






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       :		List[Any]							=    logging.get_logger(__name__)
def   a_			(   _A   , _A   , _A	)      ->      Any:







				"""simple docstring"""
				return [
				    int(1000 * (box[0] / width)	),
				    int(1000 * (box[1] / height)	),
				    int(1000 * (box[2] / width)	),
				    int(1000 * (box[3] / height)	),
				]
def   a_			(   _A   , _A   , _A	)      ->      List[str]:







				"""simple docstring"""
				snake_case__						=  to_pil_image(_A	)
				snake_case__				,    snake_case__						=  pil_image.size
				snake_case__						=  pytesseract.image_to_data(_A   , lang=_A   , output_type='dict'   , config=_A	)
				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(_A	) if not word.strip()]
				snake_case__						=  [word for idx, word in enumerate(_A	) if idx not in irrelevant_indices]
				snake_case__						=  [coord for idx, coord in enumerate(_A	) if idx not in irrelevant_indices]
				snake_case__						=  [coord for idx, coord in enumerate(_A	) if idx not in irrelevant_indices]
				snake_case__						=  [coord for idx, coord in enumerate(_A	) if idx not in irrelevant_indices]
				snake_case__						=  [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__						=  []
				for x, y, w, h in zip(_A   , _A   , _A   , _A	):
								snake_case__						=  [x, y, x + w, y + h]
								actual_boxes.append(_A	)

				# finally, normalize the bounding boxes
				snake_case__						=  []
				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 	__SCREAMING_SNAKE_CASE(						__lowerCamelCase						):
							_UpperCAmelCase			     =							['pixel_values']
							def __init__(  self:   str							,						UpperCamelCase:   bool = True							,						UpperCamelCase:   Dict[str, int] = None							,						UpperCamelCase:   PILImageResampling = PILImageResampling.BILINEAR							,						UpperCamelCase:   bool = True							,						UpperCamelCase:   float = 1 / 2_55							,						UpperCamelCase:   bool = True							,						UpperCamelCase:   Union[float, Iterable[float]] = None							,						UpperCamelCase:   Union[float, Iterable[float]] = None							,						UpperCamelCase:   bool = True							,						UpperCamelCase:   Optional[str] = None							,						UpperCamelCase:   Optional[str] = ""							,						**UpperCamelCase:   Optional[int]							,						)		->		None:
											super().__init__(**UpperCamelCase    )
											snake_case__						=  size if size is not None else {'height': 2_24, 'width': 2_24}
											snake_case__						=  get_size_dict(UpperCamelCase    )

											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:   Optional[int]							,						UpperCamelCase:   np.ndarray							,						UpperCamelCase:   Dict[str, int]							,						UpperCamelCase:   PILImageResampling = PILImageResampling.BILINEAR							,						UpperCamelCase:   Optional[Union[str, ChannelDimension]] = None							,						**UpperCamelCase:   Union[str, Any]							,						)		->		np.ndarray:
											snake_case__						=  get_size_dict(UpperCamelCase    )
											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(UpperCamelCase							,						size=UpperCamelCase							,						resample=UpperCamelCase							,						data_format=UpperCamelCase							,						**UpperCamelCase    )
							def lowerCAmelCase_			(  self:   int							,						UpperCamelCase:   np.ndarray							,						UpperCamelCase:   Union[int, float]							,						UpperCamelCase:   Optional[Union[str, ChannelDimension]] = None							,						**UpperCamelCase:   Tuple							,						)		->		np.ndarray:
											return rescale(UpperCamelCase							,						scale=UpperCamelCase							,						data_format=UpperCamelCase							,						**UpperCamelCase    )
							def lowerCAmelCase_			(  self:   Union[str, Any]							,						UpperCamelCase:   np.ndarray							,						UpperCamelCase:   Union[float, Iterable[float]]							,						UpperCamelCase:   Union[float, Iterable[float]]							,						UpperCamelCase:   Optional[Union[str, ChannelDimension]] = None							,						**UpperCamelCase:   Dict							,						)		->		np.ndarray:
											return normalize(UpperCamelCase							,						mean=UpperCamelCase							,						std=UpperCamelCase							,						data_format=UpperCamelCase							,						**UpperCamelCase    )


							def lowerCAmelCase_			(  self:   Optional[int]							,						UpperCamelCase:   ImageInput							,						UpperCamelCase:   bool = None							,						UpperCamelCase:   Dict[str, int] = None							,						UpperCamelCase:   str=None							,						UpperCamelCase:   bool = None							,						UpperCamelCase:   float = None							,						UpperCamelCase:   bool = None							,						UpperCamelCase:   Union[float, Iterable[float]] = None							,						UpperCamelCase:   Union[float, Iterable[float]] = None							,						UpperCamelCase:   bool = None							,						UpperCamelCase:   Optional[str] = None							,						UpperCamelCase:   Optional[str] = None							,						UpperCamelCase:   Optional[Union[str, TensorType]] = None							,						UpperCamelCase:   ChannelDimension = ChannelDimension.FIRST							,						**UpperCamelCase:   Optional[int]							,						)		->		PIL.Image.Image:
											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(UpperCamelCase    )
											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(UpperCamelCase    )

											if not valid_images(UpperCamelCase    ):
															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(UpperCamelCase    ) 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(UpperCamelCase							,						UpperCamelCase							,						UpperCamelCase    )
																			words_batch.append(UpperCamelCase    )
																			boxes_batch.append(UpperCamelCase    )

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

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

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

											snake_case__						=  [to_channel_dimension_format(UpperCamelCase							,						UpperCamelCase    ) for image in images]

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

											if apply_ocr:
															snake_case__						=  words_batch
															snake_case__						=  boxes_batch
											return data






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	'biogpt'







	def __init__(     self      :Tuple   ,     lowercase      :Union[str, Any]=4_2_3_8_4   ,     lowercase      :str=1_0_2_4   ,     lowercase      :Any=2_4   ,     lowercase      :List[str]=1_6   ,     lowercase      :List[str]=4_0_9_6   ,     lowercase      :int="gelu"   ,     lowercase      :List[Any]=0.1   ,     lowercase      :int=0.1   ,     lowercase      :int=1_0_2_4   ,     lowercase      :Optional[Any]=0.02   ,     lowercase      :Union[str, Any]=1e-12   ,     lowercase      :Tuple=True   ,     lowercase      :int=True   ,     lowercase      :Optional[int]=0.0   ,     lowercase      :Any=0.0   ,     lowercase      :str=1   ,     lowercase      :Any=0   ,     lowercase      :int=2   ,     **lowercase      :Tuple   ,     )		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= vocab_size
				SCREAMING_SNAKE_CASE     						= max_position_embeddings
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= intermediate_size
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= hidden_dropout_prob
				SCREAMING_SNAKE_CASE     						= attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE     						= initializer_range
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= scale_embedding
				SCREAMING_SNAKE_CASE     						= use_cache
				SCREAMING_SNAKE_CASE     						= layerdrop
				SCREAMING_SNAKE_CASE     						= activation_dropout
				super().__init__(pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     **lowercase			)