




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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase_  						=       logging.get_logger(__name__)


if is_vision_available():
    import PIL
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   Any     =  ["pixel_values"]




     def __init__(     self      :			int    ,_UpperCamelCase      :			bool = True    ,_UpperCamelCase      :			Dict[str, int] = None    ,_UpperCamelCase      :			PILImageResampling = PILImageResampling.BICUBIC    ,_UpperCamelCase      :			bool = True    ,_UpperCamelCase      :			Dict[str, int] = None    ,_UpperCamelCase      :			bool = True    ,_UpperCamelCase      :			Union[int, float] = 1 / 2_5_5    ,_UpperCamelCase      :			bool = True    ,_UpperCamelCase      :			Optional[Union[float, List[float]]] = None    ,_UpperCamelCase      :			Optional[Union[float, List[float]]] = None    ,_UpperCamelCase      :			bool = True    ,**_UpperCamelCase      :			List[Any]    ,)		->       None:


         '''simple docstring'''





         super().__init__(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =size if size is not None else {"""shortest_edge""": 2_2_4}
         SCREAMING_SNAKE_CASE__       =get_size_dict(_UpperCamelCase    ,default_to_square=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
         SCREAMING_SNAKE_CASE__       =get_size_dict(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =do_resize
         SCREAMING_SNAKE_CASE__       =size
         SCREAMING_SNAKE_CASE__       =resample
         SCREAMING_SNAKE_CASE__       =do_center_crop
         SCREAMING_SNAKE_CASE__       =crop_size
         SCREAMING_SNAKE_CASE__       =do_rescale
         SCREAMING_SNAKE_CASE__       =rescale_factor
         SCREAMING_SNAKE_CASE__       =do_normalize
         SCREAMING_SNAKE_CASE__       =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         SCREAMING_SNAKE_CASE__       =image_std if image_std is not None else OPENAI_CLIP_STD
         SCREAMING_SNAKE_CASE__       =do_convert_rgb




     def   __A    (     self      :			str    ,_UpperCamelCase      :			np.ndarray    ,_UpperCamelCase      :			Dict[str, int]    ,_UpperCamelCase      :			PILImageResampling = PILImageResampling.BICUBIC    ,_UpperCamelCase      :			Optional[Union[str, ChannelDimension]] = None    ,**_UpperCamelCase      :			Union[str, Any]    ,)		->       np.ndarray:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =get_size_dict(_UpperCamelCase    ,default_to_square=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =get_resize_output_image_size(
             _UpperCamelCase    ,size=(size["""height"""], size["""width"""])    ,default_to_square=_UpperCamelCase						)
         return resize(_UpperCamelCase    ,size=_UpperCamelCase    ,resample=_UpperCamelCase    ,data_format=_UpperCamelCase    ,**_UpperCamelCase						)




     def   __A    (     self      :			Dict    ,_UpperCamelCase      :			np.ndarray    ,_UpperCamelCase      :			Dict[str, int]    ,_UpperCamelCase      :			Optional[Union[str, ChannelDimension]] = None    ,**_UpperCamelCase      :			Union[str, Any]    ,)		->       np.ndarray:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =get_size_dict(_UpperCamelCase						)
         return center_crop(_UpperCamelCase    ,size=(size["""height"""], size["""width"""])    ,data_format=_UpperCamelCase    ,**_UpperCamelCase						)




     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			np.ndarray    ,_UpperCamelCase      :			Union[int, float]    ,_UpperCamelCase      :			Optional[Union[str, ChannelDimension]] = None    ,**_UpperCamelCase      :			Optional[int]    ,)		->       Any:


         '''simple docstring'''





         return rescale(_UpperCamelCase    ,scale=_UpperCamelCase    ,data_format=_UpperCamelCase    ,**_UpperCamelCase						)




     def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			np.ndarray    ,_UpperCamelCase      :			Union[float, List[float]]    ,_UpperCamelCase      :			Union[float, List[float]]    ,_UpperCamelCase      :			Optional[Union[str, ChannelDimension]] = None    ,**_UpperCamelCase      :			Tuple    ,)		->       np.ndarray:


         '''simple docstring'''





         return normalize(_UpperCamelCase    ,mean=_UpperCamelCase    ,std=_UpperCamelCase    ,data_format=_UpperCamelCase    ,**_UpperCamelCase						)




