

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:
  lowercase_   							=  False


lowercase_   							=  logging.get_logger(__name__)
lowercase_   							=  'ybelkada/fonts'
def 	a (     )       -> List[str]:





      """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__      :  str ,  A__      :  int ,  A__      :  int	)       -> Optional[int]:





      """simple docstring"""



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

      _lowercase      			=image_tensor.unsqueeze(0	)
      _lowercase      			=torch.nn.functional.unfold(A__ ,  (patch_height, patch_width) ,  stride=(patch_height, patch_width)	)
      _lowercase      			=patches.reshape(image_tensor.size(0	) ,  image_tensor.size(1	) ,  A__ ,  A__ ,  -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 	a (     A__      :  str ,  A__      :  int = 36 ,  A__      :  str = "black" ,  A__      :  str = "white" ,  A__      :  int = 5 ,  A__      :  int = 5 ,  A__      :  int = 5 ,  A__      :  int = 5 ,  A__      :  Optional[bytes] = None ,  A__      :  Optional[str] = None ,  )       -> Image.Image:





      """simple docstring"""



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

      _lowercase      			=textwrap.TextWrapper(width=80	)
      _lowercase      			=wrapper.wrap(text=A__	)
      _lowercase      			='\n'.join(A__	)

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

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

      # 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) ,  A__	)
      _lowercase      			=ImageDraw.Draw(A__	)
      draw.text(xy=(left_padding, top_padding) ,  text=A__ ,  fill=A__ ,  font=A__	)
      return image
def 	a (     A__      :  np.ndarray ,  A__      :  str ,  **A__      :  Optional[int]	)       -> Tuple:





      """simple docstring"""



      requires_backends(A__ ,  'vision'	)

      # Convert to PIL image if necessary
      _lowercase      			=to_pil_image(A__	)

      _lowercase      			=render_text(A__ ,  **A__	)
      _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(A__	)

      if infer_channel_dimension_format(A__	) == ChannelDimension.LAST:
            _lowercase      			=to_channel_dimension_format(A__ ,  ChannelDimension.LAST	)

      return new_image







class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    ["""flattened_patches"""]




  def __init__(							self				,  lowerCAmelCase = True				,  lowerCAmelCase = True				,  lowerCAmelCase = None				,  lowerCAmelCase = 2_048				,  lowerCAmelCase = False				,  **lowerCAmelCase				,  )    ->					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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  **lowerCAmelCase   )    ->					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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = None				,  **lowerCAmelCase   )    ->					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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = ChannelDimension.FIRST				,  **lowerCAmelCase				,  )    ->					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 gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




  @property
  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=1
        _lowercase      			=3
        _lowercase      			=(32, 32)

        _lowercase      			=floats_tensor((batch_size, num_channels) + sizes				,  rng=random.Random(0   )   ).to(lowerCAmelCase   )
        return image




  @property
  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        torch.manual_seed(0   )
        _lowercase      			=UNetaDConditionModel(
            block_out_channels=(32, 64)				,  layers_per_block=2				,  sample_size=32				,  in_channels=4				,  out_channels=4				,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,  cross_attention_dim=32				,  )
        return model




  @property
  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        torch.manual_seed(0   )
        _lowercase      			=AutoencoderKL(
            block_out_channels=[32, 64]				,  in_channels=3				,  out_channels=3				,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,  latent_channels=4				,  )
        return model




  @property
  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        torch.manual_seed(0   )
        _lowercase      			=RobertaSeriesConfig(
            hidden_size=32				,  project_dim=32				,  intermediate_size=37				,  layer_norm_eps=1e-05				,  num_attention_heads=4				,  num_hidden_layers=5				,  pad_token_id=1				,  vocab_size=5_006				,  )
        return RobertaSeriesModelWithTransformation(lowerCAmelCase   )




  @property
  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        def extract(*lowerCAmelCase				,  **lowerCAmelCase   ):





              class 					__lowerCAmelCase	:




                def __init__(							self   )    ->					Union[str, Any]:
                      '''simple docstring'''






                      _lowercase      			=torch.ones([0]   )




                def     A__	(							self				,  lowerCAmelCase   )    ->					Dict:
                      '''simple docstring'''






                      self.pixel_values.to(lowerCAmelCase   )
                      return self
              return Out()

        return extract




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			='cpu'  # ensure determinism for the device-dependent torch.Generator
        _lowercase      			=self.dummy_cond_unet
        _lowercase      			=PNDMScheduler(skip_prk_steps=lowerCAmelCase   )
        _lowercase      			=self.dummy_vae
        _lowercase      			=self.dummy_text_encoder
        _lowercase      			=XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'   )
        _lowercase      			=77

        _lowercase      			=self.dummy_image.to(lowerCAmelCase   )
        _lowercase      			=init_image / 2 + 0.5

