
'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


UpperCamelCase_   	=				logging.get_logger(__name__)


class      a_     (SCREAMING_SNAKE_CASE__			):
       __lowerCAmelCase			: Optional[int]			=					["pixel_values"]




       def __init__(   self      ,      snake_case_ = True      ,      snake_case_ = None      ,      snake_case_ = PIL.Image.BICUBIC      ,      snake_case_ = True      ,      snake_case_ = None      ,      snake_case_ = 1 / 2_5_5      ,      snake_case_ = True      ,      snake_case_ = True      ,      snake_case_ = None      ,      snake_case_ = None      ,      **snake_case_      ,      ):
         super().__init__(**lowerCamelCase__     )
         _lowerCAmelCase				:     List[str]       =    size if size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
         _lowerCAmelCase				:     Tuple       =    get_size_dict(lowerCamelCase__     )
         _lowerCAmelCase				:     Tuple       =    crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
         _lowerCAmelCase				:     Tuple       =    get_size_dict(lowerCamelCase__      ,      param_name="""crop_size"""     )

         _lowerCAmelCase				:     List[Any]       =    do_resize
         _lowerCAmelCase				:     Dict       =    size
         _lowerCAmelCase				:     Optional[Any]       =    resample
         _lowerCAmelCase				:     str       =    do_center_crop
         _lowerCAmelCase				:     Dict       =    crop_size
         _lowerCAmelCase				:     List[str]       =    do_rescale
         _lowerCAmelCase				:     List[Any]       =    rescale_factor
         _lowerCAmelCase				:     List[str]       =    do_normalize
         _lowerCAmelCase				:     Optional[int]       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _lowerCAmelCase				:     Tuple       =    image_std if image_std is not None else IMAGENET_STANDARD_STD




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_ = PIL.Image.BICUBIC      ,      snake_case_ = None      ,      **snake_case_      ,      ):
         _lowerCAmelCase				:     Any       =    get_size_dict(lowerCamelCase__     )
         if "height" not in size or "width" not in size:
           raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'     )
         return resize(
             lowerCamelCase__      ,      size=(size["""height"""], size["""width"""])      ,      resample=lowerCamelCase__      ,      data_format=lowerCamelCase__      ,      **lowerCamelCase__     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_ = None      ,      **snake_case_      ,      ):
         _lowerCAmelCase				:     Union[str, Any]       =    get_size_dict(lowerCamelCase__     )
         if "height" not in size or "width" not in size:
           raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'     )
         return center_crop(lowerCamelCase__      ,      size=(size["""height"""], size["""width"""])      ,      data_format=lowerCamelCase__      ,      **lowerCamelCase__     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_ = None      ,      **snake_case_      ,      ):
         return rescale(lowerCamelCase__      ,      scale=lowerCamelCase__      ,      data_format=lowerCamelCase__      ,      **lowerCamelCase__     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_ = None      ,      **snake_case_      ,      ):
         return normalize(lowerCamelCase__      ,      mean=lowerCamelCase__      ,      std=lowerCamelCase__      ,      data_format=lowerCamelCase__      ,      **lowerCamelCase__     )







       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_ = None      ,      snake_case_ = None      ,      snake_case_=None      ,      snake_case_ = None      ,      snake_case_ = None      ,      snake_case_ = None      ,      snake_case_ = None      ,      snake_case_ = None      ,      snake_case_ = None      ,      snake_case_ = None      ,      snake_case_ = None      ,      snake_case_ = ChannelDimension.FIRST      ,      **snake_case_      ,      ):
         _lowerCAmelCase				:     Optional[Any]       =    do_resize if do_resize is not None else self.do_resize
         _lowerCAmelCase				:     Dict       =    resample if resample is not None else self.resample
         _lowerCAmelCase				:     List[Any]       =    do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowerCAmelCase				:     int       =    do_rescale if do_rescale is not None else self.do_rescale
         _lowerCAmelCase				:     Tuple       =    rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowerCAmelCase				:     Dict       =    do_normalize if do_normalize is not None else self.do_normalize
         _lowerCAmelCase				:     Union[str, Any]       =    image_mean if image_mean is not None else self.image_mean
         _lowerCAmelCase				:     Optional[Any]       =    image_std if image_std is not None else self.image_std

         _lowerCAmelCase				:     Optional[int]       =    size if size is not None else self.size
         _lowerCAmelCase				:     Optional[int]       =    get_size_dict(lowerCamelCase__     )
         _lowerCAmelCase				:     int       =    crop_size if crop_size is not None else self.crop_size
         _lowerCAmelCase				:     Tuple       =    get_size_dict(lowerCamelCase__      ,      param_name="""crop_size"""     )

         _lowerCAmelCase				:     Tuple       =    make_list_of_images(lowerCamelCase__     )

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

         if do_resize and size is None or resample is None:
           raise ValueError("""Size and resample must be specified if do_resize is True."""     )

         if do_center_crop and crop_size is None:
           raise ValueError("""Crop size must be specified if do_center_crop is True."""     )

