


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def  lowercase_							(				self      )							->	int:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    @property
    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      int														=						1
         __lowerCamelCase  :      List[str]														=						3
         __lowerCamelCase  :      Optional[int]														=						(32, 32)

         __lowerCamelCase  :      int														=						floats_tensor((batch_size, num_channels) + sizes     ,	rng=random.Random(0      )      ).to(SCREAMING_SNAKE_CASE_      )
         return image







    @property
    def  lowercase_							(				self      )							->	str:
         torch.manual_seed(0      )
         __lowerCamelCase  :      Tuple														=						UNetaDConditionModel(
             block_out_channels=(32, 32, 64)     ,	layers_per_block=2     ,	sample_size=32     ,	in_channels=7     ,	out_channels=4     ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')     ,	up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')     ,	cross_attention_dim=32     ,	attention_head_dim=8     ,	use_linear_projection=SCREAMING_SNAKE_CASE_     ,	only_cross_attention=(True, True, False)     ,	num_class_embeds=1_00     ,	)
         return model







    @property
    def  lowercase_							(				self      )							->	str:
         torch.manual_seed(0      )
         __lowerCamelCase  :      Optional[Any]														=						AutoencoderKL(
             block_out_channels=[32, 32, 64]     ,	in_channels=3     ,	out_channels=3     ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']     ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']     ,	latent_channels=4     ,	)
         return model







    @property
    def  lowercase_							(				self      )							->	List[Any]:
         torch.manual_seed(0      )
         __lowerCamelCase  :      List[str]														=						CLIPTextConfig(
             bos_token_id=0     ,	eos_token_id=2     ,	hidden_size=32     ,	intermediate_size=37     ,	layer_norm_eps=1E-05     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	pad_token_id=1     ,	vocab_size=10_00     ,	hidden_act='gelu'     ,	projection_dim=5_12     ,	)
         return CLIPTextModel(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Any														=						'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase  :      Optional[Any]														=						self.dummy_cond_unet_upscale
         __lowerCamelCase  :      Dict														=						DDPMScheduler()
         __lowerCamelCase  :      List[str]														=						DDIMScheduler(prediction_type='v_prediction'      )
         __lowerCamelCase  :      str														=						self.dummy_vae
         __lowerCamelCase  :      List[Any]														=						self.dummy_text_encoder
         __lowerCamelCase  :      int														=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

         __lowerCamelCase  :      List[str]														=						self.dummy_image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
         __lowerCamelCase  :      Dict														=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_      )      ).convert('RGB'      ).resize((64, 64)      )

         # make sure here that pndm scheduler skips prk
         __lowerCamelCase  :      Any														=						StableDiffusionUpscalePipeline(
             unet=SCREAMING_SNAKE_CASE_     ,	low_res_scheduler=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_     ,	vae=SCREAMING_SNAKE_CASE_     ,	text_encoder=SCREAMING_SNAKE_CASE_     ,	tokenizer=SCREAMING_SNAKE_CASE_     ,	max_noise_level=3_50     ,	)
         __lowerCamelCase  :      Optional[Any]														=						sd_pipe.to(SCREAMING_SNAKE_CASE_      )
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      List[str]														=						'A painting of a squirrel eating a burger'
         __lowerCamelCase  :      Union[str, Any]														=						torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(0      )
         __lowerCamelCase  :      List[Any]														=						sd_pipe(
             [prompt]     ,	image=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	guidance_scale=6.0     ,	noise_level=20     ,	num_inference_steps=2     ,	output_type='np'     ,	)

         __lowerCamelCase  :      Optional[Any]														=						output.images

         __lowerCamelCase  :      Union[str, Any]														=						torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(0      )
         __lowerCamelCase  :      int														=						sd_pipe(
             [prompt]     ,	image=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	guidance_scale=6.0     ,	noise_level=20     ,	num_inference_steps=2     ,	output_type='np'     ,	return_dict=SCREAMING_SNAKE_CASE_     ,	)[0]

