

import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
       import textwrap

       from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
       import torch

       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       snake_case_        =			False


snake_case_        =			logging.get_logger(__name__)
snake_case_        =			"""ybelkada/fonts"""

def      snake_case__      (   ):



     '''simple docstring'''





     if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
          raise ImportError(
              f"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
              'Pix2StructImageProcessor. Please upgrade torch.'      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     requires_backends(SCREAMING_SNAKE_CASE_     ,					['torch']      )
     _check_torch_version()

     lowercase__   :       Dict				=	image_tensor.unsqueeze(0      )
     lowercase__   :       int				=	torch.nn.functional.unfold(SCREAMING_SNAKE_CASE_     ,					(patch_height, patch_width)     ,					stride=(patch_height, patch_width)      )
     lowercase__   :       List[str]				=	patches.reshape(image_tensor.size(0      )     ,					image_tensor.size(1      )     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					-1      )
     lowercase__   :       int				=	patches.permute(0     ,					4     ,					2     ,					3     ,					1      ).reshape(
         image_tensor.size(2      ) // patch_height     ,					image_tensor.size(3      ) // patch_width     ,					image_tensor.size(1      ) * patch_height * patch_width     ,					)
     return patches.unsqueeze(0      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   int = 36     ,					SCREAMING_SNAKE_CASE_		:   str = "black"     ,					SCREAMING_SNAKE_CASE_		:   str = "white"     ,					SCREAMING_SNAKE_CASE_		:   int = 5     ,					SCREAMING_SNAKE_CASE_		:   int = 5     ,					SCREAMING_SNAKE_CASE_		:   int = 5     ,					SCREAMING_SNAKE_CASE_		:   int = 5     ,					SCREAMING_SNAKE_CASE_		:   Optional[bytes] = None     ,					SCREAMING_SNAKE_CASE_		:   Optional[str] = None     ,					):



     '''simple docstring'''





     requires_backends(SCREAMING_SNAKE_CASE_     ,					'vision'      )
     # Add new lines so that each line is no more than 80 characters.

     lowercase__   :       Tuple				=	textwrap.TextWrapper(width=80      )
     lowercase__   :       List[Any]				=	wrapper.wrap(text=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[int]				=	'\n'.join(SCREAMING_SNAKE_CASE_      )

     if font_bytes is not None and font_path is None:
          lowercase__   :       List[str]				=	io.BytesIO(SCREAMING_SNAKE_CASE_      )
     elif font_path is not None:
          lowercase__   :       Optional[Any]				=	font_path
     else:
          lowercase__   :       Dict				=	hf_hub_download(SCREAMING_SNAKE_CASE_     ,					'Arial.TTF'      )
     lowercase__   :       Any				=	ImageFont.truetype(SCREAMING_SNAKE_CASE_     ,					encoding='UTF-8'     ,					size=SCREAMING_SNAKE_CASE_      )

     # Use a temporary canvas to determine the width and height in pixels when
     # rendering the text.
     lowercase__   :       Optional[Any]				=	ImageDraw.Draw(Image.new('RGB'     ,					(1, 1)     ,					SCREAMING_SNAKE_CASE_      )      )
     lowercase__				,			lowercase__				,			lowercase__				,			lowercase__   :       List[str]				=	temp_draw.textbbox((0, 0)     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     # Create the actual image with a bit of padding around the text.
     lowercase__   :       List[str]				=	text_width + left_padding + right_padding
     lowercase__   :       int				=	text_height + top_padding + bottom_padding
     lowercase__   :       int				=	Image.new('RGB'     ,					(image_width, image_height)     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Tuple				=	ImageDraw.Draw(SCREAMING_SNAKE_CASE_      )
     draw.text(xy=(left_padding, top_padding)     ,					text=SCREAMING_SNAKE_CASE_     ,					fill=SCREAMING_SNAKE_CASE_     ,					font=SCREAMING_SNAKE_CASE_      )
     return image

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   str     ,					**SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     requires_backends(SCREAMING_SNAKE_CASE_     ,					'vision'      )

