'''simple docstring'''







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:
		UpperCamelCase_      	=	False


UpperCamelCase_      	=	logging.get_logger(__name__)
UpperCamelCase_      	=	'''ybelkada/fonts'''


def    lowerCamelCase       (			)			->	Dict:


							'''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    lowerCamelCase       (			UpperCAmelCase__				:  Optional[Any]				,						UpperCAmelCase__				:  List[str]				,						UpperCAmelCase__				:  List[Any]		)			->	int:


							'''simple docstring'''






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

							SCREAMING_SNAKE_CASE__     :int						    = image_tensor.unsqueeze(0		)
							SCREAMING_SNAKE_CASE__     :str						    = torch.nn.functional.unfold(UpperCAmelCase__				,						(patch_height, patch_width)				,						stride=(patch_height, patch_width)		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = patches.reshape(image_tensor.size(0		)				,						image_tensor.size(1		)				,						UpperCAmelCase__				,						UpperCAmelCase__				,						-1		)
							SCREAMING_SNAKE_CASE__     :str						    = 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       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  int = 3_6				,						UpperCAmelCase__				:  str = "black"				,						UpperCAmelCase__				:  str = "white"				,						UpperCAmelCase__				:  int = 5				,						UpperCAmelCase__				:  int = 5				,						UpperCAmelCase__				:  int = 5				,						UpperCAmelCase__				:  int = 5				,						UpperCAmelCase__				:  Optional[bytes] = None				,						UpperCAmelCase__				:  Optional[str] = None				,						)			->	Image.Image:


							'''simple docstring'''






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

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = textwrap.TextWrapper(width=8_0		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = wrapper.wrap(text=UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :List[str]						    = '\n'.join(UpperCAmelCase__		)

							if font_bytes is not None and font_path is None:
														SCREAMING_SNAKE_CASE__     :str						    = io.BytesIO(UpperCAmelCase__		)
							elif font_path is not None:
														SCREAMING_SNAKE_CASE__     :Dict						    = font_path
							else:
														SCREAMING_SNAKE_CASE__     :Tuple						    = hf_hub_download(UpperCAmelCase__				,						'Arial.TTF'		)
							SCREAMING_SNAKE_CASE__     :int						    = ImageFont.truetype(UpperCAmelCase__				,						encoding='UTF-8'				,						size=UpperCAmelCase__		)

							# Use a temporary canvas to determine the width and height in pixels when
							# rendering the text.
							SCREAMING_SNAKE_CASE__     :Tuple						    = ImageDraw.Draw(Image.new('RGB'				,						(1, 1)				,						UpperCAmelCase__		)		)
							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = temp_draw.textbbox((0, 0)				,						UpperCAmelCase__				,						UpperCAmelCase__		)

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


def    lowerCamelCase       (			UpperCAmelCase__				:  np.ndarray				,						UpperCAmelCase__				:  str				,						**UpperCAmelCase__				:  List[str]		)			->	Tuple:


							'''simple docstring'''






							requires_backends(UpperCAmelCase__				,						'vision'		)

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

							SCREAMING_SNAKE_CASE__     :str						    = render_text(UpperCAmelCase__				,						**UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :List[str]						    = max(header_image.width				,						image.width		)

							SCREAMING_SNAKE_CASE__     :int						    = int(image.height * (new_width / image.width)		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = int(header_image.height * (new_width / header_image.width)		)

							SCREAMING_SNAKE_CASE__     :Dict						    = 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
							SCREAMING_SNAKE_CASE__     :Dict						    = to_numpy_array(UpperCAmelCase__		)

							if infer_channel_dimension_format(UpperCAmelCase__		) == ChannelDimension.LAST:
														SCREAMING_SNAKE_CASE__     :Tuple						    = to_channel_dimension_format(UpperCAmelCase__				,						ChannelDimension.LAST		)

							return new_image





class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Optional[int]     		= ['flattened_patches']




						def __init__(    self :       List[Any]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Dict[str, int] = None							,							UpperCamelCase_ :       int = 20_48							,							UpperCamelCase_ :       bool = False							,							**UpperCamelCase_ :       Tuple							,							)     ->				None:
													super().__init__(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = patch_size if patch_size is not None else {'height': 16, 'width': 16}
													SCREAMING_SNAKE_CASE__     :int						    = do_normalize
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = do_convert_rgb
													SCREAMING_SNAKE_CASE__     :Dict						    = max_patches
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = is_vqa




