






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 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





     """simple docstring"""






     def 	_lowercase					( self					:  int	):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



     @property
     def 	_lowercase					( self					:  List[str]	):
      snake_case__			:				Tuple       =  1
      snake_case__			:				Optional[int]       =  3
      snake_case__			:				Dict       =  (3_2, 3_2)

      snake_case__			:				str       =  floats_tensor((batch_size, num_channels) + sizes							,		rng=random.Random(0	)	).to(a_	)
      return image



     @property
     def 	_lowercase					( self					:  int	):
      torch.manual_seed(0	)
      snake_case__			:				str       =  UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)							,		layers_per_block=2							,		sample_size=3_2							,		in_channels=4							,		out_channels=4							,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,		up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,		cross_attention_dim=3_2							,		)
      return model



     @property
     def 	_lowercase					( self					:  Any	):
      torch.manual_seed(0	)
      snake_case__			:				str       =  AutoencoderKL(
          block_out_channels=[3_2, 6_4]							,		in_channels=3							,		out_channels=3							,		down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,		up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,		latent_channels=4							,		)
      return model



     @property
     def 	_lowercase					( self					:  Optional[Any]	):
      torch.manual_seed(0	)
      snake_case__			:				str       =  RobertaSeriesConfig(
          hidden_size=3_2							,		project_dim=3_2							,		intermediate_size=3_7							,		layer_norm_eps=1e-0_5							,		num_attention_heads=4							,		num_hidden_layers=5							,		pad_token_id=1							,		vocab_size=5_0_0_6							,		)
      return RobertaSeriesModelWithTransformation(a_	)



     @property
     def 	_lowercase					( self					:  List[Any]	):

      def extract(*__A					:  List[Any]							,		**__A					:  str	):



       class 							SCREAMING_SNAKE_CASE__						:





            """simple docstring"""






            def __init__( self					:  Optional[int]	):
             snake_case__			:				Optional[Any]       =  torch.ones([0]	)






            def 	_lowercase					( self					:  Optional[int]							,		__A					:  List[Any]	):
             self.pixel_values.to(a_	)
             return self
       return Out()

      return extract



     def 	_lowercase					( self					:  Dict	):
      snake_case__			:				List[str]       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      snake_case__			:				Dict       =  self.dummy_cond_unet
      snake_case__			:				int       =  PNDMScheduler(skip_prk_steps=a_	)
      snake_case__			:				Tuple       =  self.dummy_vae
      snake_case__			:				Optional[Any]       =  self.dummy_text_encoder
      snake_case__			:				Dict       =  XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"	)
      snake_case__			:				List[str]       =  7_7

      snake_case__			:				List[str]       =  self.dummy_image.to(a_	)
      snake_case__			:				Dict       =  init_image / 2 + 0.5

      # make sure here that pndm scheduler skips prk
      snake_case__			:				Union[str, Any]       =  AltDiffusionImgaImgPipeline(
          unet=a_							,		scheduler=a_							,		vae=a_							,		text_encoder=a_							,		tokenizer=a_							,		safety_checker=a_							,		feature_extractor=self.dummy_extractor							,		)
      snake_case__			:				str       =  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,		do_normalize=a_	)
      snake_case__			:				Dict       =  alt_pipe.to(a_	)
      alt_pipe.set_progress_bar_config(disable=a_	)

      snake_case__			:				int       =  '''A painting of a squirrel eating a burger'''
      snake_case__			:				Tuple       =  torch.Generator(device=a_	).manual_seed(0	)
      snake_case__			:				Dict       =  alt_pipe(
          [prompt]							,		generator=a_							,		guidance_scale=6.0							,		num_inference_steps=2							,		output_type="np"							,		image=a_							,		)

      snake_case__			:				List[Any]       =  output.images

      snake_case__			:				List[Any]       =  torch.Generator(device=a_	).manual_seed(0	)
      snake_case__			:				Tuple       =  alt_pipe(
          [prompt]							,		generator=a_							,		guidance_scale=6.0							,		num_inference_steps=2							,		output_type="np"							,		image=a_							,		return_dict=a_							,		)[0]

      snake_case__			:				Union[str, Any]       =  image[0, -3:, -3:, -1]
      snake_case__			:				Tuple       =  image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      snake_case__			:				Dict       =  np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]	)

      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 	_lowercase					( self					:  Dict	):
      snake_case__			:				Optional[int]       =  self.dummy_cond_unet
      snake_case__			:				Optional[Any]       =  PNDMScheduler(skip_prk_steps=a_	)
      snake_case__			:				Any       =  self.dummy_vae
      snake_case__			:				List[Any]       =  self.dummy_text_encoder
      snake_case__			:				Optional[Any]       =  XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"	)
      snake_case__			:				Tuple       =  7_7

      snake_case__			:				Optional[Any]       =  self.dummy_image.to(a_	)