     # Convert to PIL image if necessary
     lowercase__   :       List[str]				=	to_pil_image(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       Union[str, Any]				=	render_text(SCREAMING_SNAKE_CASE_     ,					**SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[int]				=	max(header_image.width     ,					image.width      )

     lowercase__   :       List[str]				=	int(image.height * (new_width / image.width)      )
     lowercase__   :       Union[str, Any]				=	int(header_image.height * (new_width / header_image.width)      )

     lowercase__   :       str				=	Image.new('RGB'     ,					(new_width, new_height + new_header_height)     ,					'white'      )
     new_image.paste(header_image.resize((new_width, new_header_height)      )     ,					(0, 0)      )
     new_image.paste(image.resize((new_width, new_height)      )     ,					(0, new_header_height)      )

     # Convert back to the original framework if necessary
     lowercase__   :       Dict				=	to_numpy_array(SCREAMING_SNAKE_CASE_      )

     if infer_channel_dimension_format(SCREAMING_SNAKE_CASE_      ) == ChannelDimension.LAST:
          lowercase__   :       Optional[int]				=	to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,					ChannelDimension.LAST      )

     return new_image


class 				SCREAMING_SNAKE_CASE__ (lowerCAmelCase__					):
    __lowerCamelCase						:	Optional[int]														=					["flattened_patches"]





    def __init__(				self						,       a = True						,       a = True						,       a = None						,       a = 2048						,       a = False						,       **a						,       ):
         super().__init__(**a__)
         lowercase__   :       Union[str, Any]				=	patch_size if patch_size is not None else {'height': 16, 'width': 16}
         lowercase__   :       Union[str, Any]				=	do_normalize
         lowercase__   :       Any				=	do_convert_rgb
         lowercase__   :       Optional[int]				=	max_patches
         lowercase__   :       Any				=	is_vqa





    def 				snake_case_    (				self						,       a						,       a						,       a						,       **a):
         requires_backends(self.extract_flattened_patches						,       'torch')
         _check_torch_version()

         # convert to torch
         lowercase__   :       List[Any]				=	to_channel_dimension_format(a__						,       ChannelDimension.FIRST)
         lowercase__   :       Optional[Any]				=	torch.from_numpy(a__)

         lowercase__				,			lowercase__   :       Any				=	patch_size['height'], patch_size['width']
         lowercase__				,			lowercase__   :       int				=	get_image_size(a__)

         # maximize scale s.t.
         lowercase__   :       Optional[int]				=	math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width))
         lowercase__   :       Optional[Any]				=	max(min(math.floor(scale * image_height / patch_height)						,       a__)						,       1)
         lowercase__   :       Optional[int]				=	max(min(math.floor(scale * image_width / patch_width)						,       a__)						,       1)
         lowercase__   :       Optional[int]				=	max(num_feasible_rows * patch_height						,       1)
         lowercase__   :       List[Any]				=	max(num_feasible_cols * patch_width						,       1)

         lowercase__   :       Optional[Any]				=	torch.nn.functional.interpolate(
             image.unsqueeze(0)						,       size=(resized_height, resized_width)						,       mode='bilinear'						,       align_corners=a__						,       antialias=a__						,       ).squeeze(0)

         # [1, rows, columns, patch_height * patch_width * image_channels]
         lowercase__   :       Dict				=	torch_extract_patches(a__						,       a__						,       a__)

         lowercase__   :       List[Any]				=	patches.shape
         lowercase__   :       Optional[Any]				=	patches_shape[1]
         lowercase__   :       List[str]				=	patches_shape[2]
         lowercase__   :       Optional[Any]				=	patches_shape[3]

         # [rows * columns, patch_height * patch_width * image_channels]
         lowercase__   :       Dict				=	patches.reshape([rows * columns, depth])

         # [rows * columns, 1]
         lowercase__   :       Union[str, Any]				=	torch.arange(a__).reshape([rows, 1]).repeat(1						,       a__).reshape([rows * columns, 1])
         lowercase__   :       str				=	torch.arange(a__).reshape([1, columns]).repeat(a__						,       1).reshape([rows * columns, 1])

