





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__			:       Dict       ,    UpperCamelCase__			:       int       ,    UpperCamelCase__			:       str						)			-> Optional[Any]:



					'''simple docstring'''




					return [
					    int(1_000 * (box[0] / width)						),
					    int(1_000 * (box[1] / height)						),
					    int(1_000 * (box[2] / width)						),
					    int(1_000 * (box[3] / height)						),
					]





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       Tuple						)			-> Optional[Any]:



					'''simple docstring'''




					_snake_case									=				to_pil_image(_lowerCAmelCase						)
					_snake_case							,     _snake_case									=				pil_image.size
					_snake_case									=				pytesseract.image_to_data(_lowerCAmelCase       ,    lang=_lowerCAmelCase       ,    output_type='dict'       ,    config=_lowerCAmelCase						)
					_snake_case							,     _snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				data['text'], data['left'], data['top'], data['width'], data['height']

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

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

					# finally, normalize the bounding boxes
					_snake_case									=				[]
					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 			UpperCamelCase_		(  __UpperCAmelCase					):
					lowerCAmelCase_							=	["pixel_values"]




					def __init__(		self				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = 1 / 255				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ""				,						**lowerCAmelCase_				,						)		->							Any:
										super().__init__(**_lowerCamelCase  )
										_snake_case									=				size if size is not None else {'height': 224, 'width': 224}
										_snake_case									=				get_size_dict(_lowerCamelCase  )

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




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							Dict:
										_snake_case									=				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									=				(size['height'], size['width'])
										return resize(_lowerCamelCase				,						size=_lowerCamelCase				,						resample=_lowerCamelCase				,						data_format=_lowerCamelCase				,						**_lowerCamelCase  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							Optional[int]:
										return rescale(_lowerCamelCase				,						scale=_lowerCamelCase				,						data_format=_lowerCamelCase				,						**_lowerCamelCase  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							List[str]:
										return normalize(_lowerCamelCase				,						mean=_lowerCamelCase				,						std=_lowerCamelCase				,						data_format=_lowerCamelCase				,						**_lowerCamelCase  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_=None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ChannelDimension.FIRST				,						**lowerCAmelCase_				,						)		->							str:
										_snake_case									=				do_resize if do_resize is not None else self.do_resize
										_snake_case									=				size if size is not None else self.size
										_snake_case									=				get_size_dict(_lowerCamelCase  )
										_snake_case									=				resample if resample is not None else self.resample
										_snake_case									=				do_rescale if do_rescale is not None else self.do_rescale
										_snake_case									=				rescale_factor if rescale_factor is not None else self.rescale_factor
										_snake_case									=				do_normalize if do_normalize is not None else self.do_normalize
										_snake_case									=				image_mean if image_mean is not None else self.image_mean
										_snake_case									=				image_std if image_std is not None else self.image_std
										_snake_case									=				apply_ocr if apply_ocr is not None else self.apply_ocr
										_snake_case									=				ocr_lang if ocr_lang is not None else self.ocr_lang
										_snake_case									=				tesseract_config if tesseract_config is not None else self.tesseract_config

										_snake_case									=				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									=				[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									=				[]
															_snake_case									=				[]
															for image in images:
																				_snake_case							,     _snake_case									=				apply_tesseract(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase  )
																				words_batch.append(_lowerCamelCase  )
																				boxes_batch.append(_lowerCamelCase  )

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

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

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

										_snake_case									=				[to_channel_dimension_format(_lowerCamelCase				,						_lowerCamelCase  ) for image in images]

										_snake_case									=				BatchFeature(data={'pixel_values': images}				,						tensor_type=_lowerCamelCase  )

										if apply_ocr:
															_snake_case									=				words_batch
															_snake_case									=				boxes_batch
										return data










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LukeForEntityClassification""",
					    """LukeForEntityPairClassification""",
					    """LukeForEntitySpanClassification""",
					    """LukeForMultipleChoice""",
					    """LukeForQuestionAnswering""",
					    """LukeForSequenceClassification""",
					    """LukeForTokenClassification""",
					    """LukeForMaskedLM""",
					    """LukeModel""",
					    """LukePreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
					from .tokenization_luke import LukeTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_luke import (
										    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LukeForEntityClassification,
										    LukeForEntityPairClassification,
										    LukeForEntitySpanClassification,
										    LukeForMaskedLM,
										    LukeForMultipleChoice,
										    LukeForQuestionAnswering,
										    LukeForSequenceClassification,
										    LukeForTokenClassification,
										    LukeModel,
										    LukePreTrainedModel,
										)

else:
					import sys

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



