



'''simple docstring'''
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

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_							):
						_UpperCamelCase					:		Optional[Any] 				=   to_pil_image(UpperCAmelCase_							)
						_UpperCamelCase				,			_UpperCamelCase					:		Dict 				=   pil_image.size
						_UpperCamelCase					:		Union[str, Any] 				=   pytesseract.image_to_data(UpperCAmelCase_  ,  lang=UpperCAmelCase_  ,  output_type='dict'  ,  config=UpperCAmelCase_							)
						_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Optional[int] 				=   data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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





	def __init__( self					:				int ,lowerCamelCase__					:				bool = True ,lowerCamelCase__					:				Dict[str, int] = None ,lowerCamelCase__					:				PILImageResampling = PILImageResampling.BILINEAR ,lowerCamelCase__					:				bool = True ,lowerCamelCase__					:				float = 1 / 255 ,lowerCamelCase__					:				bool = True ,lowerCamelCase__					:				Union[float, Iterable[float]] = None ,lowerCamelCase__					:				Union[float, Iterable[float]] = None ,lowerCamelCase__					:				bool = True ,lowerCamelCase__					:				Optional[str] = None ,lowerCamelCase__					:				Optional[str] = "" ,**lowerCamelCase__					:				Optional[int] ,):







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

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





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







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





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







							'''simple docstring'''
							return rescale(A_ ,scale=A_ ,data_format=A_ ,**A_ )





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ,lowerCamelCase__					:				np.ndarray ,lowerCamelCase__					:				Union[float, Iterable[float]] ,lowerCamelCase__					:				Union[float, Iterable[float]] ,lowerCamelCase__					:				Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__					:				int ,):







							'''simple docstring'''
							return normalize(A_ ,mean=A_ ,std=A_ ,data_format=A_ ,**A_ )





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







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

							_UpperCamelCase					:		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.' )

							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					:		Union[str, Any] 				=   [to_numpy_array(A_ ) for image in images]

							# Tesseract OCR to get words + normalized bounding boxes
							if apply_ocr:
													requires_backends(self ,'pytesseract' )
													_UpperCamelCase					:		Any 				=   []
													_UpperCamelCase					:		Union[str, Any] 				=   []
													for image in images:
																			_UpperCamelCase				,			_UpperCamelCase					:		int 				=   apply_tesseract(A_ ,A_ ,A_ )
																			words_batch.append(A_ )
																			boxes_batch.append(A_ )

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

							if do_rescale:
													_UpperCamelCase					:		Union[str, Any] 				=   [self.rescale(image=A_ ,scale=A_ ) for image in images]

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

							_UpperCamelCase					:		Union[str, Any] 				=   [to_channel_dimension_format(A_ ,A_ ) for image in images]

							_UpperCamelCase					:		Optional[Any] 				=   BatchFeature(data={'pixel_values': images} ,tensor_type=A_ )

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








from typing import TYPE_CHECKING

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


lowerCamelCase				:		str     =   {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NezhaForNextSentencePrediction",
     "NezhaForMaskedLM",
     "NezhaForPreTraining",
     "NezhaForMultipleChoice",
     "NezhaForQuestionAnswering",
     "NezhaForSequenceClassification",
     "NezhaForTokenClassification",
     "NezhaModel",
     "NezhaPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nezha import (
      NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
      NezhaForMaskedLM,
      NezhaForMultipleChoice,
      NezhaForNextSentencePrediction,
      NezhaForPreTraining,
      NezhaForQuestionAnswering,
      NezhaForSequenceClassification,
      NezhaForTokenClassification,
      NezhaModel,
      NezhaPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase				:		str     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



