






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

lowerCamelCase : Tuple						   =       logging.get_logger(__name__)
def       snake_case_		(     lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						str      ):
							return [
							    int(1000 * (box[0] / width)      ),
							    int(1000 * (box[1] / height)      ),
							    int(1000 * (box[2] / width)      ),
							    int(1000 * (box[3] / height)      ),
							]
def       snake_case_		(     lowerCAmelCase_       :						np.ndarray       ,       lowerCAmelCase_       :						Optional[str]       ,       lowerCAmelCase_       :						Optional[str] = None      ):
							__lowercase      :							Any				=     tesseract_config if tesseract_config is not None else """"""

							# apply OCR
							__lowercase      :							Optional[Any]				=     to_pil_image(lowerCAmelCase_      )
							__lowercase			,			__lowercase      :							Optional[Any]				=     pil_image.size
							__lowercase      :							Union[str, Any]				=     pytesseract.image_to_data(lowerCAmelCase_       ,       lang=lowerCAmelCase_       ,       output_type="""dict"""       ,       config=lowerCAmelCase_      )
							__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							Any				=     data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

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

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

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

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

							return words, normalized_boxes



class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			Any									=      ['''pixel_values''']



					def __init__(  self				:	Optional[int]		,     __a				:	bool = True		,     __a				:	Dict[str, int] = None		,     __a				:	PILImageResampling = PILImageResampling.BILINEAR		,     __a				:	bool = True		,     __a				:	Optional[str] = None		,     __a				:	Optional[str] = ""		,     **__a				:	str		,     )			->  None:


												"""simple docstring"""

												super().__init__(**__a							)
												__lowercase      :							Union[str, Any]				=     size if size is not None else {"""height""": 224, """width""": 224}
												__lowercase      :							Any				=     get_size_dict(__a							)

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



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	np.ndarray		,     __a				:	Dict[str, int]		,     __a				:	PILImageResampling = PILImageResampling.BILINEAR		,     __a				:	Optional[Union[str, ChannelDimension]] = None		,     **__a				:	Union[str, Any]		,     )			->  np.ndarray:


												"""simple docstring"""

												__lowercase      :							Any				=     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()}"							)
												__lowercase      :							Optional[Any]				=     (size["""height"""], size["""width"""])
												return resize(__a		,     size=__a		,     resample=__a		,     data_format=__a		,     **__a							)



					def       lowerCAmelCase				(  self				:	Dict		,     __a				:	ImageInput		,     __a				:	bool = None		,     __a				:	Dict[str, int] = None		,     __a				:	PILImageResampling = None		,     __a				:	bool = None		,     __a				:	Optional[str] = None		,     __a				:	Optional[str] = None		,     __a				:	Optional[Union[str, TensorType]] = None		,     __a				:	ChannelDimension = ChannelDimension.FIRST		,     **__a				:	Any		,     )			->  PIL.Image.Image:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     do_resize if do_resize is not None else self.do_resize
												__lowercase      :							Union[str, Any]				=     size if size is not None else self.size
												__lowercase      :							Tuple				=     get_size_dict(__a							)
												__lowercase      :							Tuple				=     resample if resample is not None else self.resample
												__lowercase      :							Union[str, Any]				=     apply_ocr if apply_ocr is not None else self.apply_ocr
												__lowercase      :							Optional[int]				=     ocr_lang if ocr_lang is not None else self.ocr_lang
												__lowercase      :							str				=     tesseract_config if tesseract_config is not None else self.tesseract_config

												__lowercase      :							Optional[Any]				=     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."""							)

												# All transformations expect numpy arrays.
												__lowercase      :							Dict				=     [to_numpy_array(__a							) for image in images]

												if apply_ocr:
																			requires_backends(self		,     """pytesseract"""							)
																			__lowercase      :							Union[str, Any]				=     []
																			__lowercase      :							Optional[int]				=     []
																			for image in images:
																										__lowercase			,			__lowercase      :							Optional[int]				=     apply_tesseract(__a		,     __a		,     __a							)
																										words_batch.append(__a							)
																										boxes_batch.append(__a							)

												if do_resize:
																			__lowercase      :							List[Any]				=     [self.resize(image=__a		,     size=__a		,     resample=__a							) for image in images]

												# flip color channels from RGB to BGR (as Detectron2 requires this)
												__lowercase      :							Any				=     [flip_channel_order(__a							) for image in images]
												__lowercase      :							Any				=     [to_channel_dimension_format(__a		,     __a							) for image in images]

												__lowercase      :							Any				=     BatchFeature(data={"""pixel_values""": images}		,     tensor_type=__a							)

												if apply_ocr:
																			__lowercase      :							Tuple				=     words_batch
																			__lowercase      :							int				=     boxes_batch
												return data







from torch import nn

class 		lowerCAmelCase     ( nn.Module			):

					'''simple docstring'''

					def __init__(  self				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__()
												__lowercase      :							int				=     class_size
												__lowercase      :							int				=     embed_size
												# self.mlp1 = nn.Linear(embed_size, embed_size)
												# self.mlp2 = (nn.Linear(embed_size, class_size))
												__lowercase      :							str				=     nn.Linear(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	int							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							str				=     self.mlp(__a							)
												return logits