import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class     __UpperCAmelCase(    A__      ,	unittest.TestCase				):






 """simple docstring"""




 __magic_name__          =   PreTrainedTokenizerFast
 __magic_name__          =   False
 __magic_name__          =   True
 __magic_name__          =   """tokenizer_file"""
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				False  # because we don't have pretrained_vocab_files_map
        super().setUp()
        A_							:       Optional[Any]	      =				True

        A_							:       Tuple	      =				['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
        A_							:       Any	      =				'''SaulLu/dummy-tokenizer-bytelevel-bpe'''

        # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
        A_							:       Optional[Any]	      =				[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

        A_							:       str	      =				PreTrainedTokenizerFast.from_pretrained(model_paths[0]      )
        tokenizer.save_pretrained(self.tmpdirname      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        pass
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        pass
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        pass
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        pass
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				self.tmpdirname
        # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
                      try:
                             A_							:       str	      =				tempfile.mkdtemp()
                             A_							:       List[str]	      =				self.rust_tokenizer_class.from_pretrained(__magic_name__ ,       **__magic_name__      )

                             tokenizer.save_pretrained(self.tmpdirname      )
                             super().test_training_new_tokenizer()
                      finally:
                             # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                             # is restored
                             shutil.rmtree(self.tmpdirname      )
                             A_							:       List[str]	      =				tmpdirname_orig
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       str	      =				self.tmpdirname
        # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
                      try:
                             A_							:       List[Any]	      =				tempfile.mkdtemp()
                             A_							:       Union[str, Any]	      =				self.rust_tokenizer_class.from_pretrained(__magic_name__ ,       **__magic_name__      )

                             tokenizer.save_pretrained(self.tmpdirname      )
                             super().test_training_new_tokenizer_with_special_tokens_change()
                      finally:
                             # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                             # is restored
                             shutil.rmtree(self.tmpdirname      )
                             A_							:       Union[str, Any]	      =				tmpdirname_orig
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name      )

        A_							:       Optional[Any]	      =				('''a''' for _ in range(1000      ))
        A_							:       Any	      =				tokenizer.train_new_from_iterator(text_iterator=__magic_name__ ,       length=1000 ,       vocab_size=50      )

        A_							:       Optional[Any]	      =				new_tokenizer.encode('''a🤗'''      )
        self.assertEqual(__magic_name__ ,       [64, 172, 253, 97, 245]      )






@require_tokenizers
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Dict	      =				AutoTokenizer.from_pretrained('''bert-base-cased'''      )
        A_							:       int	      =				json.loads(tokenizer._tokenizer.to_str()      )
        A_							:       Optional[int]	      =				len(__magic_name__      )

        with tempfile.TemporaryDirectory() as tmp_dir:
               # Hack to save this in the tokenizer_config.json
               A_							:       str	      =				['''tokenizer.4.0.0.json''']
               tokenizer.save_pretrained(__magic_name__      )
               json.dump(__magic_name__ ,       open(os.path.join(__magic_name__ ,       '''tokenizer.4.0.0.json'''      ) ,       '''w'''      )      )

               # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
               A_							:       str	      =				AutoTokenizer.from_pretrained(__magic_name__      )
               self.assertEqual(len(__magic_name__      ) ,       len(__magic_name__      ) + 1      )
               A_							:       str	      =				json.loads(new_tokenizer._tokenizer.to_str()      )
               self.assertIn('''huggingface''' ,       json_tokenizer['''model''']['''vocab''']      )

               # Will need to be adjusted if we reach v42 and this test is still here.
               # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
               shutil.move(os.path.join(__magic_name__ ,       '''tokenizer.4.0.0.json'''      ) ,       os.path.join(__magic_name__ ,       '''tokenizer.42.0.0.json'''      )      )
               A_							:       Dict	      =				['''tokenizer.42.0.0.json''']
               tokenizer.save_pretrained(__magic_name__      )
               A_							:       int	      =				AutoTokenizer.from_pretrained(__magic_name__      )
               self.assertEqual(len(__magic_name__      ) ,       len(__magic_name__      )      )
               A_							:       Optional[int]	      =				json.loads(new_tokenizer._tokenizer.to_str()      )
               self.assertNotIn('''huggingface''' ,       json_tokenizer['''model''']['''vocab''']      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[Any]	      =				'''hf-internal-testing/test-two-tokenizers'''

        # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
        A_							:       Optional[int]	      =				AutoTokenizer.from_pretrained(__magic_name__      )
        self.assertEqual(len(__magic_name__      ) ,       2_8997      )
        A_							:       Optional[int]	      =				json.loads(tokenizer._tokenizer.to_str()      )
        self.assertIn('''huggingface''' ,       json_tokenizer['''model''']['''vocab''']      )

