




"""simple docstring"""





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


A				=							logging.get_logger(__name__)

A				=							{"""vocab_file""": """spiece.model"""}

A				=							{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

A				=							{"""bert_for_seq_generation""": 512}



class   a__					(						__magic_name__ ):
			lowercase_		=						VOCAB_FILES_NAMES
			lowercase_		=						PRETRAINED_VOCAB_FILES_MAP
			lowercase_		=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase_		=						[]
			lowercase_		=						["input_ids", "attention_mask"]


			def __init__(   self   :     List[str]   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     List[str]="<s>"   ,	UpperCamelCase_   :     Optional[Any]="</s>"   ,	UpperCamelCase_   :     Optional[int]="<unk>"   ,	UpperCamelCase_   :     int="<pad>"   ,	UpperCamelCase_   :     List[Any]="<::::>"   ,	UpperCamelCase_   :     Optional[Dict[str, Any]] = None   ,	**UpperCamelCase_   :     List[Any]   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       {} if sp_model_kwargs is None else sp_model_kwargs

						# Add extra_ids to the special token list
						super().__init__(
						    bos_token=UpperCamelCase_   ,	eos_token=UpperCamelCase_   ,	unk_token=UpperCamelCase_   ,	pad_token=UpperCamelCase_   ,	sep_token=UpperCamelCase_   ,	sp_model_kwargs=self.sp_model_kwargs   ,	**UpperCamelCase_   ,	)

						__UpperCAmelCase						:			Dict												=       vocab_file

						__UpperCAmelCase						:			Tuple												=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
						self.sp_model.Load(UpperCamelCase_)


			@property
			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						return self.sp_model.get_piece_size()


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       {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   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       self.__dict__.copy()
						__UpperCAmelCase						:			List[Any]												=       None
						return state


			def __setstate__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       d

						# for backward compatibility
						if not hasattr(self   ,	"sp_model_kwargs"):
									__UpperCAmelCase						:			List[Any]												=       {}

						__UpperCAmelCase						:			Optional[int]												=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
						self.sp_model.Load(self.vocab_file)


			def    a_  (   self   :     Any   ,	UpperCamelCase_   :     str):


						"""simple docstring"""




						return self.sp_model.encode(UpperCamelCase_   ,	out_type=UpperCamelCase_)


			def    a_  (   self   :     Optional[Any]   ,	UpperCamelCase_   :     Optional[int]):


						"""simple docstring"""




						return self.sp_model.piece_to_id(UpperCamelCase_)


			def    a_  (   self   :     Tuple   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       self.sp_model.IdToPiece(UpperCamelCase_)
						return token


			def    a_  (   self   :     Dict   ,	UpperCamelCase_   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       []
						__UpperCAmelCase						:			Tuple												=       ""
						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(UpperCamelCase_) + token
												__UpperCAmelCase						:			List[Any]												=       []
									else:
												current_sub_tokens.append(UpperCamelCase_)
						out_string += self.sp_model.decode(UpperCamelCase_)
						return out_string.strip()


			def    a_  (   self   :     List[str]   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Optional[str] = None):


						"""simple docstring"""




						if not os.path.isdir(UpperCamelCase_):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory")
									return
						__UpperCAmelCase						:			Tuple												=       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:
												__UpperCAmelCase						:			List[str]												=       self.sp_model.serialized_model_proto()
												fi.write(UpperCamelCase_)

						return (out_vocab_file,)





'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase_    						=       logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Union[int, float] = 1 / 255		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		int = 8		,					**UpperCAmelCase__     :		str		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=do_rescale
										lowercase :							List[Any]    		=rescale_factor
										lowercase :							Tuple    		=do_pad
										lowercase :							List[str]    		=pad_size







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		float		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Optional[int]   ):
										'''simple docstring'''
										return rescale(UpperCAmelCase__		,					scale=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Union[str, Any]    		=get_image_size(UpperCAmelCase__   )
										lowercase :							Tuple    		=(old_height // size + 1) * size - old_height
										lowercase :							Tuple    		=(old_width // size + 1) * size - old_width

										return pad(UpperCAmelCase__		,					((0, pad_height), (0, pad_width))		,					mode='''symmetric'''		,					data_format=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		ImageInput		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[float] = None		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		Optional[Union[str, TensorType]] = None		,					UpperCAmelCase__     :		Union[str, ChannelDimension] = ChannelDimension.FIRST		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										lowercase :							int    		=do_rescale if do_rescale is not None else self.do_rescale
										lowercase :							Tuple    		=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase :							int    		=do_pad if do_pad is not None else self.do_pad
										lowercase :							List[Any]    		=pad_size if pad_size is not None else self.pad_size

										lowercase :							Any    		=make_list_of_images(UpperCAmelCase__   )

										if not valid_images(UpperCAmelCase__   ):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

										if do_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

										# All transformations expect numpy arrays.
										lowercase :							Dict    		=[to_numpy_array(UpperCAmelCase__   ) for image in images]

										if do_rescale:
																lowercase :							Tuple    		=[self.rescale(image=UpperCAmelCase__		,					scale=UpperCAmelCase__   ) for image in images]

										if do_pad:
																lowercase :							Union[str, Any]    		=[self.pad(UpperCAmelCase__		,					size=UpperCAmelCase__   ) for image in images]

										lowercase :							Dict    		=[to_channel_dimension_format(UpperCAmelCase__		,					UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		={'''pixel_values''': images}
										return BatchFeature(data=UpperCAmelCase__		,					tensor_type=UpperCAmelCase__   )





