
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__						:	Any	     = logging.get_logger(__name__)






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Any												=       ['''pixel_values''']


   def __init__(   self	:				List[Any]     ,     lowerCAmelCase_	:				bool = True     ,     lowerCAmelCase_	:				Dict[str, int] = None     ,     lowerCAmelCase_	:				PILImageResampling = PIL.Image.NEAREST     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Dict[str, int] = None     ,     lowerCAmelCase_	:				Union[int, float] = 1 / 2_5_5     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				bool = True     ,     lowerCAmelCase_	:				bool = True     ,     lowerCAmelCase_	:				Optional[Union[float, List[float]]] = None     ,     lowerCAmelCase_	:				Optional[Union[float, List[float]]] = None     ,     lowerCAmelCase_	:				bool = True     ,     **lowerCAmelCase_	:				Union[str, Any]     ,     ):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_	)
      _A:    List[Any]						     =			size if size is not None else {'''height''': 3_4_6, '''width''': 3_4_6}
      _A:    Optional[int]						     =			get_size_dict(lowerCAmelCase_	)
      _A:    Optional[Any]						     =			crop_size if crop_size is not None else {'''height''': 2_8_9, '''width''': 2_8_9}
      _A:    List[str]						     =			get_size_dict(lowerCAmelCase_     ,     param_name='''crop_size'''	)

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


   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				np.ndarray     ,     lowerCAmelCase_	:				Dict[str, int]     ,     lowerCAmelCase_	:				PILImageResampling = PIL.Image.NEAREST     ,     lowerCAmelCase_	:				Optional[Union[str, ChannelDimension]] = None     ,     **lowerCAmelCase_	:				List[Any]     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			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 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				np.ndarray     ,     lowerCAmelCase_	:				Dict[str, int]     ,     lowerCAmelCase_	:				Optional[Union[str, ChannelDimension]] = None     ,     **lowerCAmelCase_	:				str     ,     ):

      """simple docstring"""
      _A:    int						     =			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 					__magic_name__							(   self	:				List[Any]     ,     lowerCAmelCase_	:				np.ndarray     ,     lowerCAmelCase_	:				Union[int, float]     ,     lowerCAmelCase_	:				bool = True     ,     lowerCAmelCase_	:				Optional[Union[str, ChannelDimension]] = None     ,     **lowerCAmelCase_	:				Union[str, Any]     ,     ):

      """simple docstring"""
      if offset:
         _A:    str						     =			(image - 127.5) * scale
         if data_format is not None:
            _A:    Any						     =			to_channel_dimension_format(lowerCAmelCase_     ,     lowerCAmelCase_	)
         _A:    List[Any]						     =			rescaled_image.astype(np.floataa	)
      else:
         _A:    Optional[int]						     =			rescale(lowerCAmelCase_     ,     scale=lowerCAmelCase_     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_	)
      return rescaled_image


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				np.ndarray     ,     lowerCAmelCase_	:				Union[float, List[float]]     ,     lowerCAmelCase_	:				Union[float, List[float]]     ,     lowerCAmelCase_	:				Optional[Union[str, ChannelDimension]] = None     ,     **lowerCAmelCase_	:				List[str]     ,     ):

      """simple docstring"""
      return normalize(lowerCAmelCase_     ,     mean=lowerCAmelCase_     ,     std=lowerCAmelCase_     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				ImageInput     ,     lowerCAmelCase_	:				bool = None     ,     lowerCAmelCase_	:				Dict[str, int] = None     ,     lowerCAmelCase_	:				List[Any]=None     ,     lowerCAmelCase_	:				bool = None     ,     lowerCAmelCase_	:				Dict[str, int] = None     ,     lowerCAmelCase_	:				bool = None     ,     lowerCAmelCase_	:				float = None     ,     lowerCAmelCase_	:				bool = None     ,     lowerCAmelCase_	:				bool = None     ,     lowerCAmelCase_	:				Optional[Union[float, List[float]]] = None     ,     lowerCAmelCase_	:				Optional[Union[float, List[float]]] = None     ,     lowerCAmelCase_	:				bool = None     ,     lowerCAmelCase_	:				Optional[Union[str, TensorType]] = None     ,     lowerCAmelCase_	:				ChannelDimension = ChannelDimension.FIRST     ,     **lowerCAmelCase_	:				str     ,     ):

      """simple docstring"""
      _A:    Tuple						     =			do_resize if do_resize is not None else self.do_resize
      _A:    Tuple						     =			resample if resample is not None else self.resample
      _A:    Tuple						     =			do_center_crop if do_center_crop is not None else self.do_center_crop
      _A:    Tuple						     =			do_rescale if do_rescale is not None else self.do_rescale
      _A:    Optional[int]						     =			rescale_factor if rescale_factor is not None else self.rescale_factor
      _A:    Optional[Any]						     =			rescale_offset if rescale_offset is not None else self.rescale_offset
      _A:    List[str]						     =			do_normalize if do_normalize is not None else self.do_normalize
      _A:    List[str]						     =			image_mean if image_mean is not None else self.image_mean
      _A:    Union[str, Any]						     =			image_std if image_std is not None else self.image_std
      _A:    Tuple						     =			include_top if include_top is not None else self.include_top

      _A:    Optional[Any]						     =			size if size is not None else self.size
      _A:    Optional[int]						     =			get_size_dict(lowerCAmelCase_	)
      _A:    str						     =			crop_size if crop_size is not None else self.crop_size
      _A:    List[str]						     =			get_size_dict(lowerCAmelCase_     ,     param_name='''crop_size'''	)