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       np.ndarray							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       dict							,							**UpperCamelCase_ :       str      )     ->				np.ndarray:
													requires_backends(self.extract_flattened_patches							,							'torch'      )
													_check_torch_version()

													# convert to torch
													SCREAMING_SNAKE_CASE__     :Dict						    = to_channel_dimension_format(UpperCamelCase_							,							ChannelDimension.FIRST      )
													SCREAMING_SNAKE_CASE__     :Any						    = torch.from_numpy(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = patch_size['height'], patch_size['width']
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[Any]						    = get_image_size(UpperCamelCase_      )

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

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.nn.functional.interpolate(
													    image.unsqueeze(0      )							,							size=(resized_height, resized_width)							,							mode='bilinear'							,							align_corners=UpperCamelCase_							,							antialias=UpperCamelCase_							,							).squeeze(0      )

													# [1, rows, columns, patch_height * patch_width * image_channels]
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch_extract_patches(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = patches.shape
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = patches_shape[1]
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = patches_shape[2]
													SCREAMING_SNAKE_CASE__     :List[str]						    = patches_shape[3]

													# [rows * columns, patch_height * patch_width * image_channels]
													SCREAMING_SNAKE_CASE__     :List[str]						    = patches.reshape([rows * columns, depth]      )

													# [rows * columns, 1]
													SCREAMING_SNAKE_CASE__     :int						    = torch.arange(UpperCamelCase_      ).reshape([rows, 1]      ).repeat(1							,							UpperCamelCase_      ).reshape([rows * columns, 1]      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = torch.arange(UpperCamelCase_      ).reshape([1, columns]      ).repeat(UpperCamelCase_							,							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]
													SCREAMING_SNAKE_CASE__     :str						    = row_ids.to(torch.floataa      )
													SCREAMING_SNAKE_CASE__     :Any						    = col_ids.to(torch.floataa      )

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

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

													SCREAMING_SNAKE_CASE__     :Any						    = to_numpy_array(UpperCamelCase_      )

													return result




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       np.ndarray							,							UpperCamelCase_ :       Optional[Union[str, ChannelDimension]] = None							,							**UpperCamelCase_ :       str      )     ->				np.ndarray:
													if image.dtype == np.uinta:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = image.astype(np.floataa      )

													# take mean across the whole `image`
													SCREAMING_SNAKE_CASE__     :int						    = np.mean(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.std(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = max(UpperCamelCase_							,							1.0 / math.sqrt(np.prod(image.shape      )      )      )

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




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       ImageInput							,							UpperCamelCase_ :       Optional[str] = None							,							UpperCamelCase_ :       bool = None							,							UpperCamelCase_ :       Optional[bool] = None							,							UpperCamelCase_ :       Optional[int] = None							,							UpperCamelCase_ :       Optional[Dict[str, int]] = None							,							UpperCamelCase_ :       Optional[Union[str, TensorType]] = None							,							UpperCamelCase_ :       ChannelDimension = ChannelDimension.FIRST							,							**UpperCamelCase_ :       List[str]							,							)     ->				ImageInput:
													SCREAMING_SNAKE_CASE__     :List[str]						    = do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
													SCREAMING_SNAKE_CASE__     :Dict						    = patch_size if patch_size is not None else self.patch_size
													SCREAMING_SNAKE_CASE__     :Tuple						    = max_patches if max_patches is not None else self.max_patches
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.is_vqa

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

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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.'      )