        # Testing an older version by monkey-patching the version in the module it's used.
        import transformers as old_transformers

        A_							:       Optional[Any]	      =				'''3.0.0'''
        A_							:       List[Any]	      =				old_transformers.models.auto.AutoTokenizer.from_pretrained(__magic_name__      )
        self.assertEqual(len(__magic_name__      ) ,       2_8996      )
        A_							:       Union[str, Any]	      =				json.loads(old_tokenizer._tokenizer.to_str()      )
        self.assertNotIn('''huggingface''' ,       json_tokenizer['''model''']['''vocab''']      )






@require_tokenizers
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[Any]	      =				PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''      )
        A_							:       Tuple	      =				'''The Matrix is a 1999 science fiction action film.'''

        with concurrent.futures.ThreadPoolExecutor() as executor:
               A_							:       Dict	      =				[executor.submit(self.fetch ,       __magic_name__ ,       __magic_name__      ) for i in range(10      )]
               A_							:       Optional[int]	      =				[future.result() for future in futures]
               self.assertEqual(__magic_name__ ,       [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10      )]      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__      ):

        """simple docstring"""

        return tokenizer.encode(__magic_name__ ,       truncation='''longest_first''' ,       padding='''longest'''      )



def       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a  ,   )		->	tuple[list[list[int]], list[list[str]]]:
       A_							:       Optional[Any]	      =				list(a							)
       A_							:       int	      =				list(a							)
       A_							:       Union[str, Any]	      =				len(a							)
       A_							:       Any	      =				len(a							)

       A_							:       Union[str, Any]	      =				[
           [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]
       A_							:       Optional[int]	      =				[
           ['''0''' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]

       for i in range(1  ,   len_source_seq + 1							):
              A_							:       List[Any]	      =				i * delete_cost
              A_							:       List[Any]	      =				f"""D{source_seq[i - 1]:c}"""

       for i in range(1  ,   len_destination_seq + 1							):
              A_							:       Optional[Any]	      =				i * insert_cost
              A_							:       str	      =				f"""I{destination_seq[i - 1]:c}"""

       for i in range(1  ,   len_source_seq + 1							):
              for j in range(1  ,   len_destination_seq + 1							):
                     if source_seq[i - 1] == destination_seq[j - 1]:
                            A_							:       List[Any]	      =				costs[i - 1][j - 1] + copy_cost
                            A_							:       Tuple	      =				f"""C{source_seq[i - 1]:c}"""
                     else:
                            A_							:       Union[str, Any]	      =				costs[i - 1][j - 1] + replace_cost
                            A_							:       Dict	      =				f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]							)

                     if costs[i - 1][j] + delete_cost < costs[i][j]:
                            A_							:       Dict	      =				costs[i - 1][j] + delete_cost
                            A_							:       Optional[int]	      =				f"""D{source_seq[i - 1]:c}"""

                     if costs[i][j - 1] + insert_cost < costs[i][j]:
                            A_							:       List[Any]	      =				costs[i][j - 1] + insert_cost
                            A_							:       Tuple	      =				f"""I{destination_seq[j - 1]:c}"""

       return costs, ops


def       a__      (					a  ,   a  ,   a							)		->	list[str]:
       if i == 0 and j == 0:
              return []
       else:
              if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                     A_							:       Tuple	      =				assemble_transformation(a  ,   i - 1  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq
              elif ops[i][j][0] == "D":
                     A_							:       Any	      =				assemble_transformation(a  ,   i - 1  ,   a							)
                     seq.append(ops[i][j]							)
                     return seq
              else:
                     A_							:       Tuple	      =				assemble_transformation(a  ,   a  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq


if __name__ == "__main__":
    _lowerCAmelCase  ,			_lowerCAmelCase        =       compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

    _lowerCAmelCase        =       len(operations)
    _lowerCAmelCase        =       len(operations[0])
    _lowerCAmelCase        =       assemble_transformation(operations, m - 1, n - 1)

    _lowerCAmelCase        =       list('Python')
    _lowerCAmelCase        =       0
    _lowerCAmelCase        =       0

    with open('min_cost.txt', 'w') as file:
        for op in sequence:
            print(''.join(string))

            if op[0] == "C":
                file.write('%-16s' % 'Copy %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost -= 1
            elif op[0] == "R":
                _lowerCAmelCase        =       op[2]

                file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
                file.write('\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 1
            elif op[0] == "D":
                string.pop(i)

                file.write('%-16s' % 'Delete %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 2
            else:
                string.insert(i, op[1])

                file.write('%-16s' % 'Insert %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 2

            i += 1

        print(''.join(string))
        print('Cost: ', cost)

        file.write('\r\nMinimum cost: ' + str(cost))


