
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A     :	List[str]    = '▁'

A     :	str    = {'vocab_file': 'spiece.model'}

A     :	Dict    = {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

A     :	Tuple    = {
    'google/pegasus-xsum': 5_1_2,
}


A     :	Tuple    = logging.get_logger(__name__)







class 			__A( a			):
				snake_case_						   =    VOCAB_FILES_NAMES

				snake_case_						   =    VOCAB_FILES_NAMES
				snake_case_						   =    PRETRAINED_VOCAB_FILES_MAP
				snake_case_						   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_						   =    ['''input_ids''', '''attention_mask''']




				def __init__(     self		,     _snake_case		,     _snake_case="<pad>"		,     _snake_case="</s>"		,     _snake_case="<unk>"		,     _snake_case="<mask_2>"		,     _snake_case="<mask_1>"		,     _snake_case=None		,     _snake_case=103		,     _snake_case = None		,     **_snake_case		,     )    ->      None:


								'''simple docstring'''




								__a										=    offset
								if additional_special_tokens is not None:
												if not isinstance(_snake_case		,     _snake_case				):
																raise TypeError(
																    F"""additional_special_tokens should be of type {type(_snake_case				)}, but is"""
																    F""" {type(_snake_case				)}"""				)

												__a										=    (
												    ([mask_token_sent] + additional_special_tokens)
												    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
												    else additional_special_tokens
												)
												# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
												additional_special_tokens_extended += [
												    F"""<unk_{i}>""" for i in range(len(_snake_case				)		,     self.offset - 1				)
												]

												if len(set(_snake_case				)				) != len(_snake_case				):
																raise ValueError(
																    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																    F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""				)
												__a										=    additional_special_tokens_extended
								else:
												__a										=    [mask_token_sent] if mask_token_sent is not None else []
												additional_special_tokens += [F"""<unk_{i}>""" for i in range(2		,     self.offset				)]

								__a										=    {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=_snake_case		,     unk_token=_snake_case		,     mask_token=_snake_case		,     pad_token=_snake_case		,     mask_token_sent=_snake_case		,     offset=_snake_case		,     additional_special_tokens=_snake_case		,     sp_model_kwargs=self.sp_model_kwargs		,     **_snake_case		,     )
								__a										=    mask_token_sent
								__a										=    vocab_file
								__a										=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(_snake_case				)

								# add special tokens to encoder dict
								__a										=    {
								    0: self.pad_token,
								    1: self.eos_token,
								}

								if self.mask_token_sent is not None:
												self.encoder.update(
												    {
												        2: self.mask_token_sent,
												        3: self.mask_token,
												    }				)

								if self.offset > 0:
												# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
												# mask_token_sent is already added to list -> so start at 1
												self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1		,     self.offset - 1				)}				)

								__a										=    {v: k for k, v in self.encoder.items()}




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return len(self.sp_model				) + self.offset




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict[str, int]:


								'''simple docstring'''




								__a										=    {self.convert_ids_to_tokens(_snake_case				): i for i in range(self.vocab_size				)}
								vocab.update(self.added_tokens_encoder				)
								return vocab




				def __getstate__(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.__dict__.copy()
								__a										=    None
								return state




				def __setstate__(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    d

								# for backward compatibility
								if not hasattr(self		,     '''sp_model_kwargs'''				):
												__a										=    {}

								__a										=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(self.vocab_file				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								return self.sp_model.encode(_snake_case		,     out_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								if token in self.decoder:
												return self.decoder[token]
								elif token in self.added_tokens_decoder:
												return self.added_tokens_decoder[token]
								__a										=    self.sp_model.piece_to_id(_snake_case				)
								return sp_id + self.offset




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								if index in self.encoder:
												return self.encoder[index]
								elif index in self.added_tokens_encoder:
												return self.added_tokens_encoder[index]
								else:
												__a										=    self.sp_model.IdToPiece(index - self.offset				)
								return token




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    []
								__a										=    ''''''
								for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																out_string += self.sp_model.decode(_snake_case				) + token
																__a										=    []
												else:
																current_sub_tokens.append(_snake_case				)
								out_string += self.sp_model.decode(_snake_case				)
								return out_string.strip()




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False				)    ->      Any:


								'''simple docstring'''




								return 1




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    set(self.all_special_ids				)  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id				)  # <unk> is only sometimes special

								return [1 if x in all_special_ids else 0 for x in seq]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = False				)    ->      List[int]:


								'''simple docstring'''




								if already_has_special_tokens:
												return self._special_token_mask(_snake_case				)
								elif token_ids_a is None:
												return self._special_token_mask(_snake_case				) + [1]
								else:
												return self._special_token_mask(token_ids_a + token_ids_a				) + [1]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=None				)    ->      List[int]:


								'''simple docstring'''




								if token_ids_a is None:
												return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Tuple[str]:


								'''simple docstring'''




								if not os.path.isdir(_snake_case				):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
												return
								__a										=    os.path.join(
								    _snake_case		,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

								if os.path.abspath(self.vocab_file				) != os.path.abspath(_snake_case				) and os.path.isfile(self.vocab_file				):
												copyfile(self.vocab_file		,     _snake_case				)
								elif not os.path.isfile(self.vocab_file				):
												with open(_snake_case		,     '''wb'''				) as fi:
																__a										=    self.sp_model.serialized_model_proto()
																fi.write(_snake_case				)

								return (out_vocab_file,)






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_ =			logging.get_logger(__name__)

a_ =			{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """roberta"""

 def __init__(    self					,						__lowerCamelCase=5_0265					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=1					,						__lowerCamelCase=0					,						__lowerCamelCase=2					,						__lowerCamelCase="absolute"					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						bos_token_id=__lowerCamelCase					,						eos_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						str						 =     vocab_size
     __A    :						Any						 =     hidden_size
     __A    :						Any						 =     num_hidden_layers
     __A    :						int						 =     num_attention_heads
     __A    :						List[Any]						 =     hidden_act
     __A    :						Any						 =     intermediate_size
     __A    :						List[str]						 =     hidden_dropout_prob
     __A    :						List[Any]						 =     attention_probs_dropout_prob
     __A    :						Optional[Any]						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						int						 =     initializer_range
     __A    :						Tuple						 =     layer_norm_eps
     __A    :						str						 =     position_embedding_type
     __A    :						int						 =     use_cache
     __A    :						Optional[int]						 =     classifier_dropout






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.task == "multiple-choice":
         __A    :						List[Any]						 =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         __A    :						Optional[Any]						 =     {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]  )




