


import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
   from test_module.custom_tokenization_fast import CustomTokenizerFast






class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=0






    @slow
    def _a					(			self    )  ->       Optional[Any]:
         for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A    )
              self.assertIsNotNone(__A    )
              self.assertIsInstance(__A					,   (BertTokenizer, BertTokenizerFast)    )
              self.assertGreater(len(__A    )					,   0    )

         for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A    )
              self.assertIsNotNone(__A    )
              self.assertIsInstance(__A					,   (GPTaTokenizer, GPTaTokenizerFast)    )
              self.assertGreater(len(__A    )					,   0    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A    )
         self.assertIsInstance(__A					,   (BertTokenizer, BertTokenizerFast)    )
         self.assertEqual(tokenizer.vocab_size					,   12    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A    )
         self.assertIsInstance(__A					,   (RobertaTokenizer, RobertaTokenizerFast)    )
         self.assertEqual(tokenizer.vocab_size					,   20    )






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=AutoConfig.from_pretrained(__A    )
         self.assertIsInstance(__A					,   __A    )
         # Check that tokenizer_type ≠ model_type
         __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A					,   config=__A    )
         self.assertIsInstance(__A					,   (BertTokenizer, BertTokenizerFast)    )
         self.assertEqual(tokenizer.vocab_size					,   12    )






    def _a					(			self    )  ->       Any:
         with tempfile.TemporaryDirectory() as tmp_dir:
              shutil.copy('./tests/fixtures/vocab.txt'					,   os.path.join(__A					,   'vocab.txt'    )    )

              __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A					,   tokenizer_type='bert'					,   use_fast=__A    )
              self.assertIsInstance(__A					,   __A    )

         with tempfile.TemporaryDirectory() as tmp_dir:
              shutil.copy('./tests/fixtures/vocab.json'					,   os.path.join(__A					,   'vocab.json'    )    )
              shutil.copy('./tests/fixtures/merges.txt'					,   os.path.join(__A					,   'merges.txt'    )    )

              __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A					,   tokenizer_type='gpt2'					,   use_fast=__A    )
              self.assertIsInstance(__A					,   __A    )






    @require_tokenizers
    def _a					(			self    )  ->       str:
         with tempfile.TemporaryDirectory() as tmp_dir:
              shutil.copy('./tests/fixtures/vocab.txt'					,   os.path.join(__A					,   'vocab.txt'    )    )

              __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A					,   tokenizer_type='bert'    )
              self.assertIsInstance(__A					,   __A    )

         with tempfile.TemporaryDirectory() as tmp_dir:
              shutil.copy('./tests/fixtures/vocab.json'					,   os.path.join(__A					,   'vocab.json'    )    )
              shutil.copy('./tests/fixtures/merges.txt'					,   os.path.join(__A					,   'merges.txt'    )    )

              __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A					,   tokenizer_type='gpt2'    )
              self.assertIsInstance(__A					,   __A    )






    def _a					(			self    )  ->       Any:
         with pytest.raises(__A    ):
              AutoTokenizer.from_pretrained('./'					,   tokenizer_type='xxx'    )






    @require_tokenizers
    def _a					(			self    )  ->       List[str]:
         for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
              __UpperCamelCase    							=tokenizer_class.from_pretrained('wietsedv/bert-base-dutch-cased'    )
              self.assertIsInstance(__A					,   (BertTokenizer, BertTokenizerFast)    )

              if isinstance(__A					,   __A    ):
                   self.assertEqual(tokenizer.basic_tokenizer.do_lower_case					,   __A    )
              else:
                   self.assertEqual(tokenizer.do_lower_case					,   __A    )

              self.assertEqual(tokenizer.model_max_length					,   512    )






