



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:
    a__      :				Any   	=			False


a__      :				int   	=			logging.get_logger(__name__)
a__      :				List[Any]   	=			"ybelkada/fonts"

def   _lowerCAmelCase   (						):
      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   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      requires_backends(snake_case__						,  ['torch']	)
      _check_torch_version()

      lowercase__     =  image_tensor.unsqueeze(0	)
      lowercase__     =  torch.nn.functional.unfold(snake_case__						,  (patch_height, patch_width)						,  stride=(patch_height, patch_width)	)
      lowercase__     =  patches.reshape(image_tensor.size(0	)						,  image_tensor.size(1	)						,  snake_case__						,  snake_case__						,  -1	)
      lowercase__     =  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   _lowerCAmelCase   (						A__						,  A__ = 36						,  A__ = "black"						,  A__ = "white"						,  A__ = 5						,  A__ = 5						,  A__ = 5						,  A__ = 5						,  A__ = None						,  A__ = None						,  ):
      requires_backends(snake_case__						,  'vision'	)
      # Add new lines so that each line is no more than 80 characters.

      lowercase__     =  textwrap.TextWrapper(width=80	)
      lowercase__     =  wrapper.wrap(text=snake_case__	)
      lowercase__     =  '\n'.join(snake_case__	)

      if font_bytes is not None and font_path is None:
            lowercase__     =  io.BytesIO(snake_case__	)
      elif font_path is not None:
            lowercase__     =  font_path
      else:
            lowercase__     =  hf_hub_download(snake_case__						,  'Arial.TTF'	)
      lowercase__     =  ImageFont.truetype(snake_case__						,  encoding='UTF-8'						,  size=snake_case__	)

      # Use a temporary canvas to determine the width and height in pixels when
      # rendering the text.
      lowercase__     =  ImageDraw.Draw(Image.new('RGB'						,  (1, 1)						,  snake_case__	)	)
      lowercase__,	lowercase__,	lowercase__,	lowercase__     =  temp_draw.textbbox((0, 0)						,  snake_case__						,  snake_case__	)

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

def   _lowerCAmelCase   (						A__						,  A__						,  **A__	):
      requires_backends(snake_case__						,  'vision'	)

      # Convert to PIL image if necessary
      lowercase__     =  to_pil_image(snake_case__	)

      lowercase__     =  render_text(snake_case__						,  **snake_case__	)
      lowercase__     =  max(header_image.width						,  image.width	)

      lowercase__     =  int(image.height * (new_width / image.width)	)
      lowercase__     =  int(header_image.height * (new_width / header_image.width)	)

      lowercase__     =  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__     =  to_numpy_array(snake_case__	)

      if infer_channel_dimension_format(snake_case__	) == ChannelDimension.LAST:
            lowercase__     =  to_channel_dimension_format(snake_case__						,  ChannelDimension.LAST	)

      return new_image



class       UpperCAmelCase__(							__lowerCAmelCase	):

   '''simple docstring'''




   A    :  Tuple       				=    ['''flattened_patches''']


   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			int = True			,				lowerCAmelCase  :			Union[str, Any] = True			,				lowerCAmelCase  :			List[Any] = None			,				lowerCAmelCase  :			Dict = 20_48			,				lowerCAmelCase  :			int = False			,				**lowerCAmelCase  :			Any			,				)	->				None:




         """simple docstring"""

         super().__init__(**lowerCAmelCase_)
         lowercase__     =  patch_size if patch_size is not None else {'height': 16, 'width': 16}
         lowercase__     =  do_normalize
         lowercase__     =  do_convert_rgb
         lowercase__     =  max_patches
         lowercase__     =  is_vqa


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[Any]			,				**lowerCAmelCase  :			Optional[int])	->				np.ndarray:




         """simple docstring"""

         requires_backends(self.extract_flattened_patches			,				'torch')
         _check_torch_version()

         # convert to torch
         lowercase__     =  to_channel_dimension_format(lowerCAmelCase_			,				ChannelDimension.FIRST)
         lowercase__     =  torch.from_numpy(lowerCAmelCase_)

         lowercase__,	lowercase__     =  patch_size['height'], patch_size['width']
         lowercase__,	lowercase__     =  get_image_size(lowerCAmelCase_)

         # maximize scale s.t.
         lowercase__     =  math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width))
         lowercase__     =  max(min(math.floor(scale * image_height / patch_height)			,				lowerCAmelCase_)			,				1)
         lowercase__     =  max(min(math.floor(scale * image_width / patch_width)			,				lowerCAmelCase_)			,				1)
         lowercase__     =  max(num_feasible_rows * patch_height			,				1)
         lowercase__     =  max(num_feasible_cols * patch_width			,				1)

         lowercase__     =  torch.nn.functional.interpolate(
             image.unsqueeze(0)			,				size=(resized_height, resized_width)			,				mode='bilinear'			,				align_corners=lowerCAmelCase_			,				antialias=lowerCAmelCase_			,				).squeeze(0)

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

         lowercase__     =  patches.shape
         lowercase__     =  patches_shape[1]
         lowercase__     =  patches_shape[2]
         lowercase__     =  patches_shape[3]

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

         # [rows * columns, 1]
         lowercase__     =  torch.arange(lowerCAmelCase_).reshape([rows, 1]).repeat(1			,				lowerCAmelCase_).reshape([rows * columns, 1])
         lowercase__     =  torch.arange(lowerCAmelCase_).reshape([1, columns]).repeat(lowerCAmelCase_			,				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__     =  row_ids.to(torch.floataa)
         lowercase__     =  col_ids.to(torch.floataa)

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

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

         lowercase__     =  to_numpy_array(lowerCAmelCase_)

         return result


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			List[Any] = None			,				**lowerCAmelCase  :			Tuple)	->				np.ndarray:




