
import os
from pathlib import Path


def 							SCREAMING_SNAKE_CASE_      (			):
      from torch.utils.cpp_extension import load

      UpperCamelCase__    :   Tuple					=				Path(UpperCamelCase__		).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
      UpperCamelCase__    :   Any					=				[
          root / filename
          for filename in [
              '''vision.cpp''',
              os.path.join('''cpu''' ,		'''ms_deform_attn_cpu.cpp'''		),
              os.path.join('''cuda''' ,		'''ms_deform_attn_cuda.cu'''		),
          ]
      ]

      load(
          '''MultiScaleDeformableAttention''' ,		UpperCamelCase__ ,		with_cuda=UpperCamelCase__ ,		extra_include_paths=[str(UpperCamelCase__		)] ,		extra_cflags=['''-DWITH_CUDA=1'''] ,		extra_cuda_cflags=[
              '''-DCUDA_HAS_FP16=1''',
              '''-D__CUDA_NO_HALF_OPERATORS__''',
              '''-D__CUDA_NO_HALF_CONVERSIONS__''',
              '''-D__CUDA_NO_HALF2_OPERATORS__''',
          ] ,		)

      import MultiScaleDeformableAttention as MSDA

      return MSDA




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

lowerCamelCase      =logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      return [
          int(1_0_0_0 * (box[0] / width)		),
          int(1_0_0_0 * (box[1] / height)		),
          int(1_0_0_0 * (box[2] / width)		),
          int(1_0_0_0 * (box[3] / height)		),
      ]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   List[Any]					=				to_pil_image(UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__    :   Optional[int]					=				pil_image.size
      UpperCamelCase__    :   str					=				pytesseract.image_to_data(UpperCamelCase__ ,		lang=UpperCamelCase__ ,		output_type='''dict''' ,		config=UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

      # filter empty words and corresponding coordinates
      UpperCamelCase__    :   Optional[Any]					=				[idx for idx, word in enumerate(UpperCamelCase__		) if not word.strip()]
      UpperCamelCase__    :   int					=				[word for idx, word in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   int					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   List[Any]					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   List[Any]					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   Dict					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]

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

      # finally, normalize the bounding boxes
      UpperCamelCase__    :   str					=				[]
      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 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ['''pixel_values''']






     def __init__(							self						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = 1 / 2_5_5						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ""						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__    :   Optional[int]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )

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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				get_size_dict(__SCREAMING_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()}'''  )
           UpperCamelCase__    :   int					=				(size['''height'''], size['''width'''])
           return resize(__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,						**__SCREAMING_SNAKE_CASE						,						)  ->   PIL.Image.Image:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__    :   List[str]					=				size if size is not None else self.size
           UpperCamelCase__    :   Dict					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				resample if resample is not None else self.resample
           UpperCamelCase__    :   int					=				do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__    :   List[str]					=				rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__    :   str					=				do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__    :   Union[str, Any]					=				image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__    :   Tuple					=				image_std if image_std is not None else self.image_std
           UpperCamelCase__    :   Optional[Any]					=				apply_ocr if apply_ocr is not None else self.apply_ocr
           UpperCamelCase__    :   List[str]					=				ocr_lang if ocr_lang is not None else self.ocr_lang
           UpperCamelCase__    :   List[Any]					=				tesseract_config if tesseract_config is not None else self.tesseract_config

           UpperCamelCase__    :   List[Any]					=				make_list_of_images(__SCREAMING_SNAKE_CASE  )

           if not valid_images(__SCREAMING_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:
                 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.
           UpperCamelCase__    :   List[Any]					=				[to_numpy_array(__SCREAMING_SNAKE_CASE  ) for image in images]

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

           if do_resize:
                 UpperCamelCase__    :   str					=				[self.resize(image=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_rescale:
                 UpperCamelCase__    :   List[Any]					=				[self.rescale(image=__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_normalize:
                 UpperCamelCase__    :   str					=				[self.normalize(image=__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   int					=				[to_channel_dimension_format(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   List[Any]					=				BatchFeature(data={'''pixel_values''': images}						,						tensor_type=__SCREAMING_SNAKE_CASE  )

           if apply_ocr:
                 UpperCamelCase__    :   str					=				words_batch
                 UpperCamelCase__    :   Union[str, Any]					=				boxes_batch
           return data


