

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
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, logging


__lowercase				      =						logging.get_logger(__name__)

class    _lowercase				(					snake_case__			):
   _lowercase     :       List[str]  					=    ['pixel_values']

   def __init__(				self	:    Optional[int]			,						lowerCamelCase__	:    Union[str, Any] = True			,						lowerCamelCase__	:    Union[str, Any] = None			,						lowerCamelCase__	:    List[str] = PILImageResampling.BILINEAR			,						lowerCamelCase__	:    List[Any] = True			,						lowerCamelCase__	:    Optional[Any] = None			,						lowerCamelCase__	:    List[str] = True			,						lowerCamelCase__	:    Union[str, Any] = 1 / 2_5_5			,						lowerCamelCase__	:    Optional[Any] = True			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    Optional[int] = None			,						**lowerCamelCase__	:    Tuple			,						) ->			None:
         """simple docstring"""





         super().__init__(**_UpperCamelCase       )
         A_							       =		size if size is not None else {'''shortest_edge''': 2_5_6}
         A_							       =		get_size_dict(_UpperCamelCase			,						default_to_square=_UpperCamelCase       )
         A_							       =		crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         A_							       =		get_size_dict(_UpperCamelCase       )
         A_							       =		do_resize
         A_							       =		size
         A_							       =		resample
         A_							       =		do_center_crop
         A_							       =		crop_size
         A_							       =		do_rescale
         A_							       =		rescale_factor
         A_							       =		do_normalize
         A_							       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A_							       =		image_std if image_std is not None else IMAGENET_STANDARD_STD

   def     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    int = PILImageResampling.BICUBIC			,						lowerCamelCase__	:    Optional[Any] = None			,						**lowerCamelCase__	:    int			,						) ->			np.ndarray:
         """simple docstring"""





         A_							       =		get_size_dict(_UpperCamelCase			,						default_to_square=_UpperCamelCase       )
         if "shortest_edge" not in size:
               raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"       )
         A_							       =		get_resize_output_image_size(_UpperCamelCase			,						size=size['''shortest_edge''']			,						default_to_square=_UpperCamelCase       )
         return resize(_UpperCamelCase			,						size=_UpperCamelCase			,						resample=_UpperCamelCase			,						data_format=_UpperCamelCase			,						**_UpperCamelCase       )

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    Any = None			,						**lowerCamelCase__	:    Optional[int]			,						) ->			np.ndarray:
         """simple docstring"""





         A_							       =		get_size_dict(_UpperCamelCase       )
         return center_crop(_UpperCamelCase			,						size=(size['''height'''], size['''width'''])			,						data_format=_UpperCamelCase			,						**_UpperCamelCase       )

   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    int = None			,						**lowerCamelCase__	:    str       ) ->			np.ndarray:
         """simple docstring"""





         return rescale(_UpperCamelCase			,						scale=_UpperCamelCase			,						data_format=_UpperCamelCase			,						**_UpperCamelCase       )

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Optional[Any] = None			,						**lowerCamelCase__	:    List[str]			,						) ->			np.ndarray:
         """simple docstring"""





         return normalize(_UpperCamelCase			,						mean=_UpperCamelCase			,						std=_UpperCamelCase			,						data_format=_UpperCamelCase			,						**_UpperCamelCase       )





   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    Dict = None			,						lowerCamelCase__	:    List[str] = None			,						lowerCamelCase__	:    int = None			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    Optional[Any] = None			,						lowerCamelCase__	:    Optional[Any] = None			,						lowerCamelCase__	:    Any = None			,						lowerCamelCase__	:    str = None			,						lowerCamelCase__	:    List[Any] = None			,						lowerCamelCase__	:    int = None			,						lowerCamelCase__	:    int = ChannelDimension.FIRST			,						**lowerCamelCase__	:    List[Any]			,						) ->			List[Any]:
         """simple docstring"""





         A_							       =		do_resize if do_resize is not None else self.do_resize
         A_							       =		size if size is not None else self.size
         A_							       =		get_size_dict(_UpperCamelCase			,						default_to_square=_UpperCamelCase       )
         A_							       =		resample if resample is not None else self.resample
         A_							       =		do_center_crop if do_center_crop is not None else self.do_center_crop
         A_							       =		crop_size if crop_size is not None else self.crop_size
         A_							       =		get_size_dict(_UpperCamelCase       )
         A_							       =		do_rescale if do_rescale is not None else self.do_rescale
         A_							       =		rescale_factor if rescale_factor is not None else self.rescale_factor
         A_							       =		do_normalize if do_normalize is not None else self.do_normalize
         A_							       =		image_mean if image_mean is not None else self.image_mean
         A_							       =		image_std if image_std is not None else self.image_std

         A_							       =		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_center_crop and crop_size is None:
               raise ValueError('''Crop size must be specified if do_center_crop 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('''Image mean and std must be specified if do_normalize is True.'''       )

         # All transformations expect numpy arrays.
         A_							       =		[to_numpy_array(_UpperCamelCase       ) for image in images]

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

         if do_center_crop:
               A_							       =		[self.center_crop(image=_UpperCamelCase			,						size=_UpperCamelCase       ) for image in images]

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

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

         A_							       =		[to_channel_dimension_format(_UpperCamelCase			,						_UpperCamelCase       ) for image in images]

         A_							       =		{'''pixel_values''': images}
         return BatchFeature(data=_UpperCamelCase			,						tensor_type=_UpperCamelCase       )








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__      ( __A    :Optional[int]	,__A    :Any	,__A    :str		):


       """simple docstring"""




       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 						lowerCamelCase__      ( __A    :np.ndarray	,__A    :Optional[str]	,__A    :Optional[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     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							["pixel_values"]


       def __init__(		self      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = PILImageResampling.BILINEAR      , _UpperCamelCase = True      , _UpperCamelCase = 1 / 2_55      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = ""      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = PILImageResampling.BILINEAR      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              __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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              return rescale(_UpperCamelCase      , scale=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              return normalize(_UpperCamelCase      , mean=_UpperCamelCase      , std=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase=None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = ChannelDimension.FIRST      , **_UpperCamelCase      , )      ->							PIL.Image.Image:






              """simple docstring"""


              __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

