		,				[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "<unk>",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "<unk>",
                ".",
            ]		,				)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        if not self.test_slow_tokenizer:
          # as we don't have a slow version, we can't compare the outputs between slow and fast versions
          return

        a__     : List[str]  =(self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
            a__     : Optional[Any]  =self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__		,				**lowerCAmelCase__							)
            a__     : Any  =self.tokenizer_class.from_pretrained(lowerCAmelCase__		,				**lowerCAmelCase__							)

            a__     : List[Any]  =tempfile.mkdtemp()

            a__     : str  =tokenizer_r.save_pretrained(lowerCAmelCase__							)
            a__     : List[str]  =tokenizer_p.save_pretrained(lowerCAmelCase__							)

            # Checks it save with the same files + the tokenizer.json file for the fast one
            self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files							)							)
            a__     : Any  =tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f							)
            self.assertSequenceEqual(lowerCAmelCase__		,				lowerCAmelCase__							)

            # Checks everything loads correctly in the same way
            a__     : str  =tokenizer_r.from_pretrained(lowerCAmelCase__							)
            a__     : Union[str, Any]  =tokenizer_p.from_pretrained(lowerCAmelCase__							)

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(lowerCAmelCase__		,				lowerCAmelCase__							)							)
              # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
              # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

            shutil.rmtree(lowerCAmelCase__							)

            # Save tokenizer rust, legacy_format=True
            a__     : Union[str, Any]  =tempfile.mkdtemp()

            a__     : Any  =tokenizer_r.save_pretrained(lowerCAmelCase__		,				legacy_format=lowerCAmelCase__							)
            a__     : Any  =tokenizer_p.save_pretrained(lowerCAmelCase__							)

            # Checks it save with the same files
            self.assertSequenceEqual(lowerCAmelCase__		,				lowerCAmelCase__							)

            # Checks everything loads correctly in the same way
            a__     : int  =tokenizer_r.from_pretrained(lowerCAmelCase__							)
            a__     : Union[str, Any]  =tokenizer_p.from_pretrained(lowerCAmelCase__							)

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(lowerCAmelCase__		,				lowerCAmelCase__							)							)

            shutil.rmtree(lowerCAmelCase__							)

            # Save tokenizer rust, legacy_format=False
            a__     : Union[str, Any]  =tempfile.mkdtemp()

            a__     : Union[str, Any]  =tokenizer_r.save_pretrained(lowerCAmelCase__		,				legacy_format=lowerCAmelCase__							)
            a__     : Optional[int]  =tokenizer_p.save_pretrained(lowerCAmelCase__							)

            # Checks it saved the tokenizer.json file
            self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files							)							)

            # Checks everything loads correctly in the same way
            a__     : Union[str, Any]  =tokenizer_r.from_pretrained(lowerCAmelCase__							)
            a__     : Union[str, Any]  =tokenizer_p.from_pretrained(lowerCAmelCase__							)

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(lowerCAmelCase__		,				lowerCAmelCase__							)							)

            shutil.rmtree(lowerCAmelCase__							)







      @cached_property
      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"							)







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        with tempfile.NamedTemporaryFile() as f:
          shutil.copyfile(lowerCAmelCase__		,				f.name							)
          a__     : List[Any]  =XLMRobertaTokenizer(f.name		,				keep_accents=lowerCAmelCase__							)
          a__     : Optional[Any]  =pickle.dumps(lowerCAmelCase__							)
        pickle.loads(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        if not self.test_rust_tokenizer:
          return

        a__     : Dict  =self.get_tokenizer()
        a__     : Tuple  =self.get_rust_tokenizer()

        a__     : Tuple  ="I was born in 92000, and this is falsé."

        a__     : int  =tokenizer.tokenize(lowerCAmelCase__							)
        a__     : List[Any]  =rust_tokenizer.tokenize(lowerCAmelCase__							)
        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__							)

        a__     : Optional[int]  =tokenizer.encode(lowerCAmelCase__		,				add_special_tokens=lowerCAmelCase__							)
        a__     : int  =rust_tokenizer.encode(lowerCAmelCase__		,				add_special_tokens=lowerCAmelCase__							)
        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__							)

        a__     : Tuple  =self.get_rust_tokenizer()
        a__     : List[Any]  =tokenizer.encode(lowerCAmelCase__							)
        a__     : List[Any]  =rust_tokenizer.encode(lowerCAmelCase__							)
        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__							)







