





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case								=							{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							['ConditionalDetrFeatureExtractor']
      snake_case								=							['ConditionalDetrImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							[
          'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
          'ConditionalDetrForObjectDetection',
          'ConditionalDetrForSegmentation',
          'ConditionalDetrModel',
          'ConditionalDetrPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_conditional_detr import (
          CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
          ConditionalDetrConfig,
          ConditionalDetrOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
            from .image_processing_conditional_detr import ConditionalDetrImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_conditional_detr import (
                CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                ConditionalDetrForObjectDetection,
                ConditionalDetrForSegmentation,
                ConditionalDetrModel,
                ConditionalDetrPreTrainedModel,
            )

else:
      import sys

      snake_case								=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




'''simple docstring'''
from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

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






def A__       (	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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_ = None				):
						_UpperCamelCase   :						Optional[int]												=  tesseract_config if tesseract_config is not None else ''

						# apply OCR
						_UpperCamelCase   :						int												=  to_pil_image(UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  pil_image.size
						_UpperCamelCase   :						Tuple												=  pytesseract.image_to_data(UpperCAmelCase_    ,    lang=UpperCAmelCase_    ,    output_type='dict'    ,    config=UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						int												=  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   :						Any												=  [word for idx, word in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Any												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Union[str, Any]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Union[str, Any]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						List[str]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]

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

						# finally, normalize the bounding boxes
						_UpperCamelCase   :						List[Any]												=  []
						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 				lowercase__      (				lowercase  ):
	lowercase__    							=     ["""pixel_values"""]





	def __init__(					self   :							Dict					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[str] = ""					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  size if size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase   :						Union[str, Any]												=  get_size_dict(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  do_resize
							_UpperCamelCase   :						str												=  size
							_UpperCamelCase   :						Any												=  resample
							_UpperCamelCase   :						List[str]												=  apply_ocr
							_UpperCamelCase   :						int												=  ocr_lang
							_UpperCamelCase   :						Union[str, Any]												=  tesseract_config





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







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  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()}'					)
							_UpperCamelCase   :						List[str]												=  (size['height'], size['width'])
							return resize(lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							ImageInput					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = None					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[Union[str, TensorType]] = None					,lowerCamelCase__   :							ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase   :						Tuple												=  size if size is not None else self.size
							_UpperCamelCase   :						Optional[int]												=  get_size_dict(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  resample if resample is not None else self.resample
							_UpperCamelCase   :						Union[str, Any]												=  apply_ocr if apply_ocr is not None else self.apply_ocr
							_UpperCamelCase   :						Union[str, Any]												=  ocr_lang if ocr_lang is not None else self.ocr_lang
							_UpperCamelCase   :						int												=  tesseract_config if tesseract_config is not None else self.tesseract_config

							_UpperCamelCase   :						Dict												=  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.'					)

							# All transformations expect numpy arrays.
							_UpperCamelCase   :						Union[str, Any]												=  [to_numpy_array(lowerCamelCase__					) for image in images]

							if apply_ocr:
													requires_backends(self					,'pytesseract'					)
													_UpperCamelCase   :						Tuple												=  []
													_UpperCamelCase   :						Tuple												=  []
													for image in images:
																			_UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  apply_tesseract(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)
																			words_batch.append(lowerCamelCase__					)
																			boxes_batch.append(lowerCamelCase__					)

							if do_resize:
													_UpperCamelCase   :						Any												=  [self.resize(image=lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					) for image in images]

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							_UpperCamelCase   :						Optional[int]												=  [flip_channel_order(lowerCamelCase__					) for image in images]
							_UpperCamelCase   :						Dict												=  [to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						Union[str, Any]												=  BatchFeature(data={'pixel_values': images}					,tensor_type=lowerCamelCase__					)

							if apply_ocr:
													_UpperCamelCase   :						Dict												=  words_batch
													_UpperCamelCase   :						str												=  boxes_batch
							return data