         __lowerCamelCase  :      Tuple														=						image[0, -3:, -3:, -1]
         __lowerCamelCase  :      Optional[Any]														=						image_from_tuple[0, -3:, -3:, -1]

         __lowerCamelCase  :      Dict														=						low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         __lowerCamelCase  :      Optional[int]														=						np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]      )

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







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      List[str]														=						'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase  :      List[Any]														=						self.dummy_cond_unet_upscale
         __lowerCamelCase  :      List[Any]														=						DDPMScheduler()
         __lowerCamelCase  :      Union[str, Any]														=						DDIMScheduler(prediction_type='v_prediction'      )
         __lowerCamelCase  :      Optional[Any]														=						self.dummy_vae
         __lowerCamelCase  :      str														=						self.dummy_text_encoder
         __lowerCamelCase  :      Dict														=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

         __lowerCamelCase  :      str														=						self.dummy_image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
         __lowerCamelCase  :      Optional[Any]														=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_      )      ).convert('RGB'      ).resize((64, 64)      )

         # make sure here that pndm scheduler skips prk
         __lowerCamelCase  :      str														=						StableDiffusionUpscalePipeline(
             unet=SCREAMING_SNAKE_CASE_     ,	low_res_scheduler=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_     ,	vae=SCREAMING_SNAKE_CASE_     ,	text_encoder=SCREAMING_SNAKE_CASE_     ,	tokenizer=SCREAMING_SNAKE_CASE_     ,	max_noise_level=3_50     ,	)
         __lowerCamelCase  :      str														=						sd_pipe.to(SCREAMING_SNAKE_CASE_      )
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[Any]														=						'A painting of a squirrel eating a burger'
         __lowerCamelCase  :      List[Any]														=						sd_pipe(
             2 * [prompt]     ,	image=2 * [low_res_image]     ,	guidance_scale=6.0     ,	noise_level=20     ,	num_inference_steps=2     ,	output_type='np'     ,	)
         __lowerCamelCase  :      Any														=						output.images
         assert image.shape[0] == 2

         __lowerCamelCase  :      List[str]														=						torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(0      )
         __lowerCamelCase  :      List[Any]														=						sd_pipe(
             [prompt]     ,	image=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	num_images_per_prompt=2     ,	guidance_scale=6.0     ,	noise_level=20     ,	num_inference_steps=2     ,	output_type='np'     ,	)
         __lowerCamelCase  :      Union[str, Any]														=						output.images
         assert image.shape[0] == 2







    @unittest.skipIf(torch_device != 'cuda'     ,	'This test requires a GPU'      )
    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      int														=						self.dummy_cond_unet_upscale
         __lowerCamelCase  :      Union[str, Any]														=						DDPMScheduler()
         __lowerCamelCase  :      Union[str, Any]														=						DDIMScheduler(prediction_type='v_prediction'      )
         __lowerCamelCase  :      Any														=						self.dummy_vae
         __lowerCamelCase  :      str														=						self.dummy_text_encoder
         __lowerCamelCase  :      Optional[Any]														=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

         __lowerCamelCase  :      Tuple														=						self.dummy_image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
         __lowerCamelCase  :      Union[str, Any]														=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_      )      ).convert('RGB'      ).resize((64, 64)      )

         # put models in fp16, except vae as it overflows in fp16
         __lowerCamelCase  :      str														=						unet.half()
         __lowerCamelCase  :      int														=						text_encoder.half()

