






import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


class 						_a       (  UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   CanineTokenizer
  A_						=   False




  def    _UpperCAmelCase     (     self )			-> Any:
     super().setUp()
     UpperCamelCase_									=     CanineTokenizer()
     tokenizer.save_pretrained(self.tmpdirname )




  @cached_property
  def    _UpperCAmelCase     (     self )			-> List[str]:
     return CanineTokenizer.from_pretrained('google/canine-s' )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> CanineTokenizer:
     UpperCamelCase_									=     self.tokenizer_class.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )
     UpperCamelCase_									=     1024
     return tokenizer




  @require_torch
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.canine_tokenizer
     UpperCamelCase_									=     ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
     # fmt: off
     UpperCamelCase_									=     [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
     # fmt: on
     UpperCamelCase_									=     tokenizer(_UpperCAmelCase		,		padding=_UpperCAmelCase		,		return_tensors='pt' )
     self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )

     UpperCamelCase_									=     list(batch.input_ids.numpy()[0] )

     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )

     self.assertEqual((2, 39)		,		batch.input_ids.shape )
     self.assertEqual((2, 39)		,		batch.attention_mask.shape )




  @require_torch
  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     self.canine_tokenizer
     UpperCamelCase_									=     ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
     UpperCamelCase_									=     tokenizer(_UpperCAmelCase		,		padding=_UpperCAmelCase		,		return_tensors='pt' )
     # check if input_ids, attention_mask and token_type_ids are returned
     self.assertIn('input_ids'		,		_UpperCAmelCase )
     self.assertIn('attention_mask'		,		_UpperCAmelCase )
     self.assertIn('token_type_ids'		,		_UpperCAmelCase )




  @require_torch
  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     self.canine_tokenizer
     UpperCamelCase_									=     [
         'What\'s the weater?',
         'It\'s about 25 degrees.',
     ]
     UpperCamelCase_									=     tokenizer(
         text_target=_UpperCAmelCase		,		max_length=32		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		return_tensors='pt' )
     self.assertEqual(32		,		targets['input_ids'].shape[1] )




  def    _UpperCAmelCase     (     self )			-> Dict:
     # safety check on max_len default value so we are sure the test works
     UpperCamelCase_									=     self.get_tokenizers()
     for tokenizer in tokenizers:
        with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
           self.assertNotEqual(tokenizer.model_max_length		,		42 )

        # Now let's start the test
     UpperCamelCase_									=     self.get_tokenizers()
     for tokenizer in tokenizers:
        with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
           # Isolate this from the other tests because we save additional tokens/etc
           UpperCamelCase_									=     tempfile.mkdtemp()

           UpperCamelCase_									=     ' He is very happy, UNwant\u00E9d,running'
           UpperCamelCase_									=     tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase )
           tokenizer.save_pretrained(_UpperCAmelCase )

           UpperCamelCase_									=     tokenizer.__class__.from_pretrained(_UpperCAmelCase )
           UpperCamelCase_									=     after_tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase )
           self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )

           shutil.rmtree(_UpperCAmelCase )

     UpperCamelCase_									=     self.get_tokenizers(model_max_length=42 )
     for tokenizer in tokenizers:
        with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
           # Isolate this from the other tests because we save additional tokens/etc
           UpperCamelCase_									=     tempfile.mkdtemp()

           UpperCamelCase_									=     ' He is very happy, UNwant\u00E9d,running'

           UpperCamelCase_									=     tokenizer.additional_special_tokens

           # We can add a new special token for Canine as follows:
           UpperCamelCase_									=     chr(0xE007 )
           additional_special_tokens.append(_UpperCAmelCase )
           tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
           UpperCamelCase_									=     tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase )
           tokenizer.save_pretrained(_UpperCAmelCase )

           UpperCamelCase_									=     tokenizer.__class__.from_pretrained(_UpperCAmelCase )
           UpperCamelCase_									=     after_tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase )
           self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )
           self.assertIn(_UpperCAmelCase		,		after_tokenizer.additional_special_tokens )
           self.assertEqual(after_tokenizer.model_max_length		,		42 )

