

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

SCREAMING_SNAKE_CASE		:List[str]						=     {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """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		:Optional[Any]						=     {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}






class 						__magic_name__					(							lowercase_				):
     UpperCamelCase_	:Optional[Any]										=      VOCAB_FILES_NAMES
     UpperCamelCase_	:Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_	:Dict										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_	:Dict										=      ["""input_ids""", """attention_mask"""]
     UpperCamelCase_	:int										=      []






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

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

         UpperCamelCase_			  =						{} if sp_model_kwargs is None else sp_model_kwargs

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

         UpperCamelCase_			  =						vocab_file

         UpperCamelCase_			  =						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(lowerCamelCase_	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Dict:
         return self.sp_model.get_piece_size()






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_			  =						{self.convert_ids_to_tokens(lowerCamelCase_	): i for i in range(self.vocab_size	)}
         vocab.update(self.added_tokens_encoder	)
         return vocab






     def __getstate__(				self	)->     List[str]:
         UpperCamelCase_			  =						self.__dict__.copy()
         UpperCamelCase_			  =						None
         return state






     def __setstate__(				self		,						_lowercase	)->     Dict:
         UpperCamelCase_			  =						d

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

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






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         return self.sp_model.encode(lowerCamelCase_		,						out_type=lowerCamelCase_	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     int:
         return self.sp_model.piece_to_id(lowerCamelCase_	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						self.sp_model.IdToPiece(lowerCamelCase_	)
         return token






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Optional[Any]:
         UpperCamelCase_			  =						[]
         UpperCamelCase_			  =						""""""
         UpperCamelCase_			  =						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(lowerCamelCase_	) + token
                 UpperCamelCase_			  =						True
                 UpperCamelCase_			  =						[]
             else:
                 current_sub_tokens.append(lowerCamelCase_	)
                 UpperCamelCase_			  =						False
         out_string += self.sp_model.decode(lowerCamelCase_	)
         return out_string.strip()






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = False		,						_lowercase = None		,						_lowercase = True		,						**_lowercase		,						)->     str:
         UpperCamelCase_			  =						kwargs.pop("use_source_tokenizer"		,						lowerCamelCase_	)

         UpperCamelCase_			  =						self.convert_ids_to_tokens(lowerCamelCase_		,						skip_special_tokens=lowerCamelCase_	)

         # 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
         UpperCamelCase_			  =						[]
         UpperCamelCase_			  =						[]
         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(lowerCamelCase_	)	)
                     UpperCamelCase_			  =						[]
                 sub_texts.append(lowerCamelCase_	)
             else:
                 current_sub_text.append(lowerCamelCase_	)
         if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_	)	)

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

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






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     Union[str, Any]:



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

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

         return (out_vocab_file,)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     int:



         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCamelCase_			  =						[self.cls_token_id]
         UpperCamelCase_			  =						[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = False	)->     Optional[int]:



         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=lowerCamelCase_		,						token_ids_a=lowerCamelCase_		,						already_has_special_tokens=lowerCamelCase_	)

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






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     int:
         UpperCamelCase_			  =						[self.sep_token_id]
         UpperCamelCase_			  =						[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]







'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

					SCREAMING_SNAKE_CASE__				=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



