
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:
			_lowerCamelCase       :				List[Any]       =			False


_lowerCamelCase       :				Union[str, Any]       =			logging.get_logger(__name__)
_lowerCamelCase       :				Dict       =			'''ybelkada/fonts'''






def        a_		(       ) ->  List[str]:
			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_		(       __lowercase   :	Any       ,					__lowercase   :	Optional[Any]       ,					__lowercase   :	Optional[Any] ) ->  List[str]:
			requires_backends(__lowercase       ,					['torch'] )
			_check_torch_version()

			_snake_case		=			image_tensor.unsqueeze(0 )
			_snake_case		=			torch.nn.functional.unfold(__lowercase       ,					(patch_height, patch_width)       ,					stride=(patch_height, patch_width) )
			_snake_case		=			patches.reshape(image_tensor.size(0 )       ,					image_tensor.size(1 )       ,					__lowercase       ,					__lowercase       ,					-1 )
			_snake_case		=			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_		(       __lowercase   :	str       ,					__lowercase   :	int = 36       ,					__lowercase   :	str = "black"       ,					__lowercase   :	str = "white"       ,					__lowercase   :	int = 5       ,					__lowercase   :	int = 5       ,					__lowercase   :	int = 5       ,					__lowercase   :	int = 5       ,					__lowercase   :	Optional[bytes] = None       ,					__lowercase   :	Optional[str] = None       ,					) ->  Image.Image:
			requires_backends(__lowercase       ,					'vision' )
			# Add new lines so that each line is no more than 80 characters.

			_snake_case		=			textwrap.TextWrapper(width=80 )
			_snake_case		=			wrapper.wrap(text=__lowercase )
			_snake_case		=			'\n'.join(__lowercase )

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

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

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






def        a_		(       __lowercase   :	np.ndarray       ,					__lowercase   :	str       ,					**__lowercase   :	Tuple ) ->  Tuple:
			requires_backends(__lowercase       ,					'vision' )

			# Convert to PIL image if necessary
			_snake_case		=			to_pil_image(__lowercase )

			_snake_case		=			render_text(__lowercase       ,					**__lowercase )
			_snake_case		=			max(header_image.width       ,					image.width )

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

			_snake_case		=			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
			_snake_case		=			to_numpy_array(__lowercase )

			if infer_channel_dimension_format(__lowercase ) == ChannelDimension.LAST:
						_snake_case		=			to_channel_dimension_format(__lowercase       ,					ChannelDimension.LAST )

			return new_image




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		str       =					["flattened_patches"]



	def __init__(  self	:    int			,  lowercase	:    bool = True			,  lowercase	:    bool = True			,  lowercase	:    Dict[str, int] = None			,  lowercase	:    int = 2_048			,  lowercase	:    bool = False			,  **lowercase	:    Union[str, Any]			,  ):


				'''simple docstring'''


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



	def  A							(  self	:    List[Any]			,  lowercase	:    np.ndarray			,  lowercase	:    int			,  lowercase	:    dict			,  **lowercase	:    Union[str, Any]	):


				'''simple docstring'''


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

				# convert to torch
				_snake_case		=			to_channel_dimension_format(lowercase			,  ChannelDimension.FIRST	)
				_snake_case		=			torch.from_numpy(lowercase	)

				_snake_case	,					_snake_case		=			patch_size['height'], patch_size['width']
				_snake_case	,					_snake_case		=			get_image_size(lowercase	)

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

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

				# [1, rows, columns, patch_height * patch_width * image_channels]
				_snake_case		=			torch_extract_patches(lowercase			,  lowercase			,  lowercase	)

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

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

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

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

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

				_snake_case		=			to_numpy_array(lowercase	)

				return result



	def  A							(  self	:    Dict			,  lowercase	:    np.ndarray			,  lowercase	:    Optional[Union[str, ChannelDimension]] = None			,  **lowercase	:    Tuple	):


				'''simple docstring'''