     def   __A    (     self      :			int    ,_UpperCamelCase      :			ImageInput    ,_UpperCamelCase      :			bool = None    ,_UpperCamelCase      :			Dict[str, int] = None    ,_UpperCamelCase      :			PILImageResampling = None    ,_UpperCamelCase      :			bool = None    ,_UpperCamelCase      :			int = None    ,_UpperCamelCase      :			bool = None    ,_UpperCamelCase      :			float = None    ,_UpperCamelCase      :			bool = None    ,_UpperCamelCase      :			Optional[Union[float, List[float]]] = None    ,_UpperCamelCase      :			Optional[Union[float, List[float]]] = None    ,_UpperCamelCase      :			bool = None    ,_UpperCamelCase      :			Optional[Union[str, TensorType]] = None    ,_UpperCamelCase      :			Optional[ChannelDimension] = ChannelDimension.FIRST    ,**_UpperCamelCase      :			Union[str, Any]    ,)		->       PIL.Image.Image:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =do_resize if do_resize is not None else self.do_resize
         SCREAMING_SNAKE_CASE__       =size if size is not None else self.size
         SCREAMING_SNAKE_CASE__       =get_size_dict(_UpperCamelCase    ,default_to_square=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =resample if resample is not None else self.resample
         SCREAMING_SNAKE_CASE__       =do_center_crop if do_center_crop is not None else self.do_center_crop
         SCREAMING_SNAKE_CASE__       =crop_size if crop_size is not None else self.crop_size
         SCREAMING_SNAKE_CASE__       =get_size_dict(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =do_rescale if do_rescale is not None else self.do_rescale
         SCREAMING_SNAKE_CASE__       =rescale_factor if rescale_factor is not None else self.rescale_factor
         SCREAMING_SNAKE_CASE__       =do_normalize if do_normalize is not None else self.do_normalize
         SCREAMING_SNAKE_CASE__       =image_mean if image_mean is not None else self.image_mean
         SCREAMING_SNAKE_CASE__       =image_std if image_std is not None else self.image_std
         SCREAMING_SNAKE_CASE__       =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         SCREAMING_SNAKE_CASE__       =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_resize and size is None:
             raise ValueError("""Size 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."""						)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
             SCREAMING_SNAKE_CASE__       =[convert_to_rgb(_UpperCamelCase						) for image in images]

         # All transformations expect numpy arrays.
         SCREAMING_SNAKE_CASE__       =[to_numpy_array(_UpperCamelCase						) for image in images]

         if do_resize:
             SCREAMING_SNAKE_CASE__       =[self.resize(image=_UpperCamelCase    ,size=_UpperCamelCase    ,resample=_UpperCamelCase						) for image in images]

         if do_center_crop:
             SCREAMING_SNAKE_CASE__       =[self.center_crop(image=_UpperCamelCase    ,size=_UpperCamelCase						) for image in images]

         if do_rescale:
             SCREAMING_SNAKE_CASE__       =[self.rescale(image=_UpperCamelCase    ,scale=_UpperCamelCase						) for image in images]

         if do_normalize:
             SCREAMING_SNAKE_CASE__       =[self.normalize(image=_UpperCamelCase    ,mean=_UpperCamelCase    ,std=_UpperCamelCase						) for image in images]

         SCREAMING_SNAKE_CASE__       =[to_channel_dimension_format(_UpperCamelCase    ,_UpperCamelCase						) for image in images]

         SCREAMING_SNAKE_CASE__       ={"""pixel_values""": images}
         return BatchFeature(data=_UpperCamelCase    ,tensor_type=_UpperCamelCase						)












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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
    from .tokenization_deberta_va import DebertaVaTokenizer
else:
    lowerCamelCase_  						=       None

lowerCamelCase_  						=       logging.get_logger(__name__)

lowerCamelCase_  						=       {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase_  						=       {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   str     =  VOCAB_FILES_NAMES
     _A    :   Union[str, Any]     =  PRETRAINED_VOCAB_FILES_MAP
     _A    :   Optional[int]     =  PRETRAINED_INIT_CONFIGURATION
     _A    :   Any     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A    :   Optional[int]     =  DebertaVaTokenizer




     def __init__(     self      :			List[Any]    ,_UpperCamelCase      :			Tuple=None    ,_UpperCamelCase      :			str=None    ,_UpperCamelCase      :			Any=False    ,_UpperCamelCase      :			List[Any]=False    ,_UpperCamelCase      :			Any="[CLS]"    ,_UpperCamelCase      :			Optional[int]="[SEP]"    ,_UpperCamelCase      :			int="[UNK]"    ,_UpperCamelCase      :			int="[SEP]"    ,_UpperCamelCase      :			Tuple="[PAD]"    ,_UpperCamelCase      :			Optional[Any]="[CLS]"    ,_UpperCamelCase      :			Optional[int]="[MASK]"    ,**_UpperCamelCase      :			int    ,)		->       None:


         '''simple docstring'''





         super().__init__(
             _UpperCamelCase    ,tokenizer_file=_UpperCamelCase    ,do_lower_case=_UpperCamelCase    ,bos_token=_UpperCamelCase    ,eos_token=_UpperCamelCase    ,unk_token=_UpperCamelCase    ,sep_token=_UpperCamelCase    ,pad_token=_UpperCamelCase    ,cls_token=_UpperCamelCase    ,mask_token=_UpperCamelCase    ,split_by_punct=_UpperCamelCase    ,**_UpperCamelCase    ,)

         SCREAMING_SNAKE_CASE__       =do_lower_case
         SCREAMING_SNAKE_CASE__       =split_by_punct
         SCREAMING_SNAKE_CASE__       =vocab_file
         SCREAMING_SNAKE_CASE__       =False if not self.vocab_file else True




     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			str=None						)		->       Dict:


         '''simple docstring'''





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




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			List[Any]=None    ,_UpperCamelCase      :			Union[str, Any]=False						)		->       str:


         '''simple docstring'''





         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCamelCase    ,token_ids_a=_UpperCamelCase    ,already_has_special_tokens=_UpperCamelCase						)

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




     def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			str=None						)		->       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   __A    (     self      :			int    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Optional[str] = None						)		->       Tuple[str]:


         '''simple docstring'''





         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer."""						)

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

         return (out_vocab_file,)