           UpperCamelCase_									=     tokenizer.__class__.from_pretrained(_UpperCAmelCase		,		model_max_length=43 )
           self.assertEqual(tokenizer.model_max_length		,		43 )

           shutil.rmtree(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     self.get_tokenizers(do_lower_case=_UpperCAmelCase )
     for tokenizer in tokenizers:
        with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
           UpperCamelCase_		,      UpperCamelCase_									=     self.get_clean_sequence(_UpperCAmelCase )

           # a special token for Canine can be defined as follows:
           UpperCamelCase_									=     0xE005
           UpperCamelCase_									=     chr(_UpperCAmelCase )

           tokenizer.add_special_tokens({'cls_token': special_token} )
           UpperCamelCase_									=     tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase )
           self.assertEqual(len(_UpperCAmelCase )		,		1 )

           UpperCamelCase_									=     tokenizer.decode(ids + encoded_special_token		,		clean_up_tokenization_spaces=_UpperCAmelCase )
           UpperCamelCase_									=     tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase )

           UpperCamelCase_									=     tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase )
           UpperCamelCase_									=     tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase )
           self.assertEqual(_UpperCAmelCase		,		input_encoded + special_token_id )

           UpperCamelCase_									=     tokenizer.decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase )
           self.assertTrue(special_token not in decoded )




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     self.get_tokenizers(do_lower_case=_UpperCAmelCase )
     for tokenizer in tokenizers:
        with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
           UpperCamelCase_									=     chr(0xE005 )
           UpperCamelCase_									=     chr(0xE006 )

           # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
           tokenizer.add_tokens([SPECIAL_TOKEN_1]		,		special_tokens=_UpperCAmelCase )
           # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
           # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
           tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]} )

           UpperCamelCase_									=     tokenizer.tokenize(_UpperCAmelCase )
           UpperCamelCase_									=     tokenizer.tokenize(_UpperCAmelCase )

           self.assertEqual(len(_UpperCAmelCase )		,		1 )
           self.assertEqual(len(_UpperCAmelCase )		,		1 )
           self.assertEqual(token_a[0]		,		_UpperCAmelCase )
           self.assertEqual(token_a[0]		,		_UpperCAmelCase )




  @require_tokenizers
  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     self.get_tokenizers(do_lower_case=_UpperCAmelCase )
     for tokenizer in tokenizers:
        with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
           # a special token for Canine can be defined as follows:
           UpperCamelCase_									=     0xE006
           UpperCamelCase_									=     chr(_UpperCAmelCase )

           UpperCamelCase_									=     AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase )
           tokenizer.add_special_tokens({'additional_special_tokens': [new_token]} )

           with tempfile.TemporaryDirectory() as tmp_dir_name:
              tokenizer.save_pretrained(_UpperCAmelCase )
              tokenizer.from_pretrained(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     []
     if self.test_slow_tokenizer:
        tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

     if self.test_rust_tokenizer:
        tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

     for tokenizer_class, tokenizer_utils in tokenizer_list:
        with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer_utils.save_pretrained(_UpperCAmelCase )

           with open(os.path.join(_UpperCAmelCase		,		'special_tokens_map.json' )		,		encoding='utf-8' ) as json_file:
              UpperCamelCase_									=     json.load(_UpperCAmelCase )

           with open(os.path.join(_UpperCAmelCase		,		'tokenizer_config.json' )		,		encoding='utf-8' ) as json_file:
              UpperCamelCase_									=     json.load(_UpperCAmelCase )

           # a special token for Canine can be defined as follows:
           UpperCamelCase_									=     0xE006
           UpperCamelCase_									=     chr(_UpperCAmelCase )

