

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowerCAmelCase_			      =      logging.get_logger(__name__)

lowerCAmelCase_			      =      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_			      =      {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase_			      =      {
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  int		     =							VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE	:  List[Any]		     =							PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE	:  List[str]		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE	:  Union[str, Any]		     =							["input_ids", "attention_mask"]
			SCREAMING_SNAKE_CASE	:  int		     =							RobertaTokenizer







			def __init__(	self					:							int				,  _UpperCamelCase					:							Union[str, Any]=None				,  _UpperCamelCase					:							Optional[Any]=None				,  _UpperCamelCase					:							List[str]=None				,  _UpperCamelCase					:							List[str]="replace"				,  _UpperCamelCase					:							Any="<s>"				,  _UpperCamelCase					:							str="</s>"				,  _UpperCamelCase					:							Any="</s>"				,  _UpperCamelCase					:							List[str]="<s>"				,  _UpperCamelCase					:							Any="<unk>"				,  _UpperCamelCase					:							Tuple="<pad>"				,  _UpperCamelCase					:							Union[str, Any]="<mask>"				,  _UpperCamelCase					:							int=False				,  _UpperCamelCase					:							Union[str, Any]=True				,  **_UpperCamelCase					:							Tuple				,  ) ->int:
									super().__init__(
									    _UpperCamelCase				,  _UpperCamelCase				,  tokenizer_file=_UpperCamelCase				,  errors=_UpperCamelCase				,  bos_token=_UpperCamelCase				,  eos_token=_UpperCamelCase				,  sep_token=_UpperCamelCase				,  cls_token=_UpperCamelCase				,  unk_token=_UpperCamelCase				,  pad_token=_UpperCamelCase				,  mask_token=_UpperCamelCase				,  add_prefix_space=_UpperCamelCase				,  trim_offsets=_UpperCamelCase				,  **_UpperCamelCase				,  )

									snake_case_  =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
									if pre_tok_state.get('''add_prefix_space'''				,  _UpperCamelCase				) != add_prefix_space:
															snake_case_  =      getattr(_UpperCamelCase				,  pre_tok_state.pop('''type'''				)				)
															snake_case_  =      add_prefix_space
															snake_case_  =      pre_tok_class(**_UpperCamelCase				)

									snake_case_  =      add_prefix_space

									snake_case_  =      '''post_processor'''
									snake_case_  =      getattr(self.backend_tokenizer				,  _UpperCamelCase				,  _UpperCamelCase				)
									if tokenizer_component_instance:
															snake_case_  =      json.loads(tokenizer_component_instance.__getstate__()				)

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																					snake_case_  =      tuple(state['''sep''']				)
															if "cls" in state:
																					snake_case_  =      tuple(state['''cls''']				)

															snake_case_  =      False

															if state.get('''add_prefix_space'''				,  _UpperCamelCase				) != add_prefix_space:
																					snake_case_  =      add_prefix_space
																					snake_case_  =      True

															if state.get('''trim_offsets'''				,  _UpperCamelCase				) != trim_offsets:
																					snake_case_  =      trim_offsets
																					snake_case_  =      True

															if changes_to_apply:
																					snake_case_  =      getattr(_UpperCamelCase				,  state.pop('''type'''				)				)
																					snake_case_  =      component_class(**_UpperCamelCase				)
																					setattr(self.backend_tokenizer				,  _UpperCamelCase				,  _UpperCamelCase				)







			@property
			def 							snake_case__(	self					:							Union[str, Any]				) ->str:
									if self._mask_token is None:
															if self.verbose:
																					logger.error('''Using mask_token, but it is not set yet.'''				)
															return None
									return str(self._mask_token				)







			@mask_token.setter
			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							List[Any]				) ->Any:
									snake_case_  =      AddedToken(_UpperCamelCase				,  lstrip=_UpperCamelCase				,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase				,  _UpperCamelCase				) else value
									snake_case_  =      value







			def 							snake_case__(	self					:							Any				,  *_UpperCamelCase					:							Union[str, Any]				,  **_UpperCamelCase					:							Any				) ->BatchEncoding:
									snake_case_  =      kwargs.get('''is_split_into_words'''				,  _UpperCamelCase				)
									assert self.add_prefix_space or not is_split_into_words, (
									    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
									    "to use it with pretokenized inputs."
									)

									return super()._batch_encode_plus(*_UpperCamelCase				,  **_UpperCamelCase				)







			def 							snake_case__(	self					:							str				,  *_UpperCamelCase					:							List[str]				,  **_UpperCamelCase					:							str				) ->BatchEncoding:
									snake_case_  =      kwargs.get('''is_split_into_words'''				,  _UpperCamelCase				)

									assert self.add_prefix_space or not is_split_into_words, (
									    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
									    "to use it with pretokenized inputs."
									)

									return super()._encode_plus(*_UpperCamelCase				,  **_UpperCamelCase				)







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Optional[str] = None				) ->Tuple[str]:
									snake_case_  =      self._tokenizer.model.save(_UpperCamelCase				,  name=_UpperCamelCase				)
									return tuple(_UpperCamelCase				)







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Tuple=None				) ->Dict:
									snake_case_  =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
															return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







			def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							List[int]				,  _UpperCamelCase					:							Optional[List[int]] = None				) ->List[int]:
									snake_case_  =      [self.sep_token_id]
									snake_case_  =      [self.cls_token_id]

									if token_ids_a is None:
															return len(cls + token_ids_a + sep				) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]





import os
import sys
import unittest


UpperCamelCase__ :   Optional[Any]						=							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCamelCase__ :   Tuple						=							os.path.join(git_repo_path, '''src''', '''transformers''')

UpperCamelCase__ :   List[Any]						=							'''
{0} = None
'''

UpperCamelCase__ :   str						=							'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


UpperCamelCase__ :   List[Any]						=							'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''







class 				lowerCAmelCase_       (					unittest.TestCase							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
							self.assertIsNone(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      find_backend('    if not is_tokenizers_available():' )
							self.assertEqual(snake_case__      ,'tokenizers' )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      find_backend('    if not is_tensorflow_text_available():' )
							self.assertEqual(snake_case__      ,'tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers' )

							SCREAMING_SNAKE_CASE_   : str					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers_and_vision' )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn('torch'      ,snake_case__ )
							self.assertIn('tensorflow_text'      ,snake_case__ )
							self.assertIn('sentencepiece_and_tokenizers'      ,snake_case__ )

							# Likewise, we can't assert on the exact content of a key
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertModel'      ,objects['tf'] )
							self.assertIn('FlaxBertModel'      ,objects['flax'] )
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertTokenizer'      ,objects['tensorflow_text'] )
							self.assertIn('convert_slow_tokenizer'      ,objects['sentencepiece_and_tokenizers'] )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      create_dummy_object('CONSTANT'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,'\nCONSTANT = None\n' )

							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('function'      ,'\'torch\'' )
							self.assertEqual(
							    snake_case__      ,'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('FakeClass'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
							self.assertEqual(dummy_files['torch']      ,snake_case__ )



