




'''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

A_			      =		logging.get_logger(__name__)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Dict:
      return [
          int(1000 * (box[0] / width)		),
          int(1000 * (box[1] / height)		),
          int(1000 * (box[2] / width)		),
          int(1000 * (box[3] / height)		),
      ]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   str	      =    to_pil_image(A__		)
      snake_case__       :   Optional[Any]	      =    pil_image.size
      snake_case__       :   int	      =    pytesseract.image_to_data(A__				,      lang=A__				,      output_type='dict'				,      config=A__		)
      snake_case__       :   Tuple	      =    data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

      # filter empty words and corresponding coordinates
      snake_case__       :   List[Any]	      =    [idx for idx, word in enumerate(A__		) if not word.strip()]
      snake_case__       :   Tuple	      =    [word for idx, word in enumerate(A__		) if idx not in irrelevant_indices]
      snake_case__       :   List[str]	      =    [coord for idx, coord in enumerate(A__		) if idx not in irrelevant_indices]
      snake_case__       :   Dict	      =    [coord for idx, coord in enumerate(A__		) if idx not in irrelevant_indices]
      snake_case__       :   Union[str, Any]	      =    [coord for idx, coord in enumerate(A__		) if idx not in irrelevant_indices]
      snake_case__       :   Any	      =    [coord for idx, coord in enumerate(A__		) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      snake_case__       :   List[Any]	      =    []
      for x, y, w, h in zip(A__				,      A__				,      A__				,      A__		):
            snake_case__       :   Optional[int]	      =    [x, y, x + w, y + h]
            actual_boxes.append(A__		)

      # finally, normalize the bounding boxes
      snake_case__       :   Optional[int]	      =    []
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(A__				,      A__				,      A__		)		)

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

      return words, normalized_boxes






class        lowercase_      (						_SCREAMING_SNAKE_CASE							):
     A_											=			["pixel_values"]





     def __init__(     self     :					Tuple     ,							__lowerCamelCase     :					bool = True     ,							__lowerCamelCase     :					Dict[str, int] = None     ,							__lowerCamelCase     :					PILImageResampling = PILImageResampling.BILINEAR     ,							__lowerCamelCase     :					bool = True     ,							__lowerCamelCase     :					float = 1 / 255     ,							__lowerCamelCase     :					bool = True     ,							__lowerCamelCase     :					Union[float, Iterable[float]] = None     ,							__lowerCamelCase     :					Union[float, Iterable[float]] = None     ,							__lowerCamelCase     :					bool = True     ,							__lowerCamelCase     :					Optional[str] = None     ,							__lowerCamelCase     :					Optional[str] = ""     ,							**__lowerCamelCase     :					List[str]     ,							):
           super().__init__(**__lowerCamelCase			)
           snake_case__       :   int	      =    size if size is not None else {"""height""": 224, """width""": 224}
           snake_case__       :   int	      =    get_size_dict(__lowerCamelCase			)

           snake_case__       :   int	      =    do_resize
           snake_case__       :   Optional[Any]	      =    size
           snake_case__       :   str	      =    resample
           snake_case__       :   str	      =    do_rescale
           snake_case__       :   List[str]	      =    rescale_value
           snake_case__       :   Optional[Any]	      =    do_normalize
           snake_case__       :   List[Any]	      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           snake_case__       :   str	      =    image_std if image_std is not None else IMAGENET_STANDARD_STD
           snake_case__       :   Any	      =    apply_ocr
           snake_case__       :   Union[str, Any]	      =    ocr_lang
           snake_case__       :   List[str]	      =    tesseract_config





