






'''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							:							Optional[Any]  							=		logging.get_logger(__name__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return [
     int(1000 * (box[0] / width)),
     int(1000 * (box[1] / height)),
     int(1000 * (box[2] / width)),
     int(1000 * (box[3] / height)),
 ]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     to_pil_image(lowercase__)
 lowerCamelCase__  ,	lowerCamelCase__  =     pil_image.size
 lowerCamelCase__  =     pytesseract.image_to_data(lowercase__				,     lang=lowercase__				,     output_type="dict"				,     config=lowercase__)
 lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     data["text"], data["left"], data["top"], data["width"], data["height"]

 # filter empty words and corresponding coordinates
 lowerCamelCase__  =     [idx for idx, word in enumerate(lowercase__) if not word.strip()]
 lowerCamelCase__  =     [word for idx, word in enumerate(lowercase__) if idx not in irrelevant_indices]
 lowerCamelCase__  =     [coord for idx, coord in enumerate(lowercase__) if idx not in irrelevant_indices]
 lowerCamelCase__  =     [coord for idx, coord in enumerate(lowercase__) if idx not in irrelevant_indices]
 lowerCamelCase__  =     [coord for idx, coord in enumerate(lowercase__) if idx not in irrelevant_indices]
 lowerCamelCase__  =     [coord for idx, coord in enumerate(lowercase__) if idx not in irrelevant_indices]

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

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

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

 return words, normalized_boxes
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["pixel_values"]

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

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )
     lowerCamelCase__  =     size if size is not None else {"height": 224, "width": 224}
     lowerCamelCase__  =     get_size_dict(__lowerCamelCase     )

     lowerCamelCase__  =     do_resize
     lowerCamelCase__  =     size
     lowerCamelCase__  =     resample
     lowerCamelCase__  =     do_rescale
     lowerCamelCase__  =     rescale_value
     lowerCamelCase__  =     do_normalize
     lowerCamelCase__  =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__  =     image_std if image_std is not None else IMAGENET_STANDARD_STD
     lowerCamelCase__  =     apply_ocr
     lowerCamelCase__  =     ocr_lang
     lowerCamelCase__  =     tesseract_config

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

     '''simple docstring'''




     lowerCamelCase__  =     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()}'''     )
     lowerCamelCase__  =     (size["height"], size["width"])
     return resize(__lowerCamelCase				,     size=__lowerCamelCase				,     resample=__lowerCamelCase				,     data_format=__lowerCamelCase				,     **__lowerCamelCase     )

    def    a__				(					self     :     int				,     __lowerCamelCase     :     np.ndarray				,     __lowerCamelCase     :     Union[int, float]				,     __lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None				,     **__lowerCamelCase     :     Optional[Any]				,     )							->							np.ndarray:

     '''simple docstring'''




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

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

     '''simple docstring'''




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




    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     ImageInput				,     __lowerCamelCase     :     bool = None				,     __lowerCamelCase     :     Dict[str, int] = None				,     __lowerCamelCase     :     str=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     :     Optional[Any]				,     )							->							PIL.Image.Image:

     '''simple docstring'''




     lowerCamelCase__  =     do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__  =     size if size is not None else self.size
     lowerCamelCase__  =     get_size_dict(__lowerCamelCase     )
     lowerCamelCase__  =     resample if resample is not None else self.resample
     lowerCamelCase__  =     do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__  =     rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__  =     do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__  =     image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__  =     image_std if image_std is not None else self.image_std
     lowerCamelCase__  =     apply_ocr if apply_ocr is not None else self.apply_ocr
     lowerCamelCase__  =     ocr_lang if ocr_lang is not None else self.ocr_lang
     lowerCamelCase__  =     tesseract_config if tesseract_config is not None else self.tesseract_config

     lowerCamelCase__  =     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.
     lowerCamelCase__  =     [to_numpy_array(__lowerCamelCase     ) for image in images]

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

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

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

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

     lowerCamelCase__  =     [to_channel_dimension_format(__lowerCamelCase				,     __lowerCamelCase     ) for image in images]

     lowerCamelCase__  =     BatchFeature(data={"pixel_values": images}				,     tensor_type=__lowerCamelCase     )

     if apply_ocr:
      lowerCamelCase__  =     words_batch
      lowerCamelCase__  =     boxes_batch
     return data













'''simple docstring'''





from decimal import Decimal, getcontext
from math import ceil, factorial






def    lowerCamelCase_     (    lowercase__):

 if not isinstance(lowercase__				,     lowercase__):
  raise TypeError("Undefined for non-integers")
 elif precision < 1:
  raise ValueError("Undefined for non-natural numbers")

 lowerCamelCase__  =     precision
 lowerCamelCase__  =     ceil(precision / 14)
 lowerCamelCase__  =     426880 * Decimal(10005).sqrt()
 lowerCamelCase__  =     1
 lowerCamelCase__  =     13591409
 lowerCamelCase__  =     Decimal(lowercase__)
 for k in range(1				,     lowercase__):
  lowerCamelCase__  =     factorial(6 * k) // (factorial(3 * k) * factorial(lowercase__) ** 3)
  linear_term += 545140134
  exponential_term *= -262537412640768000
  partial_sum += Decimal(multinomial_term * linear_term) / exponential_term
 return str(constant_term / partial_sum)[:-1]


if __name__ == "__main__":
 __A							:							Dict  							=		50
 print(F"""The first {n} digits of pi is: {pi(n)}""")