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

													# All transformations expect numpy arrays.
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = [to_numpy_array(UpperCamelCase_      ) for image in images]

													if is_vqa:
																				if header_text is None:
																											raise ValueError('A header text must be provided for VQA models.'      )
																				SCREAMING_SNAKE_CASE__     :Tuple						    = kwargs.pop('font_bytes'							,							UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = kwargs.pop('font_path'							,							UpperCamelCase_      )

																				if isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = [header_text] * len(UpperCamelCase_      )

																				SCREAMING_SNAKE_CASE__     :Dict						    = [
																				    render_header(UpperCamelCase_							,							header_text[i]							,							font_bytes=UpperCamelCase_							,							font_path=UpperCamelCase_      )
																				    for i, image in enumerate(UpperCamelCase_      )
																				]

													if do_normalize:
																				SCREAMING_SNAKE_CASE__     :Dict						    = [self.normalize(image=UpperCamelCase_      ) for image in images]

													# convert to torch tensor and permute
													SCREAMING_SNAKE_CASE__     :Dict						    = [
													    self.extract_flattened_patches(image=UpperCamelCase_							,							max_patches=UpperCamelCase_							,							patch_size=UpperCamelCase_      )
													    for image in images
													]

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

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = BatchFeature(
													    data={'flattened_patches': images, 'attention_mask': attention_masks}							,							tensor_type=UpperCamelCase_      )

													return encoded_outputs







'''simple docstring'''







def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  int		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :list[list[str]]						    = [[] for _ in range(UpperCAmelCase__		)]
							SCREAMING_SNAKE_CASE__     :Any						    = key - 1

							if key <= 0:
														raise ValueError('Height of grid can\'t be 0 or negative'		)
							if key == 1 or len(UpperCAmelCase__		) <= key:
														return input_string

							for position, character in enumerate(UpperCAmelCase__		):
														SCREAMING_SNAKE_CASE__     :Dict						    = position % (lowest * 2)  # puts it in bounds
														SCREAMING_SNAKE_CASE__     :Tuple						    = min(UpperCAmelCase__				,						lowest * 2 - num		)  # creates zigzag pattern
														temp_grid[num].append(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :int						    = [''.join(UpperCAmelCase__		) for row in temp_grid]
							SCREAMING_SNAKE_CASE__     :str						    = ''.join(UpperCAmelCase__		)

							return output_string


def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  int		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = []
							SCREAMING_SNAKE_CASE__     :str						    = key - 1

							if key <= 0:
														raise ValueError('Height of grid can\'t be 0 or negative'		)
							if key == 1:
														return input_string

							SCREAMING_SNAKE_CASE__     :list[list[str]]						    = [[] for _ in range(UpperCAmelCase__		)]  # generates template
							for position in range(len(UpperCAmelCase__		)		):
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = position % (lowest * 2)  # puts it in bounds
														SCREAMING_SNAKE_CASE__     :Dict						    = min(UpperCAmelCase__				,						lowest * 2 - num		)  # creates zigzag pattern
														temp_grid[num].append('*'		)

							SCREAMING_SNAKE_CASE__     :Any						    = 0
							for row in temp_grid:  # fills in the characters
														SCREAMING_SNAKE_CASE__     :int						    = input_string[counter : counter + len(UpperCAmelCase__		)]
														grid.append(list(UpperCAmelCase__		)		)
														counter += len(UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :Tuple						    = ''  # reads as zigzag
							for position in range(len(UpperCAmelCase__		)		):
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = position % (lowest * 2)  # puts it in bounds
														SCREAMING_SNAKE_CASE__     :Any						    = min(UpperCAmelCase__				,						lowest * 2 - num		)  # creates zigzag pattern
														output_string += grid[num][0]
														grid[num].pop(0		)
							return output_string


def    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	dict[int, str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = {}
							for key_guess in range(1				,						len(UpperCAmelCase__		)		):  # tries every key
														SCREAMING_SNAKE_CASE__     :List[str]						    = decrypt(UpperCAmelCase__				,						UpperCAmelCase__		)
							return results


if __name__ == "__main__":
		import doctest

		doctest.testmod()






