


'''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:
		__snake_case							      =     False


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


def 			a      (			)  ->	Optional[Any]:



					'''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 			a      (			__a ,					__a ,					__a   )  ->	Optional[int]:



					'''simple docstring'''






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

					UpperCamelCase__   :Tuple												= image_tensor.unsqueeze(0   )
					UpperCamelCase__   :Optional[int]												= torch.nn.functional.unfold(__a ,					(patch_height, patch_width) ,					stride=(patch_height, patch_width)   )
					UpperCamelCase__   :Union[str, Any]												= patches.reshape(image_tensor.size(0   ) ,					image_tensor.size(1   ) ,					__a ,					__a ,					-1   )
					UpperCamelCase__   :List[Any]												= 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 			a      (			__a ,					__a = 36 ,					__a = "black" ,					__a = "white" ,					__a = 5 ,					__a = 5 ,					__a = 5 ,					__a = 5 ,					__a = None ,					__a = None ,					)  ->	Image.Image:



					'''simple docstring'''






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

					UpperCamelCase__   :Union[str, Any]												= textwrap.TextWrapper(width=80   )
					UpperCamelCase__   :int												= wrapper.wrap(text=__a   )
					UpperCamelCase__   :Union[str, Any]												= '''\n'''.join(__a   )

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

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

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


def 			a      (			__a ,					__a ,					**__a   )  ->	Optional[Any]:



					'''simple docstring'''






					requires_backends(__a ,					'''vision'''   )

					# Convert to PIL image if necessary
					UpperCamelCase__   :Optional[int]												= to_pil_image(__a   )

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

					UpperCamelCase__   :List[str]												= int(image.height * (new_width / image.width)   )
					UpperCamelCase__   :Dict												= int(header_image.height * (new_width / header_image.width)   )

					UpperCamelCase__   :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
					UpperCamelCase__   :Optional[int]												= to_numpy_array(__a   )

					if infer_channel_dimension_format(__a   ) == ChannelDimension.LAST:
										UpperCamelCase__   :int												= to_channel_dimension_format(__a ,					ChannelDimension.LAST   )

					return new_image







class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				['flattened_patches']




		def __init__(		self    ,   UpperCamelCase_ = True    ,   UpperCamelCase_ = True    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = 2048    ,   UpperCamelCase_ = False    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__   :List[Any]												= patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
							UpperCamelCase__   :Optional[int]												= do_normalize
							UpperCamelCase__   :List[str]												= do_convert_rgb
							UpperCamelCase__   :Union[str, Any]												= max_patches
							UpperCamelCase__   :Optional[int]												= is_vqa




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


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

							# convert to torch
							UpperCamelCase__   :Union[str, Any]												= to_channel_dimension_format(UpperCamelCase_    ,   ChannelDimension.FIRST							)
							UpperCamelCase__   :Dict												= torch.from_numpy(UpperCamelCase_							)

							UpperCamelCase__					,				UpperCamelCase__   :Tuple												= patch_size['''height'''], patch_size['''width''']
							UpperCamelCase__					,				UpperCamelCase__   :Any												= get_image_size(UpperCamelCase_							)

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

							UpperCamelCase__   :Optional[int]												= 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]
							UpperCamelCase__   :Optional[int]												= torch_extract_patches(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)

							UpperCamelCase__   :List[Any]												= patches.shape
							UpperCamelCase__   :Dict												= patches_shape[1]
							UpperCamelCase__   :int												= patches_shape[2]
							UpperCamelCase__   :List[str]												= patches_shape[3]

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

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

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

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

							UpperCamelCase__   :Union[str, Any]												= to_numpy_array(UpperCamelCase_							)

							return result




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   **UpperCamelCase_							):

							'''simple docstring'''


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

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

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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = ChannelDimension.FIRST    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__   :Tuple												= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							UpperCamelCase__   :List[Any]												= patch_size if patch_size is not None else self.patch_size
							UpperCamelCase__   :Any												= max_patches if max_patches is not None else self.max_patches
							UpperCamelCase__   :Optional[int]												= 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 '''							)

							UpperCamelCase__   :Dict												= 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:
												UpperCamelCase__   :List[str]												= [convert_to_rgb(UpperCamelCase_							) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase__   :Optional[int]												= [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.'''							)
												UpperCamelCase__   :Tuple												= kwargs.pop('''font_bytes'''    ,   UpperCamelCase_							)
												UpperCamelCase__   :Any												= kwargs.pop('''font_path'''    ,   UpperCamelCase_							)

												if isinstance(UpperCamelCase_    ,   UpperCamelCase_							):
																	UpperCamelCase__   :Any												= [header_text] * len(UpperCamelCase_							)

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

							if do_normalize:
												UpperCamelCase__   :Optional[int]												= [self.normalize(image=UpperCamelCase_							) for image in images]

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

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

							UpperCamelCase__   :List[str]												= BatchFeature(
							    data={'''flattened_patches''': images, '''attention_mask''': attention_masks}    ,   tensor_type=UpperCamelCase_							)

							return encoded_outputs

"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime


@require_onnxruntime
class A_	:




  '''simple docstring'''
  pass