        # make sure here that pndm scheduler skips prk
        _lowercase      			=AltDiffusionImgaImgPipeline(
            unet=lowerCAmelCase				,  scheduler=lowerCAmelCase				,  vae=lowerCAmelCase				,  text_encoder=lowerCAmelCase				,  tokenizer=lowerCAmelCase				,  safety_checker=lowerCAmelCase				,  feature_extractor=self.dummy_extractor				,  )
        _lowercase      			=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,  do_normalize=lowerCAmelCase   )
        _lowercase      			=alt_pipe.to(lowerCAmelCase   )
        alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			='A painting of a squirrel eating a burger'
        _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )
        _lowercase      			=alt_pipe(
            [prompt]				,  generator=lowerCAmelCase				,  guidance_scale=6.0				,  num_inference_steps=2				,  output_type='np'				,  image=lowerCAmelCase				,  )

        _lowercase      			=output.images

        _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )
        _lowercase      			=alt_pipe(
            [prompt]				,  generator=lowerCAmelCase				,  guidance_scale=6.0				,  num_inference_steps=2				,  output_type='np'				,  image=lowerCAmelCase				,  return_dict=lowerCAmelCase				,  )[0]

        _lowercase      			=image[0, -3:, -3:, -1]
        _lowercase      			=image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _lowercase      			=np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-3
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 5e-3




  @unittest.skipIf(torch_device != 'cuda'				,  'This test requires a GPU'   )
  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.dummy_cond_unet
        _lowercase      			=PNDMScheduler(skip_prk_steps=lowerCAmelCase   )
        _lowercase      			=self.dummy_vae
        _lowercase      			=self.dummy_text_encoder
        _lowercase      			=XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'   )
        _lowercase      			=77

        _lowercase      			=self.dummy_image.to(lowerCAmelCase   )

        # put models in fp16
        _lowercase      			=unet.half()
        _lowercase      			=vae.half()
        _lowercase      			=bert.half()

        # make sure here that pndm scheduler skips prk
        _lowercase      			=AltDiffusionImgaImgPipeline(
            unet=lowerCAmelCase				,  scheduler=lowerCAmelCase				,  vae=lowerCAmelCase				,  text_encoder=lowerCAmelCase				,  tokenizer=lowerCAmelCase				,  safety_checker=lowerCAmelCase				,  feature_extractor=self.dummy_extractor				,  )
        _lowercase      			=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,  do_normalize=lowerCAmelCase   )
        _lowercase      			=alt_pipe.to(lowerCAmelCase   )
        alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			='A painting of a squirrel eating a burger'
        _lowercase      			=torch.manual_seed(0   )
        _lowercase      			=alt_pipe(
            [prompt]				,  generator=lowerCAmelCase				,  num_inference_steps=2				,  output_type='np'				,  image=lowerCAmelCase				,  ).images

        assert image.shape == (1, 32, 32, 3)




  @unittest.skipIf(torch_device != 'cuda'				,  'This test requires a GPU'   )
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg'   )
        # resize to resolution that is divisible by 8 but not 16 or 32
        _lowercase      			=init_image.resize((760, 504)   )

        _lowercase      			='BAAI/AltDiffusion'
        _lowercase      			=AltDiffusionImgaImgPipeline.from_pretrained(
            lowerCAmelCase				,  safety_checker=lowerCAmelCase				,  )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )
        pipe.enable_attention_slicing()

        _lowercase      			='A fantasy landscape, trending on artstation'

        _lowercase      			=torch.manual_seed(0   )
        _lowercase      			=pipe(
            prompt=lowerCAmelCase				,  image=lowerCAmelCase				,  strength=0.75				,  guidance_scale=7.5				,  generator=lowerCAmelCase				,  output_type='np'				,  )
        _lowercase      			=output.images[0]

        _lowercase      			=image[255:258, 383:386, -1]

        assert image.shape == (504, 760, 3)
        _lowercase      			=np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2





@slow
@require_torch_gpu
class 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg'   )
        _lowercase      			=init_image.resize((768, 512)   )
        _lowercase      			=load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'   )

        _lowercase      			='BAAI/AltDiffusion'
        _lowercase      			=AltDiffusionImgaImgPipeline.from_pretrained(
            lowerCAmelCase				,  safety_checker=lowerCAmelCase				,  )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )
        pipe.enable_attention_slicing()

        _lowercase      			='A fantasy landscape, trending on artstation'

        _lowercase      			=torch.manual_seed(0   )
        _lowercase      			=pipe(
            prompt=lowerCAmelCase				,  image=lowerCAmelCase				,  strength=0.75				,  guidance_scale=7.5				,  generator=lowerCAmelCase				,  output_type='np'				,  )
        _lowercase      			=output.images[0]

        assert image.shape == (512, 768, 3)
        # img2img is flaky across GPUs even in fp32, so using MAE here
        assert np.abs(expected_image - image   ).max() < 1e-2


