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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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

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

SCREAMING_SNAKE_CASE							:str										=    {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

SCREAMING_SNAKE_CASE							:int										=    {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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






	def __init__(	self :			int							,A :			Tuple							,A :			Union[str, Any]="<unk>"							,A :			Any="<s>"							,A :			Dict="</s>"							,A :			List[Any]="<pad>"							,A :			Union[str, Any]="[SEP]"							,A :			Union[str, Any]="[MASK]"							,A :			Any="[CLS]"							,A :			Optional[Dict[str, Any]] = None							,**A :			str							,):
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else bos_token
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else eos_token
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else unk_token
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else pad_token
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else cls_token
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else sep_token

					# Mask token behave like a normal word, i.e. include the space before it
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else mask_token

					__A             =			{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,pad_token=A							,sep_token=A							,mask_token=A							,cls_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A             =			vocab_file

					__A             =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.Load(A			)






	@property
	def     UpperCamelCase_  (	self :			Optional[int]			):
					return self.sp_model.get_piece_size()






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			{self.convert_ids_to_tokens(A			): i for i in range(self.vocab_size			)}
					vocab.update(self.added_tokens_encoder			)
					return vocab






	def __getstate__(	self :			Tuple			):
					__A             =			self.__dict__.copy()
					__A             =			None
					return state






	def __setstate__(	self :			str							,A :			str			):
					__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     UpperCamelCase_  (	self :			str							,A :			str			):
					return self.sp_model.encode(A							,out_type=A			)






	def     UpperCamelCase_  (	self :			str							,A :			Tuple			):
					return self.sp_model.piece_to_id(A			)






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Optional[int]			):
					__A             =			self.sp_model.IdToPiece(A			)
					return token






	def     UpperCamelCase_  (	self :			Any							,A :			List[Any]			):
					__A             =			[]
					__A             =			""
					__A             =			False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(A			) + token
													__A             =			True
													__A             =			[]
									else:
													current_sub_tokens.append(A			)
													__A             =			False
					out_string += self.sp_model.decode(A			)
					return out_string.strip()






	def     UpperCamelCase_  (	self :			Tuple							,A :			List[int]							,A :			bool = False							,A :			bool = None							,A :			bool = True							,**A :			List[str]							,):
					__A             =			kwargs.pop("use_source_tokenizer"							,A			)

					__A             =			self.convert_ids_to_tokens(A							,skip_special_tokens=A			)

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					__A             =			[]
					__A             =			[]
					for token in filtered_tokens:
									if skip_special_tokens and token in self.all_special_ids:
													continue
									if token in self.added_tokens_encoder:
													if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(A			)			)
																	__A             =			[]
													sub_texts.append(A			)
									else:
													current_sub_text.append(A			)
					if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(A			)			)

					# Mimic the behavior of the Rust tokenizer:
					# No space before [MASK] and [SEP]
					if spaces_between_special_tokens:
									__A             =			re.sub(R" (\[(MASK|SEP)\])"							,R"\1"							," ".join(A			)			)
					else:
									__A             =			"".join(A			)

					__A             =			(
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
									__A             =			self.clean_up_tokenization(A			)
									return clean_text
					else:
									return text






	def     UpperCamelCase_  (	self :			Any							,A :			str							,A :			Optional[str] = None			):
					if not os.path.isdir(A			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					__A             =			os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

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

					return (out_vocab_file,)






	def     UpperCamelCase_  (	self :			List[str]							,A :			List[int]							,A :			Optional[List[int]] = None			):
					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__A             =			[self.cls_token_id]
					__A             =			[self.sep_token_id]
					return cls + token_ids_a + sep + token_ids_a + sep






	def     UpperCamelCase_  (	self :			Tuple							,A :			List[int]							,A :			Optional[List[int]] = None							,A :			bool = False			):
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A							,token_ids_a=A							,already_has_special_tokens=A			)

					if token_ids_a is None:
									return [1] + ([0] * len(A			)) + [1]
					return [1] + ([0] * len(A			)) + [1] + ([0] * len(A			)) + [1]







	def     UpperCamelCase_  (	self :			List[str]							,A :			List[int]							,A :			Optional[List[int]] = None			):
					__A             =			[self.sep_token_id]
					__A             =			[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			) * [0] + len(token_ids_a + sep			) * [1]


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							:Any										=    {
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]										=    [
					    'RagModel',
					    'RagPreTrainedModel',
					    'RagSequenceForGeneration',
					    'RagTokenForGeneration',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any										=    [
					    'TFRagModel',
					    'TFRagPreTrainedModel',
					    'TFRagSequenceForGeneration',
					    'TFRagTokenForGeneration',
					]


if TYPE_CHECKING:
					from .configuration_rag import RagConfig
					from .retrieval_rag import RagRetriever
					from .tokenization_rag import RagTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rag import (
										    TFRagModel,
										    TFRagPreTrainedModel,
										    TFRagSequenceForGeneration,
										    TFRagTokenForGeneration,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
