

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 						lowercase_					(						__lowercase     ,							unittest.TestCase  ):
      UpperCamelCase_    :	List[Any]		   =       PreTrainedTokenizerFast
      UpperCamelCase_    :	Tuple		   =       False
      UpperCamelCase_    :	Union[str, Any]		   =       True
      UpperCamelCase_    :	List[Any]		   =       "tokenizer_file"



      def 		UpperCamelCase_	(    self   :       List[Any]	)					->  Optional[int]:
        _snake_case		=      False  # because we don't have pretrained_vocab_files_map
        super().setUp()
        _snake_case		=      True

        _snake_case		=      ['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
        _snake_case		=      '''SaulLu/dummy-tokenizer-bytelevel-bpe'''

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

        _snake_case		=      PreTrainedTokenizerFast.from_pretrained(model_paths[0]	)
        tokenizer.save_pretrained(self.tmpdirname	)



      def 		UpperCamelCase_	(    self   :       str	)					->  Tuple:
        # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
        # model
        pass



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Tuple:
        # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
        # model
        pass



      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  int:
        # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
        # model
        pass



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Dict:
        # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
        pass



      def 		UpperCamelCase_	(    self   :       int	)					->  Dict:
        _snake_case		=      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:
              _snake_case		=      tempfile.mkdtemp()
              _snake_case		=      self.rust_tokenizer_class.from_pretrained(A__    ,      **A__	)

              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	)
              _snake_case		=      tmpdirname_orig



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  int:
        _snake_case		=      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:
              _snake_case		=      tempfile.mkdtemp()
              _snake_case		=      self.rust_tokenizer_class.from_pretrained(A__    ,      **A__	)

              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	)
              _snake_case		=      tmpdirname_orig







      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Tuple:
        _snake_case		=      self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name	)

        _snake_case		=      ('''a''' for _ in range(1000	))
        _snake_case		=      tokenizer.train_new_from_iterator(text_iterator=A__    ,      length=1000    ,      vocab_size=50	)

        _snake_case		=      new_tokenizer.encode('''a🤗'''	)
        self.assertEqual(A__    ,      [64, 172, 253, 97, 245]	)



@require_tokenizers
class 						lowercase_					(						unittest.TestCase  ):



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Any:
        _snake_case		=      AutoTokenizer.from_pretrained('''bert-base-cased'''	)
        _snake_case		=      json.loads(tokenizer._tokenizer.to_str()	)
        _snake_case		=      len(A__	)

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

          # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
          _snake_case		=      AutoTokenizer.from_pretrained(A__	)
          self.assertEqual(len(A__	)    ,      len(A__	) + 1	)
          _snake_case		=      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(A__    ,      '''tokenizer.4.0.0.json'''	)    ,      os.path.join(A__    ,      '''tokenizer.42.0.0.json'''	)	)
          _snake_case		=      ['''tokenizer.42.0.0.json''']
          tokenizer.save_pretrained(A__	)
          _snake_case		=      AutoTokenizer.from_pretrained(A__	)
          self.assertEqual(len(A__	)    ,      len(A__	)	)
          _snake_case		=      json.loads(new_tokenizer._tokenizer.to_str()	)
          self.assertNotIn('''huggingface'''    ,      json_tokenizer['''model''']['''vocab''']	)







      def 		UpperCamelCase_	(    self   :       str	)					->  Optional[int]:
        # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
        _snake_case		=      '''hf-internal-testing/test-two-tokenizers'''

        # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
        _snake_case		=      AutoTokenizer.from_pretrained(A__	)
        self.assertEqual(len(A__	)    ,      28997	)
        _snake_case		=      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

        _snake_case		=      '''3.0.0'''
        _snake_case		=      old_transformers.models.auto.AutoTokenizer.from_pretrained(A__	)
        self.assertEqual(len(A__	)    ,      28996	)
        _snake_case		=      json.loads(old_tokenizer._tokenizer.to_str()	)
        self.assertNotIn('''huggingface'''    ,      json_tokenizer['''model''']['''vocab''']	)



@require_tokenizers
class 						lowercase_					(						unittest.TestCase  ):



      def 		UpperCamelCase_	(    self   :       Dict	)					->  Union[str, Any]:
        # See https://github.com/huggingface/transformers/pull/12550
        # and https://github.com/huggingface/tokenizers/issues/537
        _snake_case		=      PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''	)
        _snake_case		=      '''The Matrix is a 1999 science fiction action film.'''

        with concurrent.futures.ThreadPoolExecutor() as executor:
          _snake_case		=      [executor.submit(self.fetch    ,      A__    ,      A__	) for i in range(10	)]
          _snake_case		=      [future.result() for future in futures]
          self.assertEqual(A__    ,      [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10	)]	)







      def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       List[str]    ,      A__   :       List[Any]	)					->  int:
        return tokenizer.encode(A__    ,      truncation='''longest_first'''    ,      padding='''longest'''	)




__A    	=			{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def    snake_case_(_UpperCamelCase  )							->			str:



  """simple docstring"""






  return c.isdigit()



def    snake_case_(_UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							[]

  # iterate over the string in reverse order
  for c in expression.split()[::-1]:
    # push operand to stack
    if is_operand(_UpperCamelCase  ):
      stack.append(int(_UpperCamelCase  )  )

    else:
      # pop values from stack can calculate the result
      # push the result onto the stack again
      _snake_case		=							stack.pop()
      _snake_case		=							stack.pop()
      stack.append(calc[c](_UpperCamelCase				, _UpperCamelCase  )  )

  return stack.pop()


# Driver code
if __name__ == "__main__":
 __A    	=			'''+ 9 * 2 6'''
 print(evaluate(test_expression))

 __A    	=			'''/ * 10 2 + 4 1 '''
 print(evaluate(test_expression))

