




"""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

_A													=      logging.get_logger(__name__)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Union[str, Any]:
      return [
          int(1000 * (box[0] / width)				),
          int(1000 * (box[1] / height)				),
          int(1000 * (box[2] / width)				),
          int(1000 * (box[3] / height)				),
      ]



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Any:
      lowerCAmelCase__      :			List[str]   =	to_pil_image(__UpperCAmelCase				)
      lowerCAmelCase__      :			Union[str, Any]   =	pil_image.size
      lowerCAmelCase__      :			Dict   =	pytesseract.image_to_data(__UpperCAmelCase    ,		lang=__UpperCAmelCase    ,		output_type="""dict"""    ,		config=__UpperCAmelCase				)
      lowerCAmelCase__      :			List[Any]   =	data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

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

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

      # finally, normalize the bounding boxes
      lowerCAmelCase__      :			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 _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Union[str, Any]     				=     ["pixel_values"]






   def __init__(		self       :		Optional[int]   ,     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       :		str   ,     )			->						None:

         """simple docstring"""
         super().__init__(**UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	size if size is not None else {"""height""": 2_24, """width""": 2_24}
         lowerCAmelCase__      :			Union[str, Any]   =	get_size_dict(UpperCamelCase   )

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






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

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	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()}"""   )
         lowerCAmelCase__      :			Optional[int]   =	(size["""height"""], size["""width"""])
         return resize(UpperCamelCase   ,     size=UpperCamelCase   ,     resample=UpperCamelCase   ,     data_format=UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		Union[int, float]   ,     UpperCamelCase       :		Optional[Union[str, ChannelDimension]] = None   ,     **UpperCamelCase       :		Tuple   ,     )			->						np.ndarray:

         """simple docstring"""
         return rescale(UpperCamelCase   ,     scale=UpperCamelCase   ,     data_format=UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		Union[float, Iterable[float]]   ,     UpperCamelCase       :		Union[float, Iterable[float]]   ,     UpperCamelCase       :		Optional[Union[str, ChannelDimension]] = None   ,     **UpperCamelCase       :		Optional[int]   ,     )			->						np.ndarray:

         """simple docstring"""
         return normalize(UpperCamelCase   ,     mean=UpperCamelCase   ,     std=UpperCamelCase   ,     data_format=UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		ImageInput   ,     UpperCamelCase       :		bool = None   ,     UpperCamelCase       :		Dict[str, int] = None   ,     UpperCamelCase       :		List[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       :		Any   ,     )			->						PIL.Image.Image:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__      :			Optional[int]   =	size if size is not None else self.size
         lowerCAmelCase__      :			Optional[Any]   =	get_size_dict(UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	resample if resample is not None else self.resample
         lowerCAmelCase__      :			Tuple   =	do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__      :			Optional[int]   =	rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__      :			int   =	do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__      :			Tuple   =	image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__      :			Tuple   =	image_std if image_std is not None else self.image_std
         lowerCAmelCase__      :			int   =	apply_ocr if apply_ocr is not None else self.apply_ocr
         lowerCAmelCase__      :			Any   =	ocr_lang if ocr_lang is not None else self.ocr_lang
         lowerCAmelCase__      :			Optional[Any]   =	tesseract_config if tesseract_config is not None else self.tesseract_config

         lowerCAmelCase__      :			str   =	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.
         lowerCAmelCase__      :			Any   =	[to_numpy_array(UpperCamelCase   ) for image in images]

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

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

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

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

         lowerCAmelCase__      :			Optional[int]   =	[to_channel_dimension_format(UpperCamelCase   ,     UpperCamelCase   ) for image in images]

         lowerCAmelCase__      :			int   =	BatchFeature(data={"""pixel_values""": images}   ,     tensor_type=UpperCamelCase   )

         if apply_ocr:
               lowerCAmelCase__      :			List[Any]   =	words_batch
               lowerCAmelCase__      :			List[str]   =	boxes_batch
         return data











"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Union[str, Any]   ,     *UpperCamelCase       :		int   ,     **UpperCamelCase       :		List[Any]   )			->						None:

         """simple docstring"""
         warnings.warn(
             """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use CLIPImageProcessor instead."""   ,     UpperCamelCase   ,     )
         super().__init__(*UpperCamelCase   ,     **UpperCamelCase   )