           UpperCamelCase_									=     [new_token_a]
           UpperCamelCase_									=     [new_token_a]

           with open(os.path.join(_UpperCAmelCase		,		'special_tokens_map.json' )		,		'w'		,		encoding='utf-8' ) as outfile:
              json.dump(_UpperCAmelCase		,		_UpperCAmelCase )
           with open(os.path.join(_UpperCAmelCase		,		'tokenizer_config.json' )		,		'w'		,		encoding='utf-8' ) as outfile:
              json.dump(_UpperCAmelCase		,		_UpperCAmelCase )

           # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
           # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
           # "special_tokens_map.json" files
           UpperCamelCase_									=     tokenizer_class.from_pretrained(_UpperCAmelCase		,		extra_ids=0 )
           self.assertIn(_UpperCAmelCase		,		tokenizer_without_change_in_init.additional_special_tokens )
           # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
           self.assertEqual(
               [new_token_a]		,		tokenizer_without_change_in_init.convert_ids_to_tokens(
                   tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) )		,		)

           UpperCamelCase_									=     0xE007
           UpperCamelCase_									=     chr(_UpperCAmelCase )
           # Now we test that we can change the value of additional_special_tokens in the from_pretrained
           UpperCamelCase_									=     [AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase )]
           UpperCamelCase_									=     tokenizer_class.from_pretrained(
               _UpperCAmelCase		,		additional_special_tokens=_UpperCAmelCase		,		extra_ids=0 )

           self.assertIn(_UpperCAmelCase		,		tokenizer.additional_special_tokens )
           # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
           self.assertEqual(
               [new_token_a]		,		tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )




  @require_tokenizers
  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     self.get_tokenizers(do_lower_case=_UpperCAmelCase )
     for tokenizer in tokenizers:
        with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
           UpperCamelCase_									=     'hello world'
           if self.space_between_special_tokens:
              UpperCamelCase_									=     '[CLS] hello world [SEP]'
           else:
              UpperCamelCase_									=     input
           UpperCamelCase_									=     tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase )
           UpperCamelCase_									=     tokenizer.decode(_UpperCAmelCase		,		spaces_between_special_tokens=self.space_between_special_tokens )
           self.assertIn(_UpperCAmelCase		,		[output, output.lower()] )




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.get_tokenizers()
     for tokenizer in tokenizers:
        with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
           UpperCamelCase_									=     [
               'bos_token',
               'eos_token',
               'unk_token',
               'sep_token',
               'pad_token',
               'cls_token',
               'mask_token',
           ]

     UpperCamelCase_									=     'a'
     UpperCamelCase_									=     ord(_UpperCAmelCase )

     for attr in attributes_list:
        setattr(_UpperCAmelCase		,		attr + '_id'		,		_UpperCAmelCase )
        self.assertEqual(getattr(_UpperCAmelCase		,		_UpperCAmelCase )		,		_UpperCAmelCase )
        self.assertEqual(getattr(_UpperCAmelCase		,		attr + '_id' )		,		_UpperCAmelCase )

        setattr(_UpperCAmelCase		,		attr + '_id'		,		_UpperCAmelCase )
        self.assertEqual(getattr(_UpperCAmelCase		,		_UpperCAmelCase )		,		_UpperCAmelCase )
        self.assertEqual(getattr(_UpperCAmelCase		,		attr + '_id' )		,		_UpperCAmelCase )

     setattr(_UpperCAmelCase		,		'additional_special_tokens_ids'		,		[] )
     self.assertListEqual(getattr(_UpperCAmelCase		,		'additional_special_tokens' )		,		[] )
     self.assertListEqual(getattr(_UpperCAmelCase		,		'additional_special_tokens_ids' )		,		[] )

