'''simple docstring'''




from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

__UpperCAmelCase     							=logging.get_logger(__name__)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    Any:
    return [
        int(10_00 * (box[0] / width)				),
        int(10_00 * (box[1] / height)				),
        int(10_00 * (box[2] / width)				),
        int(10_00 * (box[3] / height)				),
    ]






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    Any:
    __lowerCamelCase		=  to_pil_image(UpperCamelCase__				)
    __lowerCamelCase	,  __lowerCamelCase		=  pil_image.size
    __lowerCamelCase		=  pytesseract.image_to_data(UpperCamelCase__				,  lang=UpperCamelCase__				,  output_type='''dict'''				,  config=UpperCamelCase__				)
    __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

    # filter empty words and corresponding coordinates
    __lowerCamelCase		=  [idx for idx, word in enumerate(UpperCamelCase__				) if not word.strip()]
    __lowerCamelCase		=  [word for idx, word in enumerate(UpperCamelCase__				) if idx not in irrelevant_indices]
    __lowerCamelCase		=  [coord for idx, coord in enumerate(UpperCamelCase__				) if idx not in irrelevant_indices]
    __lowerCamelCase		=  [coord for idx, coord in enumerate(UpperCamelCase__				) if idx not in irrelevant_indices]
    __lowerCamelCase		=  [coord for idx, coord in enumerate(UpperCamelCase__				) if idx not in irrelevant_indices]
    __lowerCamelCase		=  [coord for idx, coord in enumerate(UpperCamelCase__				) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    __lowerCamelCase		=  []
    for x, y, w, h in zip(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				):
        __lowerCamelCase		=  [x, y, x + w, y + h]
        actual_boxes.append(UpperCamelCase__				)

    # finally, normalize the bounding boxes
    __lowerCamelCase		=  []
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)				)

    assert len(UpperCamelCase__				) == len(UpperCamelCase__				), "Not as many words as there are bounding boxes"

    return words, normalized_boxes




class     a__      (   UpperCAmelCase__ ):
     lowerCamelCase  :     Union[str, Any]							=["pixel_values"]






     def __init__( self		:		List[Any]  ,				a		:		bool = True  ,				a		:		Dict[str, int] = None  ,				a		:		PILImageResampling = PILImageResampling.BILINEAR  ,				a		:		bool = True  ,				a		:		float = 1 / 2_55  ,				a		:		bool = True  ,				a		:		Union[float, Iterable[float]] = None  ,				a		:		Union[float, Iterable[float]] = None  ,				a		:		bool = True  ,				a		:		Optional[str] = None  ,				a		:		Optional[str] = ""  ,				**a		:		Any  ,				):





         """simple docstring"""



         super().__init__(**a    )
         __lowerCamelCase		=  size if size is not None else {'''height''': 2_24, '''width''': 2_24}
         __lowerCamelCase		=  get_size_dict(a    )

         __lowerCamelCase		=  do_resize
         __lowerCamelCase		=  size
         __lowerCamelCase		=  resample
         __lowerCamelCase		=  do_rescale
         __lowerCamelCase		=  rescale_value
         __lowerCamelCase		=  do_normalize
         __lowerCamelCase		=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __lowerCamelCase		=  image_std if image_std is not None else IMAGENET_STANDARD_STD
         __lowerCamelCase		=  apply_ocr
         __lowerCamelCase		=  ocr_lang
         __lowerCamelCase		=  tesseract_config






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]  ,				a		:		np.ndarray  ,				a		:		Dict[str, int]  ,				a		:		PILImageResampling = PILImageResampling.BILINEAR  ,				a		:		Optional[Union[str, ChannelDimension]] = None  ,				**a		:		Union[str, Any]  ,				):





         """simple docstring"""



         __lowerCamelCase		=  get_size_dict(a    )
         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()}"""    )
         __lowerCamelCase		=  (size['''height'''], size['''width'''])
         return resize(a  ,				size=a  ,				resample=a  ,				data_format=a  ,				**a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict  ,				a		:		np.ndarray  ,				a		:		Union[int, float]  ,				a		:		Optional[Union[str, ChannelDimension]] = None  ,				**a		:		Union[str, Any]  ,				):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		np.ndarray  ,				a		:		Union[float, Iterable[float]]  ,				a		:		Union[float, Iterable[float]]  ,				a		:		Optional[Union[str, ChannelDimension]] = None  ,				**a		:		int  ,				):





         """simple docstring"""



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






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





         """simple docstring"""