      _A:    Optional[Any]						     =			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.
      _A:    Optional[Any]						     =			[to_numpy_array(lowerCAmelCase_	) for image in images]

      if do_resize:
         _A:    Optional[Any]						     =			[self.resize(image=lowerCAmelCase_     ,     size=lowerCAmelCase_     ,     resample=lowerCAmelCase_	) for image in images]

      if do_center_crop:
         _A:    str						     =			[self.center_crop(image=lowerCAmelCase_     ,     size=lowerCAmelCase_	) for image in images]

      if do_rescale:
         _A:    List[str]						     =			[self.rescale(image=lowerCAmelCase_     ,     scale=lowerCAmelCase_     ,     offset=lowerCAmelCase_	) for image in images]

      if do_normalize:
         _A:    int						     =			[self.normalize(image=lowerCAmelCase_     ,     mean=lowerCAmelCase_     ,     std=lowerCAmelCase_	) for image in images]

      if include_top:
         _A:    List[Any]						     =			[self.normalize(image=lowerCAmelCase_     ,     mean=[0, 0, 0]     ,     std=lowerCAmelCase_	) for image in images]

      _A:    Union[str, Any]						     =			[to_channel_dimension_format(lowerCAmelCase_     ,     lowerCAmelCase_	) for image in images]

      _A:    List[str]						     =			{'''pixel_values''': images}
      return BatchFeature(data=lowerCAmelCase_     ,     tensor_type=lowerCAmelCase_	)





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase__						:	str	     = logging.get_logger(__name__)

UpperCAmelCase__						:	int	     = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__						:	int	     = {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

UpperCAmelCase__						:	List[str]	     = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

UpperCAmelCase__						:	Optional[Any]	     = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Union[str, Any]												=       VOCAB_FILES_NAMES
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase						:       str												=       ['''input_ids''', '''attention_mask''', '''token_type_ids''']
   __UpperCamelCase						:       Any												=       DebertaTokenizer


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Optional[Any]=None     ,     lowerCAmelCase_	:				int=None     ,     lowerCAmelCase_	:				Dict=None     ,     lowerCAmelCase_	:				int="replace"     ,     lowerCAmelCase_	:				List[str]="[CLS]"     ,     lowerCAmelCase_	:				str="[SEP]"     ,     lowerCAmelCase_	:				int="[SEP]"     ,     lowerCAmelCase_	:				Any="[CLS]"     ,     lowerCAmelCase_	:				Dict="[UNK]"     ,     lowerCAmelCase_	:				Optional[int]="[PAD]"     ,     lowerCAmelCase_	:				List[str]="[MASK]"     ,     lowerCAmelCase_	:				Optional[Any]=False     ,     **lowerCAmelCase_	:				List[Any]     ,     ):

      """simple docstring"""
      super().__init__(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     tokenizer_file=lowerCAmelCase_     ,     errors=lowerCAmelCase_     ,     bos_token=lowerCAmelCase_     ,     eos_token=lowerCAmelCase_     ,     unk_token=lowerCAmelCase_     ,     sep_token=lowerCAmelCase_     ,     cls_token=lowerCAmelCase_     ,     pad_token=lowerCAmelCase_     ,     mask_token=lowerCAmelCase_     ,     add_prefix_space=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )
      _A:    List[str]						     =			kwargs.pop('''add_bos_token'''     ,     lowerCAmelCase_	)

      _A:    Optional[int]						     =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get('''add_prefix_space'''     ,     lowerCAmelCase_	) != add_prefix_space:
         _A:    Dict						     =			getattr(lowerCAmelCase_     ,     pre_tok_state.pop('''type'''	)	)
         _A:    List[Any]						     =			add_prefix_space
         _A:    Optional[int]						     =			pre_tok_class(**lowerCAmelCase_	)

      _A:    Optional[Any]						     =			add_prefix_space


   @property
   def 					__magic_name__							(   self	:				Optional[Any]	):

      """simple docstring"""
      if self._mask_token is None:
         if self.verbose:
            logger.error('''Using mask_token, but it is not set yet.'''	)
         return None
      return str(self._mask_token	)


   @mask_token.setter
   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Dict						     =			AddedToken(lowerCAmelCase_     ,     lstrip=lowerCAmelCase_     ,     rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else value
      _A:    str						     =			value


   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _A:    str						     =			[self.cls_token_id]
      _A:    Any						     =			[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      _A:    Dict						     =			[self.sep_token_id]
      _A:    Any						     =			[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 					__magic_name__							(   self	:				Tuple     ,     *lowerCAmelCase_	:				Dict     ,     **lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			kwargs.get('''is_split_into_words'''     ,     lowerCAmelCase_	)
      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				List[Any]     ,     *lowerCAmelCase_	:				Optional[Any]     ,     **lowerCAmelCase_	:				Any	):

      """simple docstring"""
      _A:    Optional[Any]						     =			kwargs.get('''is_split_into_words'''     ,     lowerCAmelCase_	)

      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Optional[str] = None	):

      """simple docstring"""
      _A:    Union[str, Any]						     =			self._tokenizer.model.save(lowerCAmelCase_     ,     name=lowerCAmelCase_	)
      return tuple(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				"Conversation"	):

      """simple docstring"""
      _A:    List[Any]						     =			[]
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_	) + [self.eos_token_id]	)

      if len(lowerCAmelCase_	) > self.model_max_length:
         _A:    Union[str, Any]						     =			input_ids[-self.model_max_length :]
      return input_ids