         # Offset by 1 so the ids do not contain zeros, which represent padding.
         row_ids += 1
         col_ids += 1

         # Prepare additional patch features.
         # [rows * columns, 1]
         lowercase__   :       List[Any]				=	row_ids.to(torch.floataa)
         lowercase__   :       Any				=	col_ids.to(torch.floataa)

         # [rows * columns, 2 + patch_height * patch_width * image_channels]
         lowercase__   :       Tuple				=	torch.cat([row_ids, col_ids, patches]						,       -1)

         # [max_patches, 2 + patch_height * patch_width * image_channels]
         lowercase__   :       List[Any]				=	torch.nn.functional.pad(a__						,       [0, 0, 0, max_patches - (rows * columns)]).float()

         lowercase__   :       Any				=	to_numpy_array(a__)

         return result





    def 				snake_case_    (				self						,       a						,       a = None						,       **a):
         if image.dtype == np.uinta:
              lowercase__   :       Optional[Any]				=	image.astype(np.floataa)

         # take mean across the whole `image`
         lowercase__   :       List[str]				=	np.mean(a__)
         lowercase__   :       Optional[Any]				=	np.std(a__)
         lowercase__   :       Dict				=	max(a__						,       1.0 / math.sqrt(np.prod(image.shape)))

         return normalize(a__						,       mean=a__						,       std=a__						,       **a__)





    def 				snake_case_    (				self						,       a						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = ChannelDimension.FIRST						,       **a						,       ):
         lowercase__   :       Dict				=	do_normalize if do_normalize is not None else self.do_normalize
         lowercase__   :       int				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
         lowercase__   :       Dict				=	patch_size if patch_size is not None else self.patch_size
         lowercase__   :       str				=	max_patches if max_patches is not None else self.max_patches
         lowercase__   :       Dict				=	self.is_vqa

         if kwargs.get('data_format'						,       a__) is not None:
              raise ValueError('data_format is not an accepted input as the outputs are ')

         lowercase__   :       Union[str, 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.')

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              lowercase__   :       Any				=	[convert_to_rgb(a__) for image in images]

         # All transformations expect numpy arrays.
         lowercase__   :       Union[str, Any]				=	[to_numpy_array(a__) for image in images]

         if is_vqa:
              if header_text is None:
                   raise ValueError('A header text must be provided for VQA models.')
              lowercase__   :       Dict				=	kwargs.pop('font_bytes'						,       a__)
              lowercase__   :       Any				=	kwargs.pop('font_path'						,       a__)

              if isinstance(a__						,       a__):
                   lowercase__   :       Optional[int]				=	[header_text] * len(a__)

              lowercase__   :       Optional[int]				=	[
                  render_header(a__						,       header_text[i]						,       font_bytes=a__						,       font_path=a__)
                  for i, image in enumerate(a__)
              ]

         if do_normalize:
              lowercase__   :       List[str]				=	[self.normalize(image=a__) for image in images]

         # convert to torch tensor and permute
         lowercase__   :       str				=	[
             self.extract_flattened_patches(image=a__						,       max_patches=a__						,       patch_size=a__)
             for image in images
         ]

         # create attention mask in numpy
         lowercase__   :       List[str]				=	[(image.sum(axis=-1) != 0).astype(np.floataa) for image in images]

         lowercase__   :       int				=	BatchFeature(
             data={'flattened_patches': images, 'attention_mask': attention_masks}						,       tensor_type=a__)

         return encoded_outputs




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     return 1 if digit in (0, 1) else (digit * factorial(digit - 1      ))

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	0
     lowercase__   :       Optional[int]				=	number
     while duplicate > 0:
          lowercase__				,			lowercase__   :       Tuple				=	divmod(SCREAMING_SNAKE_CASE_     ,					10      )
          fact_sum += factorial(SCREAMING_SNAKE_CASE_      )
     return fact_sum == number


if __name__ == "__main__":
       print('''Program to check whether a number is a Krisnamurthy Number or not.''')
       snake_case_        =			int(input('''Enter number: ''').strip())
       print(
           F'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
       )