      # put models in fp16
      snake_case__			:				List[Any]       =  unet.half()
      snake_case__			:				int       =  vae.half()
      snake_case__			:				Tuple       =  bert.half()

      # make sure here that pndm scheduler skips prk
      snake_case__			:				Optional[Any]       =  AltDiffusionImgaImgPipeline(
          unet=a_							,		scheduler=a_							,		vae=a_							,		text_encoder=a_							,		tokenizer=a_							,		safety_checker=a_							,		feature_extractor=self.dummy_extractor							,		)
      snake_case__			:				List[str]       =  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,		do_normalize=a_	)
      snake_case__			:				int       =  alt_pipe.to(a_	)
      alt_pipe.set_progress_bar_config(disable=a_	)

      snake_case__			:				Optional[Any]       =  '''A painting of a squirrel eating a burger'''
      snake_case__			:				Dict       =  torch.manual_seed(0	)
      snake_case__			:				Any       =  alt_pipe(
          [prompt]							,		generator=a_							,		num_inference_steps=2							,		output_type="np"							,		image=a_							,		).images

      assert image.shape == (1, 3_2, 3_2, 3)






     @unittest.skipIf(torch_device != "cuda"							,		"This test requires a GPU"	)
     def 	_lowercase					( self					:  Dict	):
      snake_case__			:				Tuple       =  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
      snake_case__			:				Optional[Any]       =  init_image.resize((7_6_0, 5_0_4)	)

      snake_case__			:				Optional[Any]       =  '''BAAI/AltDiffusion'''
      snake_case__			:				Tuple       =  AltDiffusionImgaImgPipeline.from_pretrained(
          a_							,		safety_checker=a_							,		)
      pipe.to(a_	)
      pipe.set_progress_bar_config(disable=a_	)
      pipe.enable_attention_slicing()

      snake_case__			:				List[Any]       =  '''A fantasy landscape, trending on artstation'''

      snake_case__			:				Dict       =  torch.manual_seed(0	)
      snake_case__			:				Optional[Any]       =  pipe(
          prompt=a_							,		image=a_							,		strength=0.7_5							,		guidance_scale=7.5							,		generator=a_							,		output_type="np"							,		)
      snake_case__			:				Union[str, Any]       =  output.images[0]

      snake_case__			:				Any       =  image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

      assert image.shape == (5_0_4, 7_6_0, 3)
      snake_case__			:				Dict       =  np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]	)

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



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





     """simple docstring"""






     def 	_lowercase					( self					:  List[str]	):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






     def 	_lowercase					( self					:  List[str]	):
      snake_case__			:				List[Any]       =  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/img2img/sketch-mountains-input.jpg"	)
      snake_case__			:				Dict       =  init_image.resize((7_6_8, 5_1_2)	)
      snake_case__			:				Tuple       =  load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"	)

      snake_case__			:				Tuple       =  '''BAAI/AltDiffusion'''
      snake_case__			:				Optional[int]       =  AltDiffusionImgaImgPipeline.from_pretrained(
          a_							,		safety_checker=a_							,		)
      pipe.to(a_	)
      pipe.set_progress_bar_config(disable=a_	)
      pipe.enable_attention_slicing()

      snake_case__			:				Optional[Any]       =  '''A fantasy landscape, trending on artstation'''

      snake_case__			:				Optional[Any]       =  torch.manual_seed(0	)
      snake_case__			:				str       =  pipe(
          prompt=a_							,		image=a_							,		strength=0.7_5							,		guidance_scale=7.5							,		generator=a_							,		output_type="np"							,		)
      snake_case__			:				List[str]       =  output.images[0]

      assert image.shape == (5_1_2, 7_6_8, 3)
      # img2img is flaky across GPUs even in fp32, so using MAE here
      assert np.abs(expected_image - image	).max() < 1e-2











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__lowerCamelCase :      int										=				logging.get_logger(__name__)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							Any       ):
	snake_case__			:				List[str]       =  b.T
	snake_case__			:				Union[str, Any]       =  np.sum(np.square(snake_case_       )					,  axis=1       )
	snake_case__			:				Dict       =  np.sum(np.square(snake_case_       )					,  axis=0       )
	snake_case__			:				Dict       =  np.matmul(snake_case_					,  snake_case_       )
	snake_case__			:				Any       =  aa[:, None] - 2 * ab + ba[None, :]
	return d