      @slow
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : List[str]  ="Hello World!"
        a__     : Optional[int]  =[0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(lowerCAmelCase__		,				self.big_tokenizer.encode(lowerCAmelCase__							)							)







      @slow
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : List[str]  =(
            "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
            " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
        )
        a__     : List[str]  =[
            0,
            3_2_9_3,
            8_3,
            1_0,
            4_5_5_2,
            4_9_8_9,
            7_9_8_6,
            6_7_8,
            1_0,
            5_9_1_5,
            1_1_1,
            1_7_9_4_5_9,
            1_2_4_8_5_0,
            4,
            6_0_4_4,
            2_3_7,
            1_2,
            6,
            5,
            6,
            4,
            6_7_8_0,
            7_0_5,
            1_5,
            1_3_8_8,
            4_4,
            3_7_8,
            1_0_1_1_4,
            7_1_1,
            1_5_2,
            2_0,
            6,
            5,
            2_2_3_7_6,
            6_4_2,
            1_2_2_1,
            1_5_1_9_0,
            3_4_1_5_3,
            4_5_0,
            5_6_0_8,
            9_5_9,
            1_1_1_9,
            5_7_7_0_2,
            1_3_6,
            1_8_6,
            4_7,
            1_0_9_8,
            2_9_3_6_7,
            4_7,
            # 4426, # What fairseq tokenizes from "<unk>": "_<"
            # 3678, # What fairseq tokenizes from "<unk>": "unk"
            # 2740, # What fairseq tokenizes from "<unk>": ">"
            3,  # What we tokenize from "<unk>": "<unk>"
            6,  # Residue from the tokenization: an extra sentencepiece underline
            4,
            6_0_4_4,
            2_3_7,
            6_2_8_4,
            5_0_9_0_1,
            5_2_8,
            3_1,
            9_0,
            3_4,
            9_2_7,
            2,
        ]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(lowerCAmelCase__		,				self.big_tokenizer.encode(lowerCAmelCase__							)							)







      @slow
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Dict  ={"input_ids": [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCAmelCase__		,				model_name="xlm-roberta-base"		,				revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"		,				)













from __future__ import annotations







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =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(lowerCAmelCase__							) != 0:
          a__     : List[str]  =len(rows[0]							)
          if cols == 0:
            raise error
          for row in rows:
            if len(lowerCAmelCase__							) != cols:
              raise error
            for value in row:
              if not isinstance(lowerCAmelCase__		,				(int, float)							):
                raise error
          a__     : List[Any]  =rows
        else:
          a__     : str  =[]







      def        _lowercase  (   self							)       ->						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							)       ->						int:



        '''simple docstring'''




        return len(self.rows							)







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows[0]							)







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



        '''simple docstring'''




        return (self.num_rows, self.num_columns)







      @property
      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : 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(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						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							)       ->						bool:



        '''simple docstring'''




        return bool(self.determinant()							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : List[str]  =[
            [
                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(lowerCAmelCase__							).determinant()







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						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							)       ->						Matrix:



        '''simple docstring'''




        a__     : Dict  =[
            [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
            for row in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Union[str, 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							)       ->						str:



        '''simple docstring'''




        return str(self.rows							)







      def __str__(   self							)       ->						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(lowerCAmelCase__							) for value in row]							) + ".]"
                    for row in self.rows
                ]							)
            + "]"
        )







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : List[str]  =TypeError("Row must be a list containing all ints and/or floats"							)
        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise type_error
        for value in row:
          if not isinstance(lowerCAmelCase__		,				(int, float)							):
            raise type_error
        if len(lowerCAmelCase__							) != 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(lowerCAmelCase__							)
        else:
          a__     : Tuple  =self.rows[0:position] + [row] + self.rows[position:]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




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







      def __eq__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




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







      def __ne__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        return not self == other







      def __neg__(   self							)       ->						Matrix:



        '''simple docstring'''




        return self * -1







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



        '''simple docstring'''




        if isinstance(lowerCAmelCase__		,				(int, float)							):
          return Matrix(
              [[int(element * other							) for element in row] for row in self.rows]							)
        elif isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          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(lowerCAmelCase__		,				lowerCAmelCase__							) 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		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          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__     : Tuple  =self
        for _ in range(other - 1							):
          result *= self
        return result







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




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


if __name__ == "__main__":
     import doctest

     doctest.testmod()






