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

lowercase_  : Tuple								=					logging.get_logger(__name__)







def       A__   (						snake_case_			:		List[Any]							,					snake_case_			:		Dict							,					snake_case_			:		List[Any]   ):
 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       A__   (						snake_case_			:		np.ndarray							,					snake_case_			:		Optional[str]							,					snake_case_			:		Optional[str]   ):
 SCREAMING_SNAKE_CASE__: str=   to_pil_image(snake_case_   )
 SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Union[str, Any]=   pil_image.size
 SCREAMING_SNAKE_CASE__: Optional[int]=   pytesseract.image_to_data(snake_case_							,					lang=snake_case_							,					output_type='''dict'''							,					config=snake_case_   )
 SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[int]=   data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

 # finally, normalize the bounding boxes
 SCREAMING_SNAKE_CASE__: Dict=   []
 for box in actual_boxes:
  normalized_boxes.append(normalize_box(snake_case_							,					snake_case_							,					snake_case_   )   )

 assert len(snake_case_   ) == len(snake_case_   ), "Not as many words as there are bounding boxes"

 return words, normalized_boxes






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   ["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			,			)							->       None:
     super().__init__(**lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   size if size is not None else {'''height''': 224, '''width''': 224}
     SCREAMING_SNAKE_CASE__: List[str]=   get_size_dict(lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Any=   do_resize
     SCREAMING_SNAKE_CASE__: Any=   size
     SCREAMING_SNAKE_CASE__: Any=   resample
     SCREAMING_SNAKE_CASE__: Dict=   do_rescale
     SCREAMING_SNAKE_CASE__: Optional[Any]=   rescale_value
     SCREAMING_SNAKE_CASE__: Optional[Any]=   do_normalize
     SCREAMING_SNAKE_CASE__: Optional[int]=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   image_std if image_std is not None else IMAGENET_STANDARD_STD
     SCREAMING_SNAKE_CASE__: Any=   apply_ocr
     SCREAMING_SNAKE_CASE__: int=   ocr_lang
     SCREAMING_SNAKE_CASE__: int=   tesseract_config
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = PILImageResampling.BILINEAR			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       np.ndarray:
     SCREAMING_SNAKE_CASE__: Any=   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()}' )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   (size['''height'''], size['''width'''])
     return resize(lowerCAmelCase			,			size=lowerCAmelCase			,			resample=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       np.ndarray:
     return rescale(lowerCAmelCase			,			scale=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       np.ndarray:
     return normalize(lowerCAmelCase			,			mean=lowerCAmelCase			,			std=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )



    def     UpperCamelCase_					(	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			,			)							->       PIL.Image.Image:
     SCREAMING_SNAKE_CASE__: Tuple=   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__: List[Any]=   size if size is not None else self.size
     SCREAMING_SNAKE_CASE__: int=   get_size_dict(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: List[str]=   resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__: List[Any]=   do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__: List[Any]=   rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__: Optional[Any]=   do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__: List[str]=   image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE__: Tuple=   image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE__: Optional[Any]=   apply_ocr if apply_ocr is not None else self.apply_ocr
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   ocr_lang if ocr_lang is not None else self.ocr_lang
     SCREAMING_SNAKE_CASE__: List[str]=   tesseract_config if tesseract_config is not None else self.tesseract_config

     SCREAMING_SNAKE_CASE__: int=   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.
     SCREAMING_SNAKE_CASE__: str=   [to_numpy_array(lowerCAmelCase ) for image in images]

     # Tesseract OCR to get words + normalized bounding boxes
     if apply_ocr:
      requires_backends(self			,			'''pytesseract''' )
      SCREAMING_SNAKE_CASE__: Dict=   []
      SCREAMING_SNAKE_CASE__: Optional[Any]=   []
      for image in images:
       SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: List[str]=   apply_tesseract(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )
       words_batch.append(lowerCAmelCase )
       boxes_batch.append(lowerCAmelCase )

     if do_resize:
      SCREAMING_SNAKE_CASE__: str=   [self.resize(image=lowerCAmelCase			,			size=lowerCAmelCase			,			resample=lowerCAmelCase ) for image in images]

     if do_rescale:
      SCREAMING_SNAKE_CASE__: Tuple=   [self.rescale(image=lowerCAmelCase			,			scale=lowerCAmelCase ) for image in images]

     if do_normalize:
      SCREAMING_SNAKE_CASE__: List[Any]=   [self.normalize(image=lowerCAmelCase			,			mean=lowerCAmelCase			,			std=lowerCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__: Optional[int]=   [to_channel_dimension_format(lowerCAmelCase			,			lowerCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__: Dict=   BatchFeature(data={'''pixel_values''': images}			,			tensor_type=lowerCAmelCase )

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




"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 if digit_amount > 0:
  return round(number - int(lowercase__   )					,					lowercase__   )
 return number - int(lowercase__   )


if __name__ == "__main__":
    print(decimal_isolate(1.53, 0))
    print(decimal_isolate(35.345, 1))
    print(decimal_isolate(35.345, 2))
    print(decimal_isolate(35.345, 3))
    print(decimal_isolate(-14.789, 3))
    print(decimal_isolate(0, 2))
    print(decimal_isolate(-14.123, 1))
    print(decimal_isolate(-14.123, 2))
    print(decimal_isolate(-14.123, 3))