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]					,  snake_case_ :							Tuple       ):
	snake_case__			:				Tuple       =  x.reshape(-1					,  3       )
	snake_case__			:				int       =  squared_euclidean_distance(snake_case_					,  snake_case_       )
	return np.argmin(snake_case_					,  axis=1       )





class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["pixel_values"]



					def __init__( self					:  str							,		__A					:  Optional[Union[List[List[int]], np.ndarray]] = None							,		__A					:  bool = True							,		__A					:  Dict[str, int] = None							,		__A					:  PILImageResampling = PILImageResampling.BILINEAR							,		__A					:  bool = True							,		__A					:  bool = True							,		**__A					:  Union[str, Any]							,		):
						super().__init__(**__A	)
						snake_case__			:				Optional[int]       =  size if size is not None else {"height": 2_5_6, "width": 2_5_6}
						snake_case__			:				List[Any]       =  get_size_dict(__A	)
						snake_case__			:				Any       =  np.array(__A	) if clusters is not None else None
						snake_case__			:				Optional[Any]       =  do_resize
						snake_case__			:				Any       =  size
						snake_case__			:				List[Any]       =  resample
						snake_case__			:				List[Any]       =  do_normalize
						snake_case__			:				Dict       =  do_color_quantize



					def 	_lowercase					( self					:  List[Any]							,		__A					:  np.ndarray							,		__A					:  Dict[str, int]							,		__A					:  PILImageResampling = PILImageResampling.BILINEAR							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		**__A					:  int							,		):
						snake_case__			:				List[Any]       =  get_size_dict(__A	)
						if "height" not in size or "width" not in size:
							raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''	)
						return resize(
						    __A							,		size=(size["height"], size["width"])							,		resample=__A							,		data_format=__A							,		**__A	)



					def 	_lowercase					( self					:  List[Any]							,		__A					:  np.ndarray							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		):
						snake_case__			:				List[str]       =  rescale(image=__A							,		scale=1 / 1_2_7.5							,		data_format=__A	)
						snake_case__			:				List[Any]       =  image - 1
						return image






					def 	_lowercase					( self					:  Dict							,		__A					:  ImageInput							,		__A					:  bool = None							,		__A					:  Dict[str, int] = None							,		__A					:  PILImageResampling = None							,		__A					:  bool = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[Union[List[List[int]], np.ndarray]] = None							,		__A					:  Optional[Union[str, TensorType]] = None							,		__A					:  Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST							,		**__A					:  Optional[int]							,		):
						snake_case__			:				Any       =  do_resize if do_resize is not None else self.do_resize
						snake_case__			:				Union[str, Any]       =  size if size is not None else self.size
						snake_case__			:				Union[str, Any]       =  get_size_dict(__A	)
						snake_case__			:				Optional[Any]       =  resample if resample is not None else self.resample
						snake_case__			:				Tuple       =  do_normalize if do_normalize is not None else self.do_normalize
						snake_case__			:				Optional[Any]       =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						snake_case__			:				Union[str, Any]       =  clusters if clusters is not None else self.clusters
						snake_case__			:				Union[str, Any]       =  np.array(__A	)

						snake_case__			:				Any       =  make_list_of_images(__A	)

						if not valid_images(__A	):
							raise ValueError(
							    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
							    "torch.Tensor, tf.Tensor or jax.ndarray."	)

						if do_resize and size is None or resample is None:
							raise ValueError("Size and resample must be specified if do_resize is True."	)

						if do_color_quantize and clusters is None:
							raise ValueError("Clusters must be specified if do_color_quantize is True."	)

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

						if do_resize:
							snake_case__			:				List[str]       =  [self.resize(image=__A							,		size=__A							,		resample=__A	) for image in images]

						if do_normalize:
							snake_case__			:				Union[str, Any]       =  [self.normalize(image=__A	) for image in images]

						if do_color_quantize:
							snake_case__			:				int       =  [to_channel_dimension_format(__A							,		ChannelDimension.LAST	) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							snake_case__			:				int       =  np.array(__A	)
							snake_case__			:				Dict       =  color_quantize(__A							,		__A	).reshape(images.shape[:-1]	)

							# flatten to (batch_size, height*width)
							snake_case__			:				str       =  images.shape[0]
							snake_case__			:				str       =  images.reshape(__A							,		-1	)

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							snake_case__			:				Union[str, Any]       =  list(__A	)
						else:
							snake_case__			:				Any       =  [to_channel_dimension_format(__A							,		__A	) for image in images]

						snake_case__			:				Optional[int]       =  {"input_ids": images}
						return BatchFeature(data=__A							,		tensor_type=__A	)