         __lowerCamelCase		=  do_resize if do_resize is not None else self.do_resize
         __lowerCamelCase		=  size if size is not None else self.size
         __lowerCamelCase		=  get_size_dict(a    )
         __lowerCamelCase		=  resample if resample is not None else self.resample
         __lowerCamelCase		=  do_rescale if do_rescale is not None else self.do_rescale
         __lowerCamelCase		=  rescale_factor if rescale_factor is not None else self.rescale_factor
         __lowerCamelCase		=  do_normalize if do_normalize is not None else self.do_normalize
         __lowerCamelCase		=  image_mean if image_mean is not None else self.image_mean
         __lowerCamelCase		=  image_std if image_std is not None else self.image_std
         __lowerCamelCase		=  apply_ocr if apply_ocr is not None else self.apply_ocr
         __lowerCamelCase		=  ocr_lang if ocr_lang is not None else self.ocr_lang
         __lowerCamelCase		=  tesseract_config if tesseract_config is not None else self.tesseract_config

         __lowerCamelCase		=  make_list_of_images(a    )

         if not valid_images(a    ):
             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_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('''If do_normalize is True, image_mean and image_std must be specified.'''    )

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

         # Tesseract OCR to get words + normalized bounding boxes
         if apply_ocr:
             requires_backends(self  ,				'''pytesseract'''    )
             __lowerCamelCase		=  []
             __lowerCamelCase		=  []
             for image in images:
                 __lowerCamelCase	,  __lowerCamelCase		=  apply_tesseract(a  ,				a  ,				a    )
                 words_batch.append(a    )
                 boxes_batch.append(a    )

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

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

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

         __lowerCamelCase		=  [to_channel_dimension_format(a  ,				a    ) for image in images]

         __lowerCamelCase		=  BatchFeature(data={'''pixel_values''': images}  ,				tensor_type=a    )

         if apply_ocr:
             __lowerCamelCase		=  words_batch
             __lowerCamelCase		=  boxes_batch
         return data





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


__A   			=logging.get_logger(__name__)

__A   			={
    "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__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       """deberta-v2"""




			def __init__(   self							: Optional[int]       ,       a_							: List[str]=12_81_00       ,       a_							: Optional[Any]=15_36       ,       a_							: Optional[Any]=24       ,       a_							: List[Any]=24       ,       a_							: Optional[int]=61_44       ,       a_							: List[Any]="gelu"       ,       a_							: Any=0.1       ,       a_							: Tuple=0.1       ,       a_							: Optional[Any]=5_12       ,       a_							: Tuple=0       ,       a_							: Dict=0.0_2       ,       a_							: Optional[Any]=1e-7       ,       a_							: List[str]=False       ,       a_							: List[Any]=-1       ,       a_							: List[str]=0       ,       a_							: Optional[Any]=True       ,       a_							: List[Any]=None       ,       a_							: Optional[int]=0       ,       a_							: Tuple="gelu"       ,       **a_							: List[str]       ,       ):
					'''simple docstring'''





					super().__init__(**a_ )

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

					# Backwards compatibility
					if type(a_ ) == str:
							__UpperCAmelCase    :     Optional[Any]													=	[x.strip() for x in pos_att_type.lower().split('''|''' )]

					__UpperCAmelCase    :     Tuple													=	pos_att_type
					__UpperCAmelCase    :     int													=	vocab_size
					__UpperCAmelCase    :     Optional[Any]													=	layer_norm_eps

					__UpperCAmelCase    :     str													=	kwargs.get('''pooler_hidden_size'''       ,       a_ )
					__UpperCAmelCase    :     Union[str, Any]													=	pooler_dropout
					__UpperCAmelCase    :     Tuple													=	pooler_hidden_act

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			@property
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					if self.task == "multiple-choice":
							__UpperCAmelCase    :     List[Any]													=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
							__UpperCAmelCase    :     int													=	{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							: Union[str, Any] ):
					'''simple docstring'''





					return 12




			def  snake_case__     (   self							: Any       ,       a_							: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,       a_							: int = -1       ,       a_							: int = -1       ,       a_							: int = -1       ,       a_							: bool = False       ,       a_							: Optional["TensorType"] = None       ,       a_							: int = 3       ,       a_							: int = 40       ,       a_							: int = 40       ,       a_							: "PreTrainedTokenizerBase" = None       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	super().generate_dummy_inputs(preprocessor=a_       ,       framework=a_ )
					if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
							del dummy_inputs["token_type_ids"]
					return dummy_inputs