         """simple docstring"""

         if image.dtype == np.uinta:
               lowercase__     =  image.astype(np.floataa)

         # take mean across the whole `image`
         lowercase__     =  np.mean(lowerCAmelCase_)
         lowercase__     =  np.std(lowerCAmelCase_)
         lowercase__     =  max(lowerCAmelCase_			,				1.0 / math.sqrt(np.prod(image.shape)))

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



   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			int = None			,				lowerCAmelCase  :			Optional[int] = None			,				lowerCAmelCase  :			Optional[int] = None			,				lowerCAmelCase  :			str = None			,				lowerCAmelCase  :			Optional[Any] = None			,				lowerCAmelCase  :			Dict = None			,				lowerCAmelCase  :			Dict = ChannelDimension.FIRST			,				**lowerCAmelCase  :			Any			,				)	->				ImageInput:




         """simple docstring"""

         lowercase__     =  do_normalize if do_normalize is not None else self.do_normalize
         lowercase__     =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
         lowercase__     =  patch_size if patch_size is not None else self.patch_size
         lowercase__     =  max_patches if max_patches is not None else self.max_patches
         lowercase__     =  self.is_vqa

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

         lowercase__     =  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.')

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

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

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

               if isinstance(lowerCAmelCase_			,				lowerCAmelCase_):
                     lowercase__     =  [header_text] * len(lowerCAmelCase_)

               lowercase__     =  [
                   render_header(lowerCAmelCase_			,				header_text[i]			,				font_bytes=lowerCAmelCase_			,				font_path=lowerCAmelCase_)
                   for i, image in enumerate(lowerCAmelCase_)
               ]

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

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

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

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

         return encoded_outputs





import argparse
import hashlib  # hashlib is only used inside the Test class
import struct



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Optional[Any]			,				lowerCAmelCase  :			str)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  data
         lowercase__     =  [0X6_7_4_5_2_3_0_1, 0XE_F_C_D_A_B_8_9, 0X9_8_B_A_D_C_F_E, 0X1_0_3_2_5_4_7_6, 0XC_3_D_2_E_1_F_0]


   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			Optional[int])	->				str:




         """simple docstring"""

         return ((n << b) | (n >> (32 - b))) & 0XF_F_F_F_F_F_F_F


   def 			UpperCAmelCase		(						self  :			Dict)	->				Dict:




         """simple docstring"""

         lowercase__     =  B'\x80' + B'\x00' * (63 - (len(self.data) + 8) % 64)
         lowercase__     =  self.data + padding + struct.pack('>Q'			,				8 * len(self.data))
         return padded_data


   def 			UpperCAmelCase		(						self  :			int)	->				Tuple:




         """simple docstring"""

         return [
             self.padded_data[i : i + 64] for i in range(0			,				len(self.padded_data)			,				64)
         ]


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			int)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  list(struct.unpack('>16L'			,				lowerCAmelCase)) + [0] * 64
         for i in range(16			,				80):
               lowercase__     =  self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])			,				1)
         return w



   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.padding()
         lowercase__     =  self.split_blocks()
         for block in self.blocks:
               lowercase__     =  self.expand_block(lowerCAmelCase)
               lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  self.h
               for i in range(0			,				80):
                     if 0 <= i < 20:
                           lowercase__     =  (b & c) | ((~b) & d)
                           lowercase__     =  0X5_A_8_2_7_9_9_9
                     elif 20 <= i < 40:
                           lowercase__     =  b ^ c ^ d
                           lowercase__     =  0X6_E_D_9_E_B_A_1
                     elif 40 <= i < 60:
                           lowercase__     =  (b & c) | (b & d) | (c & d)
                           lowercase__     =  0X8_F_1_B_B_C_D_C
                     elif 60 <= i < 80:
                           lowercase__     =  b ^ c ^ d
                           lowercase__     =  0XC_A_6_2_C_1_D_6
                     lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  (
                         self.rotate(lowerCAmelCase			,				5) + f + e + k + expanded_block[i] & 0XF_F_F_F_F_F_F_F,
                         a,
                         self.rotate(lowerCAmelCase			,				30),
                         c,
                         d,
                     )
               lowercase__     =  (
                   self.h[0] + a & 0XF_F_F_F_F_F_F_F,
                   self.h[1] + b & 0XF_F_F_F_F_F_F_F,
                   self.h[2] + c & 0XF_F_F_F_F_F_F_F,
                   self.h[3] + d & 0XF_F_F_F_F_F_F_F,
                   self.h[4] + e & 0XF_F_F_F_F_F_F_F,
               )
         return ("{:08x}" * 5).format(*self.h)

def   _lowerCAmelCase   (						):
      lowercase__     =  B'Test String'
      assert SHAaHash(A__	).final_hash() == hashlib.shaa(A__	).hexdigest()  # noqa: S324

def   _lowerCAmelCase   (						):
      lowercase__     =  argparse.ArgumentParser(description='Process some strings or files'	)
      parser.add_argument(
          '--string'						,  dest='input_string'						,  default='Hello World!! Welcome to Cryptography'						,  help='Hash the string'						,  )
      parser.add_argument('--file'						,  dest='input_file'						,  help='Hash contents of a file'	)
      lowercase__     =  parser.parse_args()
      lowercase__     =  args.input_string
      # In any case hash input should be a bytestring
      if args.input_file:
            with open(args.input_file						,  'rb'	) as f:
                  lowercase__     =  f.read()
      else:
            lowercase__     =  bytes(A__						,  'utf-8'	)
      print(SHAaHash(A__	).final_hash()	)


if __name__ == "__main__":
       main()
       import doctest

       doctest.testmod()
