



'''simple docstring'''






import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase: List[str]							=						logging.get_logger(__name__)

class 							a__( __a     ):
    lowercase__			=   ["""input_ids""", """attention_mask"""]







    def __init__(				self			:  str						,      __snake_case			:  Any="</s>"						,      __snake_case			:  List[Any]="<unk>"						,      __snake_case			:  int="<pad>"						,      __snake_case			:  Optional[Any]=1_25						,      __snake_case			:  Any=None						,      **__snake_case			:  Dict						,      ):
      # Add extra_ids to the special token list
      if extra_ids > 0 and additional_special_tokens is None:
        a						: List[Any]					       =					[F"""<extra_id_{i}>""" for i in range(a__     )]
      elif extra_ids > 0 and additional_special_tokens is not None:
        # Check that we have the right number of extra_id special tokens
        a						: Optional[Any]					       =					len(set(filter(lambda __snake_case     : bool('extra_id' in str(a__     )     )						,      a__     )     )     )
        if extra_tokens != extra_ids:
          raise ValueError(
              F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
              ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
              ' extra_ids tokens'     )

      a						: Dict					       =					AddedToken(a__						,      lstrip=a__						,      rstrip=a__     ) if isinstance(a__						,      a__     ) else pad_token
      a						: Dict					       =					AddedToken(a__						,      lstrip=a__						,      rstrip=a__     ) if isinstance(a__						,      a__     ) else eos_token
      a						: Tuple					       =					AddedToken(a__						,      lstrip=a__						,      rstrip=a__     ) if isinstance(a__						,      a__     ) else unk_token

      super().__init__(
          eos_token=a__						,      unk_token=a__						,      pad_token=a__						,      extra_ids=a__						,      additional_special_tokens=a__						,      **a__						,      )

      a						: str					       =					extra_ids

      a						: Any					       =					2**8  # utf is 8 bits

      # define special tokens dict
      a						: Dict					       =					{
          self.pad_token: 0,
          self.eos_token: 1,
          self.unk_token: 2,
      }
      a						: List[Any]					       =					len(self.special_tokens_encoder     )
      a						: Tuple					       =					len(a__     )
      for i, token in enumerate(a__     ):
        a						: Union[str, Any]					       =					self.vocab_size + i - n
      a						: Optional[Any]					       =					{v: k for k, v in self.special_tokens_encoder.items()}







    @property
    def       lowercase_	(				self			:  Union[str, Any]     ):
      return self._utf_vocab_size + self._num_special_tokens + self._extra_ids







    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None						,      __snake_case			:  bool = False     ):
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=a__						,      token_ids_a=a__						,      already_has_special_tokens=a__     )

      # normal case: some special tokens
      if token_ids_a is None:
        return ([0] * len(a__     )) + [1]
      return ([0] * len(a__     )) + [1] + ([0] * len(a__     )) + [1]







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  List[int]     ):
      if len(a__     ) > 0 and token_ids[-1] == self.eos_token_id:
        warnings.warn(
            F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
            ' eos tokens being added.'     )
        return token_ids
      else:
        return token_ids + [self.eos_token_id]







    def       lowercase_	(				self			:  Any						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None     ):
      a						: Optional[int]					       =					[self.eos_token_id]

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







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None     ):
      a						: Dict					       =					self._add_eos_if_not_present(a__     )
      if token_ids_a is None:
        return token_ids_a
      else:
        a						: Dict					       =					self._add_eos_if_not_present(a__     )
        return token_ids_a + token_ids_a







    def       lowercase_	(				self			:  str						,      __snake_case			:  str     ):
      a						: List[str]					       =					[chr(a__     ) for i in text.encode('utf-8'     )]
      return tokens







    def       lowercase_	(				self			:  int						,      __snake_case			:  str     ):
      if token in self.special_tokens_encoder:
        a						: Optional[Any]					       =					self.special_tokens_encoder[token]
      elif token in self.added_tokens_encoder:
        a						: List[str]					       =					self.added_tokens_encoder[token]
      elif len(a__     ) != 1:
        a						: List[str]					       =					self.unk_token_id
      else:
        a						: Tuple					       =					ord(a__     ) + self._num_special_tokens
      return token_id







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[str]     ):
      if index in self.special_tokens_decoder:
        a						: List[Any]					       =					self.special_tokens_decoder[index]
      else:
        a						: List[Any]					       =					chr(index - self._num_special_tokens     )
      return token







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union[str, Any]     ):
      a						: Optional[Any]					       =					b''
      for token in tokens:
        if token in self.special_tokens_decoder:
          a						: Any					       =					self.special_tokens_decoder[token].encode('utf-8'     )
        elif token in self.added_tokens_decoder:
          a						: Any					       =					self.special_tokens_decoder[token].encode('utf-8'     )
        elif token in self.special_tokens_encoder:
          a						: Tuple					       =					token.encode('utf-8'     )
        elif token in self.added_tokens_encoder:
          a						: Dict					       =					token.encode('utf-8'     )
        else:
          a						: Tuple					       =					bytes([ord(a__     )]     )
        bstring += tok_string
      a						: int					       =					bstring.decode('utf-8'						,      errors='ignore'     )
      return string







    def       lowercase_	(				self			:  int						,      __snake_case			:  str						,      __snake_case			:  Optional[str] = None     ):
      return ()




'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  bool = True						,      __snake_case			:  int = 32						,      __snake_case			:  Union[str, Any]=PILImageResampling.BILINEAR						,      __snake_case			:  bool = True						,      **__snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					do_resize
      a						: Union[str, Any]					       =					do_rescale
      a						: Union[str, Any]					       =					size_divisor
      a						: List[Any]					       =					resample
      super().__init__(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Optional[int]					       =					get_image_size(__snake_case     )
      # Rounds the height and width down to the closest multiple of size_divisor
      a						: int					       =					height // size_divisor * size_divisor
      a						: int					       =					width // size_divisor * size_divisor
      a						: Any					       =					resize(__snake_case						,      (new_h, new_w)						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )
      return image







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  np.ndarray						,      __snake_case			:  float						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Optional[Any]     ):
      return rescale(image=__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[Union[TensorType, str]] = None						,      __snake_case			:  ChannelDimension = ChannelDimension.FIRST						,      **__snake_case			:  Any						,      ):
      a						: List[str]					       =					do_resize if do_resize is not None else self.do_resize
      a						: Tuple					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: Optional[Any]					       =					size_divisor if size_divisor is not None else self.size_divisor
      a						: Union[str, Any]					       =					resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
        raise ValueError('size_divisor is required for resizing'     )

      a						: Tuple					       =					make_list_of_images(__snake_case     )

      if not valid_images(__snake_case     ):
        raise ValueError('Invalid image(s)'     )

      # All transformations expect numpy arrays.
      a						: str					       =					[to_numpy_array(__snake_case     ) for img in images]

      if do_resize:
        a						: int					       =					[self.resize(__snake_case						,      size_divisor=__snake_case						,      resample=__snake_case     ) for image in images]

      if do_rescale:
        a						: List[str]					       =					[self.rescale(__snake_case						,      scale=1 / 2_55     ) for image in images]

      a						: Any					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

      a						: Any					       =					{'pixel_values': images}
      return BatchFeature(data=__snake_case						,      tensor_type=__snake_case     )