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

         if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("""Image mean and std must be specified if do_normalize is True."""     )

         # All transformations expect numpy arrays.
         _lowerCAmelCase				:     Optional[Any]       =    [to_numpy_array(lowerCamelCase__     ) for image in images]

         if do_resize:
           _lowerCAmelCase				:     List[str]       =    [self.resize(image=lowerCamelCase__      ,      size=lowerCamelCase__      ,      resample=lowerCamelCase__     ) for image in images]

         if do_center_crop:
           _lowerCAmelCase				:     Tuple       =    [self.center_crop(image=lowerCamelCase__      ,      size=lowerCamelCase__     ) for image in images]

         if do_rescale:
           _lowerCAmelCase				:     Optional[Any]       =    [self.rescale(image=lowerCamelCase__      ,      scale=lowerCamelCase__     ) for image in images]

         if do_normalize:
           _lowerCAmelCase				:     Tuple       =    [self.normalize(image=lowerCamelCase__      ,      mean=lowerCamelCase__      ,      std=lowerCamelCase__     ) for image in images]

         _lowerCAmelCase				:     Any       =    [to_channel_dimension_format(lowerCamelCase__      ,      lowerCamelCase__     ) for image in images]

         _lowerCAmelCase				:     int       =    {"""pixel_values""": images}
         return BatchFeature(data=lowerCamelCase__      ,      tensor_type=lowerCamelCase__     )







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


SCREAMING_SNAKE_CASE_:List[Any]             =						"""▁"""

SCREAMING_SNAKE_CASE_:int             =						{"""vocab_file""": """spiece.model"""}

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

SCREAMING_SNAKE_CASE_:Optional[Any]             =						{
    """google/pegasus-xsum""": 512,
}


SCREAMING_SNAKE_CASE_:Tuple             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      VOCAB_FILES_NAMES

    __lowerCamelCase			: List[Any]					=      VOCAB_FILES_NAMES
    __lowerCamelCase			: Optional[Any]					=      PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase			: str					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase			: str					=      ["input_ids", "attention_mask"]






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

            A :       Dict          =   (
                ([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(lowerCamelCase__		),		self.offset - 1		)
            ]

            if len(set(lowerCamelCase__		)		) != len(lowerCamelCase__		):
                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 :       int          =   additional_special_tokens_extended
        else:
            A :       Optional[int]          =   [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 :       Any          =   {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=lowerCamelCase__,		unk_token=lowerCamelCase__,		mask_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		mask_token_sent=lowerCamelCase__,		offset=lowerCamelCase__,		additional_special_tokens=lowerCamelCase__,		sp_model_kwargs=self.sp_model_kwargs,		**lowerCamelCase__,		)
        A :       Union[str, Any]          =   mask_token_sent
        A :       Optional[Any]          =   vocab_file
        A :       Optional[int]          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(lowerCamelCase__		)

        # add special tokens to encoder dict
        A :       Dict[int, str]          =   {
            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 :       Dict[str, int]          =   {v: k for k, v in self.encoder.items()}






    @property
    def 			_lowerCAmelCase					(       self		):
        return len(self.sp_model		) + self.offset






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   {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		):
        A :       List[Any]          =   self.__dict__.copy()
        A :       Union[str, Any]          =   None
        return state






    def __setstate__(       self,		lowerCamelCase__		):
        A :       List[Any]          =   d

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

        A :       Tuple          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(self.vocab_file		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return self.sp_model.encode(lowerCamelCase__,		out_type=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if token in self.decoder:
            return self.decoder[token]
        elif token in self.added_tokens_decoder:
            return self.added_tokens_decoder[token]
        A :       List[str]          =   self.sp_model.piece_to_id(lowerCamelCase__		)
        return sp_id + self.offset






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if index in self.encoder:
            return self.encoder[index]
        elif index in self.added_tokens_encoder:
            return self.added_tokens_encoder[index]
        else:
            A :       Dict          =   self.sp_model.IdToPiece(index - self.offset		)
        return token






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[int]          =   []
        A :       Optional[Any]          =   """"""
        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(lowerCamelCase__		) + token
                A :       int          =   []
            else:
                current_sub_tokens.append(lowerCamelCase__		)
        out_string += self.sp_model.decode(lowerCamelCase__		)
        return out_string.strip()






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__=False		):
        return 1






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[int]          =   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 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = False		):
        if already_has_special_tokens:
            return self._special_token_mask(lowerCamelCase__		)
        elif token_ids_a is None:
            return self._special_token_mask(lowerCamelCase__		) + [1]
        else:
            return self._special_token_mask(token_ids_a + token_ids_a		) + [1]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None		):
        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 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A :       Any          =   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:
                A :       Dict          =   self.sp_model.serialized_model_proto()
                fi.write(lowerCamelCase__		)

        return (out_vocab_file,)