     UpperCamelCase_									=     0xE006
     UpperCamelCase_									=     chr(_UpperCAmelCase )
     setattr(_UpperCAmelCase		,		'additional_special_tokens_ids'		,		[additional_special_token_id] )
     self.assertListEqual(getattr(_UpperCAmelCase		,		'additional_special_tokens' )		,		[additional_special_token] )
     self.assertListEqual(getattr(_UpperCAmelCase		,		'additional_special_tokens_ids' )		,		[additional_special_token_id] )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     pass




  def    _UpperCAmelCase     (     self )			-> Any:
     pass




  def    _UpperCAmelCase     (     self )			-> List[str]:
     pass




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     pass




  def    _UpperCAmelCase     (     self )			-> Tuple:
     pass




  def    _UpperCAmelCase     (     self )			-> Any:
     pass




  def    _UpperCAmelCase     (     self )			-> Dict:
     pass




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     pass













from typing import Any






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validation(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   # Creates data structures and fill initial step
   UpperCamelCase_									=     {}
   UpperCamelCase_									=     {}
   for state in states_space:
      UpperCamelCase_									=     observations_space[0]
      UpperCamelCase_									=     (
          initial_probabilities[state] * emission_probabilities[state][observation]
      )
      UpperCamelCase_									=     None

   # Fills the data structure with the probabilities of
   # different transitions and pointers to previous states
   for o in range(1    ,      len(__lowercase)):
      UpperCamelCase_									=     observations_space[o]
      UpperCamelCase_									=     observations_space[o - 1]
      for state in states_space:
         # Calculates the argmax for probability function
         UpperCamelCase_									=     ''
         UpperCamelCase_									=     -1
         for k_state in states_space:
            UpperCamelCase_									=     (
                probabilities[(k_state, prior_observation)]
                * transition_probabilities[k_state][state]
                * emission_probabilities[state][observation]
            )
            if probability > max_probability:
               UpperCamelCase_									=     probability
               UpperCamelCase_									=     k_state

            # Update probabilities and pointers dicts
         UpperCamelCase_									=     (
             probabilities[(arg_max, prior_observation)]
             * transition_probabilities[arg_max][state]
             * emission_probabilities[state][observation]
         )

         UpperCamelCase_									=     arg_max

    # The final observation
   UpperCamelCase_									=     observations_space[len(__lowercase) - 1]

   # argmax for given final observation
   UpperCamelCase_									=     ''
   UpperCamelCase_									=     -1
   for k_state in states_space:
      UpperCamelCase_									=     probabilities[(k_state, final_observation)]
      if probability > max_probability:
         UpperCamelCase_									=     probability
         UpperCamelCase_									=     k_state
   UpperCamelCase_									=     arg_max

   # Process pointers backwards
   UpperCamelCase_									=     last_state
   UpperCamelCase_									=     []
   for o in range(len(__lowercase) - 1    ,      -1    ,      -1):
      result.append(__lowercase)
      UpperCamelCase_									=     pointers[previous, observations_space[o]]
   result.reverse()

   return result






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_not_empty(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   _validate_lists(__lowercase    ,      __lowercase)
   _validate_dicts(
       __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   if not all(
       [
           observations_space,
           states_space,
           initial_probabilities,
           transition_probabilities,
           emission_probabilities,
       ]):
      raise ValueError('There\'s an empty parameter')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_list(__lowercase    ,      'observations_space')
   _validate_list(__lowercase    ,      'states_space')






def     _snake_case    (__lowercase    ,      __lowercase):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a list"""
      raise ValueError(__lowercase)
   else:
      for x in _object:
         if not isinstance(__lowercase    ,      __lowercase):
            UpperCamelCase_									=     f"""{var_name} must be a list of strings"""
            raise ValueError(__lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_dict(__lowercase    ,      'initial_probabilities'    ,      __lowercase)
   _validate_nested_dict(__lowercase    ,      'transition_probabilities')
   _validate_nested_dict(__lowercase    ,      'emission_probabilities')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_dict(_object    ,      __lowercase    ,      __lowercase)
   for x in _object.values():
      _validate_dict(__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase = False):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a dict"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object):
      UpperCamelCase_									=     f"""{var_name} all keys must be strings"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object.values()):
      UpperCamelCase_									=     'nested dictionary ' if nested else ''
      UpperCamelCase_									=     f"""{var_name} {nested_text}all values must be {value_type.__name__}"""
      raise ValueError(__lowercase)


if __name__ == "__main__":
 from doctest import testmod

 testmod()