    @require_tokenizers
    def _a					(			self    )  ->       Optional[Any]:
         for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
              with self.assertRaisesRegex(
                  __A					,   'julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'					,   ):
                   __UpperCamelCase    							=tokenizer_class.from_pretrained('julien-c/herlolip-not-exists'    )






    def _a					(			self    )  ->       Any:
         # tests: https://github.com/huggingface/transformers/pull/13251
         # 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
         # 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
         __UpperCamelCase    							=TOKENIZER_MAPPING.values()
         __UpperCamelCase    							=[]

         for slow_tok, fast_tok in tokenizers:
              if slow_tok is not None:
                   tokenizer_names.append(slow_tok.__name__    )

              if fast_tok is not None:
                   tokenizer_names.append(fast_tok.__name__    )

         for tokenizer_name in tokenizer_names:
              # must find the right class
              tokenizer_class_from_name(__A    )






    @require_tokenizers
    def _a					(			self    )  ->       Any:
         self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'					,   use_fast=__A    )					,   __A    )
         self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'    )					,   __A    )






    @require_tokenizers
    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('distilbert-base-uncased'					,   do_lower_case=__A    )
         __UpperCamelCase    							='Hello, world. How are you?'
         __UpperCamelCase    							=tokenizer.tokenize(__A    )
         self.assertEqual('[UNK]'					,   tokens[0]    )

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('microsoft/mpnet-base'					,   do_lower_case=__A    )
         __UpperCamelCase    							=tokenizer.tokenize(__A    )
         self.assertEqual('[UNK]'					,   tokens[0]    )






    @require_tokenizers
    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('robot-test/dummy-tokenizer-fast-with-model-config'    )
         self.assertEqual(type(__A    )					,   __A    )
         self.assertEqual(tokenizer.model_max_length					,   512    )
         self.assertEqual(tokenizer.vocab_size					,   30000    )
         self.assertEqual(tokenizer.unk_token					,   '[UNK]'    )
         self.assertEqual(tokenizer.padding_side					,   'right'    )
         self.assertEqual(tokenizer.truncation_side					,   'right'    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A    )
         self.assertIsInstance(__A					,   (BertTokenizer, BertTokenizerFast)    )
         with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(__A    )
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A    )

         self.assertIsInstance(__A					,   tokenizer.__class__    )
         self.assertEqual(tokenizera.vocab_size					,   12    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('ctrl'    )
         # There is no fast CTRL so this always gives us a slow tokenizer.
         self.assertIsInstance(__A					,   __A    )






    def _a					(			self    )  ->       Union[str, Any]:
         # Check we can load the tokenizer config of an online model.
         __UpperCamelCase    							=get_tokenizer_config('bert-base-cased'    )
         __UpperCamelCase    							=config.pop('_commit_hash'					,   __A    )
         # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
         self.assertEqual(__A					,   {'do_lower_case': False}    )

         # This model does not have a tokenizer_config so we get back an empty dict.
         __UpperCamelCase    							=get_tokenizer_config(__A    )
         self.assertDictEqual(__A					,   {}    )

         # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
         __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A    )
         with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(__A    )
              __UpperCamelCase    							=get_tokenizer_config(__A    )

         # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
         self.assertEqual(config['tokenizer_class']					,   'BertTokenizer'    )






    def _a					(			self    )  ->       str:
         try:
              AutoConfig.register('custom'					,   __A    )

              AutoTokenizer.register(__A					,   slow_tokenizer_class=__A    )
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(__A    ):
                   AutoTokenizer.register(__A					,   slow_tokenizer_class=__A    )

              __UpperCamelCase    							=CustomTokenizer.from_pretrained(__A    )
              with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(__A    )

                   __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A    )
                   self.assertIsInstance(__A					,   __A    )

         finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]






    @require_tokenizers
    def _a					(			self    )  ->       int:
         try:
              AutoConfig.register('custom'					,   __A    )

              # Can register in two steps
              AutoTokenizer.register(__A					,   slow_tokenizer_class=__A    )
              self.assertEqual(TOKENIZER_MAPPING[CustomConfig]					,   (CustomTokenizer, None)    )
              AutoTokenizer.register(__A					,   fast_tokenizer_class=__A    )
              self.assertEqual(TOKENIZER_MAPPING[CustomConfig]					,   (CustomTokenizer, CustomTokenizerFast)    )

              del TOKENIZER_MAPPING._extra_content[CustomConfig]
              # Can register in one step
              AutoTokenizer.register(
                  __A					,   slow_tokenizer_class=__A					,   fast_tokenizer_class=__A    )
              self.assertEqual(TOKENIZER_MAPPING[CustomConfig]					,   (CustomTokenizer, CustomTokenizerFast)    )

