
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):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:List[str]						)		-> Optional[Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	@property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> List[Any]:
								__UpperCamelCase	:   Any			   =					1
								__UpperCamelCase	:   Union[str, Any]			   =					3
								__UpperCamelCase	:   List[str]			   =					(3_2, 3_2)

								__UpperCamelCase	:   Tuple			   =					floats_tensor((batch_size, num_channels) + sizes     ,   rng=random.Random(0						)						).to(__lowerCAmelCase						)
								return image







	@property
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Any:
								torch.manual_seed(0						)
								__UpperCamelCase	:   List[Any]			   =					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    _lowerCamelCase				(					self		:Optional[Any]						)		-> Any:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Optional[int]			   =					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    _lowerCamelCase				(					self		:Optional[int]						)		-> Tuple:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Any			   =					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(__lowerCAmelCase						)







	@property
	def    _lowerCamelCase				(					self		:str						)		-> int:







								def extract(*a		:Any     ,   **a		:Optional[int]						):




															class  lowerCamelCase__							:




																'''simple docstring'''




																def __init__(					self		:str						)		-> List[str]:
																							__UpperCamelCase	:   Dict			   =					torch.ones([0]						)







																def    _lowerCamelCase				(					self		:Any     ,   a		:Optional[int]						)		-> Any:
																							self.pixel_values.to(__lowerCAmelCase						)
																							return self
															return Out()

								return extract







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Optional[int]:
								__UpperCamelCase	:   List[Any]			   =					"cpu"  # ensure determinism for the device-dependent torch.Generator
								__UpperCamelCase	:   Dict			   =					self.dummy_cond_unet
								__UpperCamelCase	:   Union[str, Any]			   =					PNDMScheduler(skip_prk_steps=__lowerCAmelCase						)
								__UpperCamelCase	:   Optional[int]			   =					self.dummy_vae
								__UpperCamelCase	:   Optional[Any]			   =					self.dummy_text_encoder
								__UpperCamelCase	:   List[str]			   =					XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"						)
								__UpperCamelCase	:   Optional[Any]			   =					7_7

								__UpperCamelCase	:   Optional[int]			   =					self.dummy_image.to(__lowerCAmelCase						)
								__UpperCamelCase	:   Tuple			   =					init_image / 2 + 0.5

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

								__UpperCamelCase	:   str			   =					"A painting of a squirrel eating a burger"
								__UpperCamelCase	:   Dict			   =					torch.Generator(device=__lowerCAmelCase						).manual_seed(0						)
								__UpperCamelCase	:   Union[str, Any]			   =					alt_pipe(
								    [prompt]     ,   generator=__lowerCAmelCase     ,   guidance_scale=6.0     ,   num_inference_steps=2     ,   output_type="np"     ,   image=__lowerCAmelCase     ,   )

								__UpperCamelCase	:   str			   =					output.images

								__UpperCamelCase	:   Optional[Any]			   =					torch.Generator(device=__lowerCAmelCase						).manual_seed(0						)
								__UpperCamelCase	:   str			   =					alt_pipe(
								    [prompt]     ,   generator=__lowerCAmelCase     ,   guidance_scale=6.0     ,   num_inference_steps=2     ,   output_type="np"     ,   image=__lowerCAmelCase     ,   return_dict=__lowerCAmelCase     ,   )[0]

								__UpperCamelCase	:   Union[str, Any]			   =					image[0, -3:, -3:, -1]
								__UpperCamelCase	:   Optional[Any]			   =					image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 3_2, 3_2, 3)
								__UpperCamelCase	:   Union[str, Any]			   =					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    _lowerCamelCase				(					self		:Optional[int]						)		-> str:
								__UpperCamelCase	:   List[str]			   =					self.dummy_cond_unet
								__UpperCamelCase	:   Dict			   =					PNDMScheduler(skip_prk_steps=__lowerCAmelCase						)
								__UpperCamelCase	:   Any			   =					self.dummy_vae
								__UpperCamelCase	:   Any			   =					self.dummy_text_encoder
								__UpperCamelCase	:   Any			   =					XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"						)
								__UpperCamelCase	:   Dict			   =					7_7

								__UpperCamelCase	:   Tuple			   =					self.dummy_image.to(__lowerCAmelCase						)

								# put models in fp16
								__UpperCamelCase	:   Any			   =					unet.half()
								__UpperCamelCase	:   str			   =					vae.half()
								__UpperCamelCase	:   int			   =					bert.half()

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

								__UpperCamelCase	:   Any			   =					"A painting of a squirrel eating a burger"
								__UpperCamelCase	:   Tuple			   =					torch.manual_seed(0						)
								__UpperCamelCase	:   Tuple			   =					alt_pipe(
								    [prompt]     ,   generator=__lowerCAmelCase     ,   num_inference_steps=2     ,   output_type="np"     ,   image=__lowerCAmelCase     ,   ).images

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







	@unittest.skipIf(torch_device != "cuda"     ,   "This test requires a GPU"						)
	def    _lowerCamelCase				(					self		:List[str]						)		-> List[Any]:
								__UpperCamelCase	:   Optional[Any]			   =					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
								__UpperCamelCase	:   Optional[Any]			   =					init_image.resize((7_6_0, 5_0_4)						)

								__UpperCamelCase	:   str			   =					"BAAI/AltDiffusion"
								__UpperCamelCase	:   Optional[int]			   =					AltDiffusionImgaImgPipeline.from_pretrained(
								    __lowerCAmelCase     ,   safety_checker=__lowerCAmelCase     ,   )
								pipe.to(__lowerCAmelCase						)
								pipe.set_progress_bar_config(disable=__lowerCAmelCase						)
								pipe.enable_attention_slicing()

								__UpperCamelCase	:   Dict			   =					"A fantasy landscape, trending on artstation"

								__UpperCamelCase	:   Optional[int]			   =					torch.manual_seed(0						)
								__UpperCamelCase	:   Optional[int]			   =					pipe(
								    prompt=__lowerCAmelCase     ,   image=__lowerCAmelCase     ,   strength=0.75     ,   guidance_scale=7.5     ,   generator=__lowerCAmelCase     ,   output_type="np"     ,   )
								__UpperCamelCase	:   Tuple			   =					output.images[0]

								__UpperCamelCase	:   List[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)
								__UpperCamelCase	:   Optional[int]			   =					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):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:str						)		-> int:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







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

								__UpperCamelCase	:   Tuple			   =					"BAAI/AltDiffusion"
								__UpperCamelCase	:   int			   =					AltDiffusionImgaImgPipeline.from_pretrained(
								    __lowerCAmelCase     ,   safety_checker=__lowerCAmelCase     ,   )
								pipe.to(__lowerCAmelCase						)
								pipe.set_progress_bar_config(disable=__lowerCAmelCase						)
								pipe.enable_attention_slicing()

								__UpperCamelCase	:   Optional[int]			   =					"A fantasy landscape, trending on artstation"

								__UpperCamelCase	:   List[str]			   =					torch.manual_seed(0						)
								__UpperCamelCase	:   List[Any]			   =					pipe(
								    prompt=__lowerCAmelCase     ,   image=__lowerCAmelCase     ,   strength=0.75     ,   guidance_scale=7.5     ,   generator=__lowerCAmelCase     ,   output_type="np"     ,   )
								__UpperCamelCase	:   Dict			   =					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






def lowerCAmelCase__(__snake_case							)				->      str:







	'''simple docstring'''







	return "".join(chr(ord(__snake_case							) - 32							) if '''a''' <= char <= '''z''' else char for char in word							)


if __name__ == "__main__":
			from doctest import testmod

			testmod()