         # make sure here that pndm scheduler skips prk
         __lowerCamelCase  :      int														=						StableDiffusionUpscalePipeline(
             unet=SCREAMING_SNAKE_CASE_     ,	low_res_scheduler=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_     ,	vae=SCREAMING_SNAKE_CASE_     ,	text_encoder=SCREAMING_SNAKE_CASE_     ,	tokenizer=SCREAMING_SNAKE_CASE_     ,	max_noise_level=3_50     ,	)
         __lowerCamelCase  :      List[Any]														=						sd_pipe.to(SCREAMING_SNAKE_CASE_      )
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						'A painting of a squirrel eating a burger'
         __lowerCamelCase  :      Optional[int]														=						torch.manual_seed(0      )
         __lowerCamelCase  :      Dict														=						sd_pipe(
             [prompt]     ,	image=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	num_inference_steps=2     ,	output_type='np'     ,	).images

         __lowerCamelCase  :      int														=						low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)







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





    """simple docstring"""






    def  lowercase_							(				self      )							->	List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      str														=						load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/sd2-upscale/low_res_cat.png'      )
         __lowerCamelCase  :      Any														=						load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
             '/upsampled_cat.npy'      )

         __lowerCamelCase  :      Optional[Any]														=						'stabilityai/stable-diffusion-x4-upscaler'
         __lowerCamelCase  :      List[str]														=						StableDiffusionUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE_      )
         pipe.to(SCREAMING_SNAKE_CASE_      )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )
         pipe.enable_attention_slicing()

         __lowerCamelCase  :      Tuple														=						'a cat sitting on a park bench'

         __lowerCamelCase  :      Dict														=						torch.manual_seed(0      )
         __lowerCamelCase  :      Tuple														=						pipe(
             prompt=SCREAMING_SNAKE_CASE_     ,	image=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	output_type='np'     ,	)
         __lowerCamelCase  :      Union[str, Any]														=						output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image      ).max() < 1E-3







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase  :      List[Any]														=						load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/sd2-upscale/low_res_cat.png'      )
         __lowerCamelCase  :      int														=						load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
             '/upsampled_cat_fp16.npy'      )

         __lowerCamelCase  :      Optional[int]														=						'stabilityai/stable-diffusion-x4-upscaler'
         __lowerCamelCase  :      Dict														=						StableDiffusionUpscalePipeline.from_pretrained(
             SCREAMING_SNAKE_CASE_     ,	torch_dtype=torch.floataa     ,	)
         pipe.to(SCREAMING_SNAKE_CASE_      )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )
         pipe.enable_attention_slicing()

         __lowerCamelCase  :      str														=						'a cat sitting on a park bench'

         __lowerCamelCase  :      Dict														=						torch.manual_seed(0      )
         __lowerCamelCase  :      int														=						pipe(
             prompt=SCREAMING_SNAKE_CASE_     ,	image=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	output_type='np'     ,	)
         __lowerCamelCase  :      List[str]														=						output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image      ).max() < 5E-1







    def  lowercase_							(				self      )							->	Optional[Any]:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase  :      List[Any]														=						load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/sd2-upscale/low_res_cat.png'      )

         __lowerCamelCase  :      Dict														=						'stabilityai/stable-diffusion-x4-upscaler'
         __lowerCamelCase  :      Any														=						StableDiffusionUpscalePipeline.from_pretrained(
             SCREAMING_SNAKE_CASE_     ,	torch_dtype=torch.floataa     ,	)
         pipe.to(SCREAMING_SNAKE_CASE_      )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )
         pipe.enable_attention_slicing(1      )
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase  :      Union[str, Any]														=						'a cat sitting on a park bench'

         __lowerCamelCase  :      Union[str, Any]														=						torch.manual_seed(0      )
         __lowerCamelCase  :      List[Any]														=						pipe(
             prompt=SCREAMING_SNAKE_CASE_     ,	image=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	num_inference_steps=5     ,	output_type='np'     ,	)

         __lowerCamelCase  :      List[Any]														=						torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 10**9








"""simple docstring"""
import warnings

from ..trainer import Trainer
from ..utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_=None    ,				**snake_case_				)       ->  Optional[Any]:
													warnings.warn(
													    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
													    """instead."""    ,				snake_case_    ,				)
													super().__init__(args=snake_case_    ,				**snake_case_				)