              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(__A    ):
                   AutoTokenizer.register(__A					,   fast_tokenizer_class=__A    )

              # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
              # and that model does not have a tokenizer.json
              with tempfile.TemporaryDirectory() as tmp_dir:
                   __UpperCamelCase    							=BertTokenizerFast.from_pretrained(__A    )
                   bert_tokenizer.save_pretrained(__A    )
                   __UpperCamelCase    							=CustomTokenizerFast.from_pretrained(__A    )

              with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(__A    )

                   __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A    )
                   self.assertIsInstance(__A					,   __A    )

                   __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A					,   use_fast=__A    )
                   self.assertIsInstance(__A					,   __A    )

         finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]






    def _a					(			self    )  ->       Any:
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(__A    ):
              __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'    )
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(__A    ):
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(
                  'hf-internal-testing/test_dynamic_tokenizer'					,   trust_remote_code=__A    )

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'					,   trust_remote_code=__A    )
         self.assertTrue(tokenizer.special_attribute_present    )
         # Test tokenizer can be reloaded.
         with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(__A    )
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A					,   trust_remote_code=__A    )
         self.assertTrue(reloaded_tokenizer.special_attribute_present    )

         if is_tokenizers_available():
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizerFast'    )
              self.assertEqual(reloaded_tokenizer.__class__.__name__					,   'NewTokenizerFast'    )

              # Test we can also load the slow version
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(
                  'hf-internal-testing/test_dynamic_tokenizer'					,   trust_remote_code=__A					,   use_fast=__A    )
              self.assertTrue(tokenizer.special_attribute_present    )
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizer'    )
              # Test tokenizer can be reloaded.
              with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(__A    )
                   __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A					,   trust_remote_code=__A					,   use_fast=__A    )
              self.assertEqual(reloaded_tokenizer.__class__.__name__					,   'NewTokenizer'    )
              self.assertTrue(reloaded_tokenizer.special_attribute_present    )
         else:
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizer'    )
              self.assertEqual(reloaded_tokenizer.__class__.__name__					,   'NewTokenizer'    )






    @require_tokenizers
    def _a					(			self    )  ->       Tuple:






         class     UpperCAmelCase__       (					__lowerCamelCase		):



             """simple docstring"""






             UpperCAmelCase__						:		Any      =  False






         class     UpperCAmelCase__       (					__lowerCamelCase		):



             """simple docstring"""






             UpperCAmelCase__						:		str      =  NewTokenizer
             UpperCAmelCase__						:		str      =  False

         try:
              AutoConfig.register('custom'					,   __A    )
              AutoTokenizer.register(__A					,   slow_tokenizer_class=__A    )
              AutoTokenizer.register(__A					,   fast_tokenizer_class=__A    )
              # If remote code is not set, the default is to use local
              __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'    )
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizerFast'    )
              self.assertFalse(tokenizer.special_attribute_present    )
              __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'					,   use_fast=__A    )
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizer'    )
              self.assertFalse(tokenizer.special_attribute_present    )

              # If remote code is disabled, we load the local one.
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(
                  'hf-internal-testing/test_dynamic_tokenizer'					,   trust_remote_code=__A    )
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizerFast'    )
              self.assertFalse(tokenizer.special_attribute_present    )
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(
                  'hf-internal-testing/test_dynamic_tokenizer'					,   trust_remote_code=__A					,   use_fast=__A    )
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizer'    )
              self.assertFalse(tokenizer.special_attribute_present    )

