
"""simple docstring"""




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


UpperCAmelCase__      :    Union[str, Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    List[str]    							= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__      :    Tuple    							= {
    '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'
        ),
    },
}

UpperCAmelCase__      :    Any    							= {
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[int]					       =   VOCAB_FILES_NAMES
				__UpperCamelCase      :						Any					       =   PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase      :						Optional[int]					       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase      :						List[str]					       =   ['''input_ids''', '''attention_mask''']
				__UpperCamelCase      :						str					       =   RobertaTokenizer



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__="replace"   ,       SCREAMING_SNAKE_CASE__="<s>"   ,       SCREAMING_SNAKE_CASE__="</s>"   ,       SCREAMING_SNAKE_CASE__="</s>"   ,       SCREAMING_SNAKE_CASE__="<s>"   ,       SCREAMING_SNAKE_CASE__="<unk>"   ,       SCREAMING_SNAKE_CASE__="<pad>"   ,       SCREAMING_SNAKE_CASE__="<mask>"   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=True   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Dict:



											"""simple docstring"""




											super().__init__(
											    SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       tokenizer_file=SCREAMING_SNAKE_CASE__   ,       errors=SCREAMING_SNAKE_CASE__   ,       bos_token=SCREAMING_SNAKE_CASE__   ,       eos_token=SCREAMING_SNAKE_CASE__   ,       sep_token=SCREAMING_SNAKE_CASE__   ,       cls_token=SCREAMING_SNAKE_CASE__   ,       unk_token=SCREAMING_SNAKE_CASE__   ,       pad_token=SCREAMING_SNAKE_CASE__   ,       mask_token=SCREAMING_SNAKE_CASE__   ,       add_prefix_space=SCREAMING_SNAKE_CASE__   ,       trim_offsets=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__   ,       )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
											if pre_tok_state.get("""add_prefix_space"""   ,       SCREAMING_SNAKE_CASE__    ) != add_prefix_space:
																		SCREAMING_SNAKE_CASE__						:       str           =   getattr(SCREAMING_SNAKE_CASE__   ,       pre_tok_state.pop("""type"""    )    )
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   add_prefix_space
																		SCREAMING_SNAKE_CASE__						:       Dict           =   pre_tok_class(**SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       int           =   add_prefix_space

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   """post_processor"""
											SCREAMING_SNAKE_CASE__						:       str           =   getattr(self.backend_tokenizer   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											if tokenizer_component_instance:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   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:
																									SCREAMING_SNAKE_CASE__						:       Dict           =   tuple(state["""sep"""]    )
																		if "cls" in state:
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tuple(state["""cls"""]    )

																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   False

																		if state.get("""add_prefix_space"""   ,       SCREAMING_SNAKE_CASE__    ) != add_prefix_space:
																									SCREAMING_SNAKE_CASE__						:       int           =   add_prefix_space
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   True

																		if state.get("""trim_offsets"""   ,       SCREAMING_SNAKE_CASE__    ) != trim_offsets:
																									SCREAMING_SNAKE_CASE__						:       int           =   trim_offsets
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   True

																		if changes_to_apply:
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   getattr(SCREAMING_SNAKE_CASE__   ,       state.pop("""type"""    )    )
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   component_class(**SCREAMING_SNAKE_CASE__    )
																									setattr(self.backend_tokenizer   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				@property
				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   AddedToken(SCREAMING_SNAKE_CASE__   ,       lstrip=SCREAMING_SNAKE_CASE__   ,       rstrip=SCREAMING_SNAKE_CASE__    ) if isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ) else value
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   value



				def 	__magic_name__    (self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		BatchEncoding:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   kwargs.get("""is_split_into_words"""   ,       SCREAMING_SNAKE_CASE__    )
											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(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		BatchEncoding:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   kwargs.get("""is_split_into_words"""   ,       SCREAMING_SNAKE_CASE__    )

											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(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		Tuple[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self._tokenizer.model.save(SCREAMING_SNAKE_CASE__   ,       name=SCREAMING_SNAKE_CASE__    )
											return tuple(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=None    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		List[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [self.sep_token_id]
											SCREAMING_SNAKE_CASE__						:       str           =   [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]






"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.nn.Linear(10   ,       10    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.optim.SGD(model.parameters()   ,       0.1    )
											SCREAMING_SNAKE_CASE__						:       int           =   Accelerator()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   accelerator.prepare(SCREAMING_SNAKE_CASE__    )
											try:
																		pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE__    )    )
											except Exception as e:
																		self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
											AcceleratorState._reset_state()




