"""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,
    get_resize_output_image_size,
    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, logging


__UpperCamelCase									=			logging.get_logger(__name__)




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      ["pixel_values"]







       def __init__( self,							lowerCAmelCase__ = True,							lowerCAmelCase__ = None,							lowerCAmelCase__ = PILImageResampling.BILINEAR,							lowerCAmelCase__ = True,							lowerCAmelCase__ = None,							lowerCAmelCase__ = True,							lowerCAmelCase__ = 1 / 255,							lowerCAmelCase__ = True,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      None:
            super().__init__(**lowerCAmelCase__)
            snake_case_					       =					size if size is not None else {'shortest_edge': 256}
            snake_case_					       =					get_size_dict(lowerCAmelCase__,							default_to_square=lowerCAmelCase__)
            snake_case_					       =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
            snake_case_					       =					get_size_dict(lowerCAmelCase__)
            snake_case_					       =					do_resize
            snake_case_					       =					size
            snake_case_					       =					resample
            snake_case_					       =					do_center_crop
            snake_case_					       =					crop_size
            snake_case_					       =					do_rescale
            snake_case_					       =					rescale_factor
            snake_case_					       =					do_normalize
            snake_case_					       =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            snake_case_					       =					image_std if image_std is not None else IMAGENET_STANDARD_STD







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = PILImageResampling.BICUBIC,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      np.ndarray:
            snake_case_					       =					get_size_dict(lowerCAmelCase__,							default_to_square=lowerCAmelCase__)
            if "shortest_edge" not in size:
                 raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
            snake_case_					       =					get_resize_output_image_size(lowerCAmelCase__,							size=size['shortest_edge'],							default_to_square=lowerCAmelCase__)
            return resize(lowerCAmelCase__,							size=lowerCAmelCase__,							resample=lowerCAmelCase__,							data_format=lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      np.ndarray:
            snake_case_					       =					get_size_dict(lowerCAmelCase__)
            return center_crop(lowerCAmelCase__,							size=(size['height'], size['width']),							data_format=lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__)      ->      np.ndarray:
            return rescale(lowerCAmelCase__,							scale=lowerCAmelCase__,							data_format=lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      np.ndarray:
            return normalize(lowerCAmelCase__,							mean=lowerCAmelCase__,							std=lowerCAmelCase__,							data_format=lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = ChannelDimension.FIRST,							**lowerCAmelCase__,							)      ->      Any:
            snake_case_					       =					do_resize if do_resize is not None else self.do_resize
            snake_case_					       =					size if size is not None else self.size
            snake_case_					       =					get_size_dict(lowerCAmelCase__,							default_to_square=lowerCAmelCase__)
            snake_case_					       =					resample if resample is not None else self.resample
            snake_case_					       =					do_center_crop if do_center_crop is not None else self.do_center_crop
            snake_case_					       =					crop_size if crop_size is not None else self.crop_size
            snake_case_					       =					get_size_dict(lowerCAmelCase__)
            snake_case_					       =					do_rescale if do_rescale is not None else self.do_rescale
            snake_case_					       =					rescale_factor if rescale_factor is not None else self.rescale_factor
            snake_case_					       =					do_normalize if do_normalize is not None else self.do_normalize
            snake_case_					       =					image_mean if image_mean is not None else self.image_mean
            snake_case_					       =					image_std if image_std is not None else self.image_std

            snake_case_					       =					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:
                 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.')

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

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

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

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

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

            snake_case_					       =					[to_channel_dimension_format(lowerCAmelCase__,							lowerCAmelCase__) for image in images]

            snake_case_					       =					{'pixel_values': images}
            return BatchFeature(data=lowerCAmelCase__,							tensor_type=lowerCAmelCase__)






"""simple docstring"""






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

_lowercase : int      						=						{
    "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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'deberta-v2'



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  Optional[int]=12_8100,  lowerCamelCase							:  List[Any]=1536,  lowerCamelCase							:  Dict=24,  lowerCamelCase							:  Any=24,  lowerCamelCase							:  Union[str, Any]=6144,  lowerCamelCase							:  List[Any]="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  Optional[Any]=0,  lowerCamelCase							:  Any=0.02,  lowerCamelCase							:  int=1E-7,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Union[str, Any]=-1,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  int=None,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Tuple="gelu",  **lowerCamelCase							:  Optional[int],  )->					Union[str, Any]:
								super().__init__(**lowerCamelCase     )

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

								# Backwards compatibility
								if type(lowerCamelCase     ) == str:
											lowerCamelCase__		:		Union[str, Any]			=[x.strip() for x in pos_att_type.lower().split('''|'''     )]

								lowerCamelCase__		:		Tuple			=pos_att_type
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		Optional[int]			=layer_norm_eps

								lowerCamelCase__		:		Dict			=kwargs.get('''pooler_hidden_size''',  lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=pooler_dropout
								lowerCamelCase__		:		List[Any]			=pooler_hidden_act





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  List[str]     )->					Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
											lowerCamelCase__		:		Union[str, 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 		snake_case       (       self							:  List[str]     )->					int:
								return 12




					def 		snake_case       (       self							:  str,  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,  )->					Mapping[str, Any]:
								lowerCamelCase__		:		List[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