              # If remote is enabled, we load from the Hub
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(
                  'hf-internal-testing/test_dynamic_tokenizer'					,   trust_remote_code=__A    )
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizerFast'    )
              self.assertTrue(tokenizer.special_attribute_present    )
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(
                  'hf-internal-testing/test_dynamic_tokenizer'					,   trust_remote_code=__A					,   use_fast=__A    )
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizer'    )
              self.assertTrue(tokenizer.special_attribute_present    )

         finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained(
             'hf-internal-testing/test_dynamic_tokenizer_legacy'					,   trust_remote_code=__A    )
         self.assertTrue(tokenizer.special_attribute_present    )
         if is_tokenizers_available():
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizerFast'    )

              # Test we can also load the slow version
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(
                  'hf-internal-testing/test_dynamic_tokenizer_legacy'					,   trust_remote_code=__A					,   use_fast=__A    )
              self.assertTrue(tokenizer.special_attribute_present    )
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizer'    )
         else:
              self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizer'    )






    def _a					(			self    )  ->       Dict:
         with self.assertRaisesRegex(
             __A					,   'bert-base is not a local folder and is not a valid model identifier'    ):
              __UpperCamelCase    							=AutoTokenizer.from_pretrained('bert-base'    )






    def _a					(			self    )  ->       Optional[Any]:
         with self.assertRaisesRegex(
             __A					,   r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'    ):
              __UpperCamelCase    							=AutoTokenizer.from_pretrained(__A					,   revision='aaaaaa'    )






    def _a					(			self    )  ->       Any:
         # Make sure we have cached the tokenizer.
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'    )
         with RequestCounter() as counter:
              __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'    )
              self.assertEqual(counter.get_request_count					,   0    )
              self.assertEqual(counter.head_request_count					,   1    )
              self.assertEqual(counter.other_request_count					,   0    )












'''simple docstring'''
from __future__ import annotations

class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							Optional[int]							,	__A       :							list[list[int]]	):
        __UpperCamelCase       =  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:
               __UpperCamelCase       =  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
               __UpperCamelCase       =  rows
        else:
               __UpperCamelCase       =  []






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






 @property
 def        _lowerCamelCase					(       self       :							str	):
        return len(self.rows	)






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






 @property
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        return (self.num_rows, self.num_columns)






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






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  [
            [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        _lowerCamelCase					(       self       :							Any	):
        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        _lowerCamelCase					(       self       :							List[str]	):
        return bool(self.determinant()	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							int							,	__A       :							int	):
        __UpperCamelCase       =  [
            [
                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        _lowerCamelCase					(       self       :							Dict							,	__A       :							int							,	__A       :							int	):
        if (row + column) % 2 == 0:
               return self.get_minor(__A							,	__A	)
        return -1 * self.get_minor(__A							,	__A	)






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






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        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        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  [
            [self.cofactors().rows[column][row] for column in range(self.num_columns	)]
            for row in range(self.num_rows	)
        ]
        return Matrix(__A	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  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[Any]	):
        return str(self.rows	)






 def __str__(       self       :							Union[str, Any]	):
        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        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							list[int]							,	__A       :							int | None = None	):
        __UpperCamelCase       =  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:
               __UpperCamelCase       =  self.rows[0:position] + [row] + self.rows[position:]






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							list[int]							,	__A       :							int | None = None	):
        __UpperCamelCase       =  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:
               __UpperCamelCase       =  [self.rows[i] + [column[i]] for i in range(self.num_rows	)]
        else:
               __UpperCamelCase       =  [
                   self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                   for i in range(self.num_rows	)
               ]






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






 def __ne__(       self       :							Any							,	__A       :							object	):
        return not self == other






 def __neg__(       self       :							List[Any]	):
        return self * -1






 def __add__(       self       :							List[str]							,	__A       :							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       :							str							,	__A       :							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       :							str							,	__A       :							Matrix | int | float	):
        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       :							Union[str, Any]							,	__A       :							int	):
        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'	)
        __UpperCamelCase       =  self
        for _ in range(other - 1	):
               result *= self
        return result






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


if __name__ == "__main__":
    import doctest

    doctest.testmod()
