"""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 convert_to_rgb, 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


if is_vision_available():
     import PIL


__magic_name__     					=       logging.get_logger(__name__)

class SCREAMING_SNAKE_CASE  (     snake_case__						):







 """simple docstring"""


 a_							:						List[str]	      =['pixel_values']


 def __init__( self		:   List[Any]       , _snake_case		:   bool = True       , _snake_case		:   Dict[str, int] = None       , _snake_case		:   PILImageResampling = PILImageResampling.BICUBIC       , _snake_case		:   bool = True       , _snake_case		:   Union[int, float] = 1 / 255       , _snake_case		:   bool = True       , _snake_case		:   Optional[Union[float, List[float]]] = None       , _snake_case		:   Optional[Union[float, List[float]]] = None       , _snake_case		:   bool = True       , **_snake_case		:   Optional[Any]       , )					->      str:




    '''simple docstring'''






    super().__init__(**_snake_case					)
    a__  					=		size if size is not None else {'height': 384, 'width': 384}
    a__  					=		get_size_dict(_snake_case       , default_to_square=_snake_case					)

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


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




    '''simple docstring'''






    a__  					=		get_size_dict(_snake_case       , default_to_square=_snake_case					)
    if "height" not in size or "width" not in size:
       raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
    a__  					=		(size['height'], size['width'])
    return resize(_snake_case       , size=_snake_case       , resample=_snake_case       , data_format=_snake_case       , **_snake_case					)


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




    '''simple docstring'''






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


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




    '''simple docstring'''






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



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




    '''simple docstring'''






    a__  					=		do_resize if do_resize is not None else self.do_resize
    a__  					=		resample if resample is not None else self.resample
    a__  					=		do_rescale if do_rescale is not None else self.do_rescale
    a__  					=		rescale_factor if rescale_factor is not None else self.rescale_factor
    a__  					=		do_normalize if do_normalize is not None else self.do_normalize
    a__  					=		image_mean if image_mean is not None else self.image_mean
    a__  					=		image_std if image_std is not None else self.image_std
    a__  					=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    a__  					=		size if size is not None else self.size
    a__  					=		get_size_dict(_snake_case       , default_to_square=_snake_case					)

    a__  					=		make_list_of_images(_snake_case					)

    if not valid_images(_snake_case					):
       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_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:
       a__  					=		[convert_to_rgb(_snake_case					) for image in images]

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

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

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

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

    a__  					=		[to_channel_dimension_format(_snake_case       , _snake_case					) for image in images]

    a__  					=		BatchFeature(data={'pixel_values': images}       , tensor_type=_snake_case					)

    return encoded_outputs









from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase		:  Tuple			       =logging.get_logger(__name__)

_lowercase		:  Any			       ={
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						int						    =					'deberta-v2'



					def __init__(		self     :     Any       ,      lowerCamelCase     :     Dict=12_81_00       ,      lowerCamelCase     :     List[Any]=15_36       ,      lowerCamelCase     :     Union[str, Any]=24       ,      lowerCamelCase     :     Union[str, Any]=24       ,      lowerCamelCase     :     Optional[int]=61_44       ,      lowerCamelCase     :     Any="gelu"       ,      lowerCamelCase     :     int=0.1       ,      lowerCamelCase     :     str=0.1       ,      lowerCamelCase     :     Optional[int]=5_12       ,      lowerCamelCase     :     str=0       ,      lowerCamelCase     :     Union[str, Any]=0.02       ,      lowerCamelCase     :     List[str]=1E-7       ,      lowerCamelCase     :     Dict=False       ,      lowerCamelCase     :     List[Any]=-1       ,      lowerCamelCase     :     Union[str, Any]=0       ,      lowerCamelCase     :     Optional[int]=True       ,      lowerCamelCase     :     Optional[int]=None       ,      lowerCamelCase     :     Tuple=0       ,      lowerCamelCase     :     Tuple="gelu"       ,      **lowerCamelCase     :     Optional[int]       ,      ):
							super().__init__(**lowerCamelCase						)

							lowerCamelCase_	:     Any 						=      hidden_size
							lowerCamelCase_	:     Dict 						=      num_hidden_layers
							lowerCamelCase_	:     Optional[int] 						=      num_attention_heads
							lowerCamelCase_	:     Optional[int] 						=      intermediate_size
							lowerCamelCase_	:     str 						=      hidden_act
							lowerCamelCase_	:     int 						=      hidden_dropout_prob
							lowerCamelCase_	:     Tuple 						=      attention_probs_dropout_prob
							lowerCamelCase_	:     Union[str, Any] 						=      max_position_embeddings
							lowerCamelCase_	:     Union[str, Any] 						=      type_vocab_size
							lowerCamelCase_	:     List[Any] 						=      initializer_range
							lowerCamelCase_	:     Optional[Any] 						=      relative_attention
							lowerCamelCase_	:     List[Any] 						=      max_relative_positions
							lowerCamelCase_	:     Optional[int] 						=      pad_token_id
							lowerCamelCase_	:     int 						=      position_biased_input

							# Backwards compatibility
							if type(lowerCamelCase						) == str:
									lowerCamelCase_	:     Dict 						=      [x.strip() for x in pos_att_type.lower().split('|'						)]

							lowerCamelCase_	:     Optional[Any] 						=      pos_att_type
							lowerCamelCase_	:     List[Any] 						=      vocab_size
							lowerCamelCase_	:     Any 						=      layer_norm_eps

							lowerCamelCase_	:     int 						=      kwargs.get('pooler_hidden_size'       ,      lowerCamelCase						)
							lowerCamelCase_	:     Dict 						=      pooler_dropout
							lowerCamelCase_	:     Tuple 						=      pooler_hidden_act






class 						UpperCamelCase_  (  snake_case__							):



					@property
					def   __a     (		self     :     Optional[int]						):
							if self.task == "multiple-choice":
									lowerCamelCase_	:     Optional[Any] 						=      {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
									lowerCamelCase_	:     Any 						=      {0: 'batch', 1: 'sequence'}
							if self._config.type_vocab_size > 0:
									return OrderedDict(
									    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]						)
							else:
									return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]						)



					@property
					def   __a     (		self     :     List[Any]						):
							return 12





					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,      lowerCamelCase     :     int = -1       ,      lowerCamelCase     :     int = -1       ,      lowerCamelCase     :     int = -1       ,      lowerCamelCase     :     bool = False       ,      lowerCamelCase     :     Optional["TensorType"] = None       ,      lowerCamelCase     :     int = 3       ,      lowerCamelCase     :     int = 40       ,      lowerCamelCase     :     int = 40       ,      lowerCamelCase     :     "PreTrainedTokenizerBase" = None       ,      ):
							lowerCamelCase_	:     Union[str, Any] 						=      super().generate_dummy_inputs(preprocessor=lowerCamelCase       ,      framework=lowerCamelCase						)
							if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
									del dummy_inputs["token_type_ids"]
							return dummy_inputs
