


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


_lowercase       :      int	=	"▁"

_lowercase       :      Optional[Any]	=	{"vocab_file": "spiece.model"}

_lowercase       :      Any	=	{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_lowercase       :      Dict	=	{
    "google/pegasus-xsum": 512,
}


_lowercase       :      Dict	=	logging.get_logger(__name__)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= VOCAB_FILES_NAMES

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

				def __init__(						self				,   a__				,   a__="<pad>"				,   a__="</s>"				,   a__="<unk>"				,   a__="<mask_2>"				,   a__="<mask_1>"				,   a__=None				,   a__=103				,   a__ = None				,   **a__				,   )	->				None:
					A								=				offset
					if additional_special_tokens is not None:
						if not isinstance(a__				,   a__   ):
							raise TypeError(
							    f'additional_special_tokens should be of type {type(a__   )}, but is'
							    f' {type(a__   )}'   )

						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(a__   )				,   self.offset - 1   )
						]

						if len(set(a__   )   ) != len(a__   ):
							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=a__				,   unk_token=a__				,   mask_token=a__				,   pad_token=a__				,   mask_token_sent=a__				,   offset=a__				,   additional_special_tokens=a__				,   sp_model_kwargs=self.sp_model_kwargs				,   **a__				,   )
					A								=				mask_token_sent
					A								=				vocab_file
					A								=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(a__   )

					# 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      _UpperCAmelCase					(						self   )	->				int:
					return len(self.sp_model   ) + self.offset

				def      _UpperCAmelCase					(						self   )	->				Dict[str, int]:
					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   )	->				Optional[int]:
					A								=				self.__dict__.copy()
					A								=				None
					return state

				def __setstate__(						self				,   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				,   a__   )	->				List[str]:
					return self.sp_model.encode(a__				,   out_type=a__   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				int:
					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(a__   )
					return sp_id + self.offset

				def      _UpperCAmelCase					(						self				,   a__   )	->				str:
					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      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					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(a__   ) + token
							A								=				[]
						else:
							current_sub_tokens.append(a__   )
					out_string += self.sp_model.decode(a__   )
					return out_string.strip()

				def      _UpperCAmelCase					(						self				,   a__=False   )	->				Optional[int]:
					return 1

				def      _UpperCAmelCase					(						self				,   a__   )	->				str:
					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      _UpperCAmelCase					(						self				,   a__				,   a__ = None				,   a__ = False   )	->				List[int]:
					if already_has_special_tokens:
						return self._special_token_mask(a__   )
					elif token_ids_a is None:
						return self._special_token_mask(a__   ) + [1]
					else:
						return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]

				def      _UpperCAmelCase					(						self				,   a__				,   a__=None   )	->				List[int]:
					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      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				Tuple[str]:
					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,)







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= ['image_processor', 'tokenizer']
				lowerCAmelCase 		= 'ViltImageProcessor'
				lowerCAmelCase 		= ('BertTokenizer', 'BertTokenizerFast')

				def __init__(						self				,   a__=None				,   a__=None				,   **a__   )	->				Any:
					A								=				None
					if "feature_extractor" in kwargs:
						warnings.warn(
						    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
						    """ instead."""				,   a__				,   )
						A								=				kwargs.pop("""feature_extractor"""   )

					A								=				image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
						raise ValueError("""You need to specify an `image_processor`."""   )
					if tokenizer is None:
						raise ValueError("""You need to specify a `tokenizer`."""   )

					super().__init__(a__				,   a__   )
					A								=				self.image_processor

				def __call__(						self				,   a__				,   a__ = None				,   a__ = True				,   a__ = False				,   a__ = None				,   a__ = None				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = True				,   a__ = None				,   **a__				,   )	->				BatchEncoding:
					A								=				self.tokenizer(
					    text=a__				,   add_special_tokens=a__				,   padding=a__				,   truncation=a__				,   max_length=a__				,   stride=a__				,   pad_to_multiple_of=a__				,   return_token_type_ids=a__				,   return_attention_mask=a__				,   return_overflowing_tokens=a__				,   return_special_tokens_mask=a__				,   return_offsets_mapping=a__				,   return_length=a__				,   verbose=a__				,   return_tensors=a__				,   **a__				,   )
					# add pixel_values + pixel_mask
					A								=				self.image_processor(a__				,   return_tensors=a__   )
					encoding.update(a__   )

					return encoding

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Union[str, Any]:
					return self.tokenizer.batch_decode(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Any:
					return self.tokenizer.decode(*a__				,   **a__   )

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.tokenizer.model_input_names
					A								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,   a__				,   )
					return self.image_processor_class

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,   a__				,   )
					return self.image_processor