     def 			_lowerCAmelCase      (     self     :					Optional[int]     ,							__lowerCamelCase     :					np.ndarray     ,							__lowerCamelCase     :					Dict[str, int]     ,							__lowerCamelCase     :					PILImageResampling = PILImageResampling.BILINEAR     ,							__lowerCamelCase     :					Optional[Union[str, ChannelDimension]] = None     ,							**__lowerCamelCase     :					Union[str, Any]     ,							):
           snake_case__       :   Optional[Any]	      =    get_size_dict(__lowerCamelCase			)
           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()}"			)
           snake_case__       :   List[Any]	      =    (size["""height"""], size["""width"""])
           return resize(__lowerCamelCase     ,							size=__lowerCamelCase     ,							resample=__lowerCamelCase     ,							data_format=__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					np.ndarray     ,							__lowerCamelCase     :					Union[int, float]     ,							__lowerCamelCase     :					Optional[Union[str, ChannelDimension]] = None     ,							**__lowerCamelCase     :					str     ,							):
           return rescale(__lowerCamelCase     ,							scale=__lowerCamelCase     ,							data_format=__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					np.ndarray     ,							__lowerCamelCase     :					Union[float, Iterable[float]]     ,							__lowerCamelCase     :					Union[float, Iterable[float]]     ,							__lowerCamelCase     :					Optional[Union[str, ChannelDimension]] = None     ,							**__lowerCamelCase     :					int     ,							):
           return normalize(__lowerCamelCase     ,							mean=__lowerCamelCase     ,							std=__lowerCamelCase     ,							data_format=__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					ImageInput     ,							__lowerCamelCase     :					bool = None     ,							__lowerCamelCase     :					Dict[str, int] = None     ,							__lowerCamelCase     :					Dict=None     ,							__lowerCamelCase     :					bool = None     ,							__lowerCamelCase     :					float = None     ,							__lowerCamelCase     :					bool = None     ,							__lowerCamelCase     :					Union[float, Iterable[float]] = None     ,							__lowerCamelCase     :					Union[float, Iterable[float]] = None     ,							__lowerCamelCase     :					bool = None     ,							__lowerCamelCase     :					Optional[str] = None     ,							__lowerCamelCase     :					Optional[str] = None     ,							__lowerCamelCase     :					Optional[Union[str, TensorType]] = None     ,							__lowerCamelCase     :					ChannelDimension = ChannelDimension.FIRST     ,							**__lowerCamelCase     :					List[str]     ,							):
           snake_case__       :   Dict	      =    do_resize if do_resize is not None else self.do_resize
           snake_case__       :   Optional[int]	      =    size if size is not None else self.size
           snake_case__       :   Dict	      =    get_size_dict(__lowerCamelCase			)
           snake_case__       :   Optional[int]	      =    resample if resample is not None else self.resample
           snake_case__       :   Optional[Any]	      =    do_rescale if do_rescale is not None else self.do_rescale
           snake_case__       :   Tuple	      =    rescale_factor if rescale_factor is not None else self.rescale_factor
           snake_case__       :   str	      =    do_normalize if do_normalize is not None else self.do_normalize
           snake_case__       :   List[str]	      =    image_mean if image_mean is not None else self.image_mean
           snake_case__       :   Optional[Any]	      =    image_std if image_std is not None else self.image_std
           snake_case__       :   Optional[int]	      =    apply_ocr if apply_ocr is not None else self.apply_ocr
           snake_case__       :   Tuple	      =    ocr_lang if ocr_lang is not None else self.ocr_lang
           snake_case__       :   Dict	      =    tesseract_config if tesseract_config is not None else self.tesseract_config

           snake_case__       :   int	      =    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_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.
           snake_case__       :   str	      =    [to_numpy_array(__lowerCamelCase			) for image in images]

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

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

           if do_rescale:
                 snake_case__       :   Union[str, Any]	      =    [self.rescale(image=__lowerCamelCase     ,							scale=__lowerCamelCase			) for image in images]

           if do_normalize:
                 snake_case__       :   Union[str, Any]	      =    [self.normalize(image=__lowerCamelCase     ,							mean=__lowerCamelCase     ,							std=__lowerCamelCase			) for image in images]

           snake_case__       :   Dict	      =    [to_channel_dimension_format(__lowerCamelCase     ,							__lowerCamelCase			) for image in images]

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

           if apply_ocr:
                 snake_case__       :   Dict	      =    words_batch
                 snake_case__       :   Any	      =    boxes_batch
           return data




'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


__A   :    List[Any]											= logging.get_logger(__name__)

__A   :    List[Any]											= {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roberta-prelayernorm'

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Tuple=5_02_65       ,		lowerCamelCase       :    Optional[int]=7_68       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    int=30_72       ,		lowerCamelCase       :    Optional[int]="gelu"       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    Tuple=0.1       ,		lowerCamelCase       :    Optional[int]=5_12       ,		lowerCamelCase       :    Union[str, Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    Any=1E-12       ,		lowerCamelCase       :    int=1       ,		lowerCamelCase       :    List[Any]=0       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    Optional[Any]="absolute"       ,		lowerCamelCase       :    List[Any]=True       ,		lowerCamelCase       :    Tuple=None       ,		**lowerCamelCase       :    int       ,		)  ->    Optional[Any]:
      super().__init__(pad_token_id=lowerCamelCase       ,		bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  vocab_size
      lowerCAmelCase_     : str     =  hidden_size
      lowerCAmelCase_     : Dict     =  num_hidden_layers
      lowerCAmelCase_     : List[Any]     =  num_attention_heads
      lowerCAmelCase_     : List[str]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  intermediate_size
      lowerCAmelCase_     : List[str]     =  hidden_dropout_prob
      lowerCAmelCase_     : Tuple     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Optional[int]     =  max_position_embeddings
      lowerCAmelCase_     : List[Any]     =  type_vocab_size
      lowerCAmelCase_     : List[Any]     =  initializer_range
      lowerCAmelCase_     : Union[str, Any]     =  layer_norm_eps
      lowerCAmelCase_     : List[str]     =  position_embedding_type
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : Union[str, Any]     =  classifier_dropout







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    List[Any]						)  ->    Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
        lowerCAmelCase_     : Dict     =  {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        lowerCAmelCase_     : Optional[int]     =  {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]						)