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

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

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





	def  A							(  self	:    Optional[int]			,  lowercase	:    ImageInput			,  lowercase	:    Optional[str] = None			,  lowercase	:    bool = None			,  lowercase	:    Optional[bool] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[Dict[str, int]] = None			,  lowercase	:    Optional[Union[str, TensorType]] = None			,  lowercase	:    ChannelDimension = ChannelDimension.FIRST			,  **lowercase	:    int			,  ):


				'''simple docstring'''


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

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

				_snake_case		=			make_list_of_images(lowercase	)

				if not valid_images(lowercase	):
							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:
							_snake_case		=			[convert_to_rgb(lowercase	) for image in images]

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

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

							if isinstance(lowercase			,  lowercase	):
										_snake_case		=			[header_text] * len(lowercase	)

							_snake_case		=			[
							    render_header(lowercase			,  header_text[i]			,  font_bytes=lowercase			,  font_path=lowercase	)
							    for i, image in enumerate(lowercase	)
							]

				if do_normalize:
							_snake_case		=			[self.normalize(image=lowercase	) for image in images]

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

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

				_snake_case		=			BatchFeature(
				    data={'flattened_patches': images, 'attention_mask': attention_masks}			,  tensor_type=lowercase	)

				return encoded_outputs

import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return self.get_dummy_input()



	@property
	def  A							(  self	:    Any	):


				'''simple docstring'''


				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''	)



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Any=True			,  lowercase	:    List[Any]=False			,  lowercase	:    List[str]=False			,  lowercase	:    Dict=False			,  ):


				'''simple docstring'''


				_snake_case		=			4
				_snake_case		=			32
				_snake_case		=			(32, 32)

				_snake_case		=			torch.manual_seed(0	)
				_snake_case		=			torch.device(lowercase	)
				_snake_case		=			(batch_size, num_channels) + sizes
				_snake_case		=			randn_tensor(lowercase			,  generator=lowercase			,  device=lowercase	)
				_snake_case		=			{'hidden_states': hidden_states}

				if include_temb:
							_snake_case		=			128
							_snake_case		=			randn_tensor((batch_size, temb_channels)			,  generator=lowercase			,  device=lowercase	)

				if include_res_hidden_states_tuple:
							_snake_case		=			torch.manual_seed(1	)
							_snake_case		=			(randn_tensor(lowercase			,  generator=lowercase			,  device=lowercase	),)

				if include_encoder_hidden_states:
							_snake_case		=			floats_tensor((batch_size, 32, 32)	).to(lowercase	)

				if include_skip_sample:
							_snake_case		=			randn_tensor(((batch_size, 3) + sizes)			,  generator=lowercase			,  device=lowercase	)

				return dummy_input



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			{
				    'in_channels': 32,
				    'out_channels': 32,
				    'temb_channels': 128,
				}
				if self.block_type == "up":
							_snake_case		=			32

				if self.block_type == "mid":
							init_dict.pop('out_channels'	)

				_snake_case		=			self.dummy_input
				return init_dict, inputs_dict



	def  A							(  self	:    Dict			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.prepare_init_args_and_inputs_for_common()
				_snake_case		=			self.block_class(**lowercase	)
				unet_block.to(lowercase	)
				unet_block.eval()

				with torch.no_grad():
							_snake_case		=			unet_block(**lowercase	)

				if isinstance(lowercase			,  lowercase	):
							_snake_case		=			output[0]

				self.assertEqual(output.shape			,  self.output_shape	)

				_snake_case		=			output[0, -1, -3:, -3:]
				_snake_case		=			torch.tensor(lowercase	).to(lowercase	)
				assert torch_all_close(output_slice.flatten()			,  lowercase			,  atol=5E-3	)





	@unittest.skipIf(torch_device == 'mps'			,  'Training is not supported in mps'	)
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.prepare_init_args_and_inputs_for_common()
				_snake_case		=			self.block_class(**lowercase	)
				model.to(lowercase	)
				model.train()
				_snake_case		=			model(**lowercase	)

				if isinstance(lowercase			,  lowercase	):
							_snake_case		=			output[0]

				_snake_case		=			torch.device(lowercase	)
				_snake_case		=			randn_tensor(output.shape			,  device=lowercase	)
				_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  lowercase	)
				loss.backward()