





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

import sentencepiece as spm

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


_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      {'vocab_file': 'sentencepiece.model'}

_lowerCamelCase 					=      {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

_lowerCamelCase 					=      {
    'google/rembert': 256,
}




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       VOCAB_FILES_NAMES
							lowerCamelCase_							     =       PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase_							     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


							def __init__(				self     :Optional[int]				,					__A     :Any				,					__A     :int=False				,					__A     :Any=True				,					__A     :List[Any]=True				,					__A     :int="[CLS]"				,					__A     :Optional[Any]="[SEP]"				,					__A     :Union[str, Any]="[UNK]"				,					__A     :List[Any]="[SEP]"				,					__A     :Union[str, Any]="[PAD]"				,					__A     :List[str]="[CLS]"				,					__A     :Dict="[MASK]"				,					**__A     :List[str]				,					) -> Tuple:



									"""simple docstring"""



									super().__init__(
									    do_lower_case=__A				,					remove_space=__A				,					keep_accents=__A				,					bos_token=__A				,					eos_token=__A				,					unk_token=__A				,					sep_token=__A				,					pad_token=__A				,					cls_token=__A				,					mask_token=__A				,					**__A				,					)

									SCREAMING_SNAKE_CASE__								=   do_lower_case
									SCREAMING_SNAKE_CASE__								=   remove_space
									SCREAMING_SNAKE_CASE__								=   keep_accents
									SCREAMING_SNAKE_CASE__								=   vocab_file

									SCREAMING_SNAKE_CASE__								=   spm.SentencePieceProcessor()
									self.sp_model.Load(__A		)


							@property
							def   _snake_case	(				self     :Optional[int]		) -> Optional[int]:



									"""simple docstring"""



									return len(self.sp_model		)


							def   _snake_case	(				self     :Dict		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   {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[Any]		) -> str:



									"""simple docstring"""



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


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   d
									SCREAMING_SNAKE_CASE__								=   spm.SentencePieceProcessor()
									self.sp_model.Load(self.vocab_file		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Dict				,					__A     :Optional[Any]=False		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.sp_model.EncodeAsPieces(__A		)
									return pieces


							def   _snake_case	(				self     :str				,					__A     :int		) -> int:



									"""simple docstring"""



									return self.sp_model.PieceToId(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :List[str]		) -> int:



									"""simple docstring"""



									return self.sp_model.IdToPiece(__A		)


							def   _snake_case	(				self     :Optional[int]				,					__A     :Optional[Any]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.sp_model.decode_pieces(__A		)
									return out_string


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



									"""simple docstring"""



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


							def   _snake_case	(				self     :List[Any]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None				,					__A     :bool = False		) -> List[int]:



									"""simple docstring"""



									if already_has_special_tokens:
											if token_ids_a is not None:
													raise ValueError(
													    """You should not supply a second sequence if the provided sequence of """
													    """ids is already formatted with special tokens for the model."""		)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

									if token_ids_a is not None:
											return [1] + ([0] * len(__A		)) + [1] + ([0] * len(__A		)) + [1]
									return [1] + ([0] * len(__A		)) + [1]


							def   _snake_case	(				self     :Optional[Any]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None		) -> List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [self.sep_token_id]
									SCREAMING_SNAKE_CASE__								=   [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]


							def   _snake_case	(				self     :Optional[Any]				,					__A     :str				,					__A     :Optional[str] = None		) -> Tuple[str]:



									"""simple docstring"""



									if not os.path.isdir(__A		):
											logger.error("""Vocabulary path ({}) should be a directory""".format(__A		)		)
											return
									SCREAMING_SNAKE_CASE__								=   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		):
											copyfile(self.vocab_file				,					__A		)

									return (out_vocab_file,)







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				'swin2sr'

						SCREAMING_SNAKE_CASE_:					Dict 					=				{
						    'hidden_size': 'embed_dim',
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_=6_4   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=3   ,				lowerCAmelCase_=1_8_0   ,				lowerCAmelCase_=[6, 6, 6, 6, 6, 6]   ,				lowerCAmelCase_=[6, 6, 6, 6, 6, 6]   ,				lowerCAmelCase_=8   ,				lowerCAmelCase_=2.0   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=0.0   ,				lowerCAmelCase_=0.0   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_="gelu"   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=0.02   ,				lowerCAmelCase_=1e-5   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=1.0   ,				lowerCAmelCase_="1conv"   ,				lowerCAmelCase_="pixelshuffle"   ,				**lowerCAmelCase_   ,				)		->							Dict:
										super().__init__(**lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    image_size
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    patch_size
										_SCREAMING_SNAKE_CASE   :		str       		=    num_channels
										_SCREAMING_SNAKE_CASE   :		Any       		=    embed_dim
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    depths
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    len(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_heads
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    window_size
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    mlp_ratio
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    qkv_bias
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    drop_path_rate
										_SCREAMING_SNAKE_CASE   :		int       		=    hidden_act
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    use_absolute_embeddings
										_SCREAMING_SNAKE_CASE   :		str       		=    layer_norm_eps
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    initializer_range
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    upscale
										_SCREAMING_SNAKE_CASE   :		str       		=    img_range
										_SCREAMING_SNAKE_CASE   :		Any       		=    resi_connection
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    upsampler

