"""simple docstring"""




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


lowerCamelCase_    :		Optional[int]							=	logging.get_logger(__name__)

lowerCamelCase_    :		Dict							=	{"""vocab_file""": """spiece.model"""}

lowerCamelCase_    :		List[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"""
        ),
    }
}

lowerCamelCase_    :		Optional[Any]							=	{
    """google/bigbird-roberta-base""": 4_096,
    """google/bigbird-roberta-large""": 4_096,
    """google/bigbird-base-trivia-itc""": 4_096,
}


class 						a__							(       __snake_case		):
			A__    :       Tuple        =      VOCAB_FILES_NAMES
			A__    :       int        =      PRETRAINED_VOCAB_FILES_MAP
			A__    :       Tuple        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__    :       Optional[Any]        =      ['input_ids', 'attention_mask']
			A__    :       List[int]        =      []



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase="<unk>"				,						UpperCAmelCase="<s>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="<pad>"				,						UpperCAmelCase="[SEP]"				,						UpperCAmelCase="[MASK]"				,						UpperCAmelCase="[CLS]"				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   None:
								__a		 =       AddedToken(UpperCAmelCase				,						lstrip=UpperCAmelCase				,						rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase				,						UpperCAmelCase   ) else bos_token
								__a		 =       AddedToken(UpperCAmelCase				,						lstrip=UpperCAmelCase				,						rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase				,						UpperCAmelCase   ) else eos_token
								__a		 =       AddedToken(UpperCAmelCase				,						lstrip=UpperCAmelCase				,						rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase				,						UpperCAmelCase   ) else unk_token
								__a		 =       AddedToken(UpperCAmelCase				,						lstrip=UpperCAmelCase				,						rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase				,						UpperCAmelCase   ) else pad_token
								__a		 =       AddedToken(UpperCAmelCase				,						lstrip=UpperCAmelCase				,						rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase				,						UpperCAmelCase   ) else cls_token
								__a		 =       AddedToken(UpperCAmelCase				,						lstrip=UpperCAmelCase				,						rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase				,						UpperCAmelCase   ) else sep_token

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

								__a		 =       {} if sp_model_kwargs is None else sp_model_kwargs

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

								__a		 =       vocab_file

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



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								return self.sp_model.get_piece_size()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       {self.convert_ids_to_tokens(UpperCAmelCase   ): 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				,						UpperCAmelCase   )			->   Dict:
								__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				,						UpperCAmelCase   )			->   List[str]:
								return self.sp_model.encode(UpperCAmelCase				,						out_type=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[Any]:
								return self.sp_model.piece_to_id(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:
								__a		 =       self.sp_model.IdToPiece(UpperCAmelCase   )
								return token



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[int]:
								__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(UpperCAmelCase   ) + token
																		__a		 =       True
																		__a		 =       []
													else:
																		current_sub_tokens.append(UpperCAmelCase   )
																		__a		 =       False
								out_string += self.sp_model.decode(UpperCAmelCase   )
								return out_string.strip()



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = False				,						UpperCAmelCase = None				,						UpperCAmelCase = True				,						**UpperCAmelCase				,						)			->   str:
								__a		 =       kwargs.pop('use_source_tokenizer'				,						UpperCAmelCase   )

								__a		 =       self.convert_ids_to_tokens(UpperCAmelCase				,						skip_special_tokens=UpperCAmelCase   )

								# 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(UpperCAmelCase   )   )
																							__a		 =       []
																		sub_texts.append(UpperCAmelCase   )
													else:
																		current_sub_text.append(UpperCAmelCase   )
								if current_sub_text:
													sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase   )   )

								# 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(UpperCAmelCase   )   )
								else:
													__a		 =       ''.join(UpperCAmelCase   )

								__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(UpperCAmelCase   )
													return clean_text
								else:
													return text



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[str]:
								if not os.path.isdir(UpperCAmelCase   ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
													return
								__a		 =       os.path.join(
								    UpperCAmelCase				,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

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

								return (out_vocab_file,)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   List[int]:
								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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = False   )			->   List[int]:
								if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=UpperCAmelCase				,						token_ids_a=UpperCAmelCase				,						already_has_special_tokens=UpperCAmelCase   )

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







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   List[int]:
								__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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :		Tuple							=	logging.get_logger(__name__)

lowerCamelCase_    :		List[Any]							=	{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}


class 						a__							(       __snake_case		):
			A__    :       Dict        =      'fnet'



			def __init__(						self				,						UpperCAmelCase=3_2_0_0_0				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu_new"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=4				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=False				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=3				,						UpperCAmelCase=1				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)			->   Dict:
								super().__init__(pad_token_id=UpperCAmelCase				,						bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       vocab_size
								__a		 =       max_position_embeddings
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       intermediate_size
								__a		 =       hidden_act
								__a		 =       hidden_dropout_prob
								__a		 =       initializer_range
								__a		 =       type_vocab_size
								__a		 =       layer_norm_eps
								__a		 =       use_tpu_fourier_optimizations
								__a		 =       tpu_short_seq_length




