
"""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							):




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




      @property
      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					1
          UpperCAmelCase									=					3
          UpperCAmelCase									=					(3_2, 3_2)

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




      @property
      def     a_      (							self							)						->					Optional[Any]:
          torch.manual_seed(0							)
          UpperCAmelCase									=					UNetaDConditionModel(
              block_out_channels=(3_2, 3_2, 6_4)   ,       layers_per_block=2   ,       sample_size=3_2   ,       in_channels=7   ,       out_channels=4   ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')   ,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')   ,       cross_attention_dim=3_2   ,       attention_head_dim=8   ,       use_linear_projection=lowercase_   ,       only_cross_attention=(True, True, False)   ,       num_class_embeds=1_0_0   ,       )
          return model




      @property
      def     a_      (							self							)						->					Optional[Any]:
          torch.manual_seed(0							)
          UpperCAmelCase									=					AutoencoderKL(
              block_out_channels=[3_2, 3_2, 6_4]   ,       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     a_      (							self							)						->					Tuple:
          torch.manual_seed(0							)
          UpperCAmelCase									=					CLIPTextConfig(
              bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=3_2   ,       intermediate_size=3_7   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1_0_0_0   ,       hidden_act='gelu'   ,       projection_dim=5_1_2   ,       )
          return CLIPTextModel(lowercase_							)




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

          UpperCAmelCase									=					self.dummy_image.cpu().permute(0   ,       2   ,       3   ,       1							)[0]
          UpperCAmelCase									=					Image.fromarray(np.uinta(lowercase_							)							).convert('RGB'							).resize((6_4, 6_4)							)

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase									=					StableDiffusionUpscalePipeline(
              unet=lowercase_   ,       low_res_scheduler=lowercase_   ,       scheduler=lowercase_   ,       vae=lowercase_   ,       text_encoder=lowercase_   ,       tokenizer=lowercase_   ,       max_noise_level=3_5_0   ,       )
          UpperCAmelCase									=					sd_pipe.to(lowercase_							)
          sd_pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					'A painting of a squirrel eating a burger'
          UpperCAmelCase									=					torch.Generator(device=lowercase_							).manual_seed(0							)
          UpperCAmelCase									=					sd_pipe(
              [prompt]   ,       image=lowercase_   ,       generator=lowercase_   ,       guidance_scale=6.0   ,       noise_level=2_0   ,       num_inference_steps=2   ,       output_type='np'   ,       )

          UpperCAmelCase									=					output.images

          UpperCAmelCase									=					torch.Generator(device=lowercase_							).manual_seed(0							)
          UpperCAmelCase									=					sd_pipe(
              [prompt]   ,       image=lowercase_   ,       generator=lowercase_   ,       guidance_scale=6.0   ,       noise_level=2_0   ,       num_inference_steps=2   ,       output_type='np'   ,       return_dict=lowercase_   ,       )[0]

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

          UpperCAmelCase									=					low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)
          UpperCAmelCase									=					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     a_      (							self							)						->					int:
          UpperCAmelCase									=					'cpu'  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase									=					self.dummy_cond_unet_upscale
          UpperCAmelCase									=					DDPMScheduler()
          UpperCAmelCase									=					DDIMScheduler(prediction_type='v_prediction'							)
          UpperCAmelCase									=					self.dummy_vae
          UpperCAmelCase									=					self.dummy_text_encoder
          UpperCAmelCase									=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

          UpperCAmelCase									=					self.dummy_image.cpu().permute(0   ,       2   ,       3   ,       1							)[0]
          UpperCAmelCase									=					Image.fromarray(np.uinta(lowercase_							)							).convert('RGB'							).resize((6_4, 6_4)							)

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase									=					StableDiffusionUpscalePipeline(
              unet=lowercase_   ,       low_res_scheduler=lowercase_   ,       scheduler=lowercase_   ,       vae=lowercase_   ,       text_encoder=lowercase_   ,       tokenizer=lowercase_   ,       max_noise_level=3_5_0   ,       )
          UpperCAmelCase									=					sd_pipe.to(lowercase_							)
          sd_pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					'A painting of a squirrel eating a burger'
          UpperCAmelCase									=					sd_pipe(
              2 * [prompt]   ,       image=2 * [low_res_image]   ,       guidance_scale=6.0   ,       noise_level=2_0   ,       num_inference_steps=2   ,       output_type='np'   ,       )
          UpperCAmelCase									=					output.images
          assert image.shape[0] == 2

          UpperCAmelCase									=					torch.Generator(device=lowercase_							).manual_seed(0							)
          UpperCAmelCase									=					sd_pipe(
              [prompt]   ,       image=lowercase_   ,       generator=lowercase_   ,       num_images_per_prompt=2   ,       guidance_scale=6.0   ,       noise_level=2_0   ,       num_inference_steps=2   ,       output_type='np'   ,       )
          UpperCAmelCase									=					output.images
          assert image.shape[0] == 2




      @unittest.skipIf(torch_device != 'cuda'   ,       'This test requires a GPU'							)
      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.dummy_cond_unet_upscale
          UpperCAmelCase									=					DDPMScheduler()
          UpperCAmelCase									=					DDIMScheduler(prediction_type='v_prediction'							)
          UpperCAmelCase									=					self.dummy_vae
          UpperCAmelCase									=					self.dummy_text_encoder
          UpperCAmelCase									=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

          UpperCAmelCase									=					self.dummy_image.cpu().permute(0   ,       2   ,       3   ,       1							)[0]
          UpperCAmelCase									=					Image.fromarray(np.uinta(lowercase_							)							).convert('RGB'							).resize((6_4, 6_4)							)

          # put models in fp16, except vae as it overflows in fp16
          UpperCAmelCase									=					unet.half()
          UpperCAmelCase									=					text_encoder.half()

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase									=					StableDiffusionUpscalePipeline(
              unet=lowercase_   ,       low_res_scheduler=lowercase_   ,       scheduler=lowercase_   ,       vae=lowercase_   ,       text_encoder=lowercase_   ,       tokenizer=lowercase_   ,       max_noise_level=3_5_0   ,       )
          UpperCAmelCase									=					sd_pipe.to(lowercase_							)
          sd_pipe.set_progress_bar_config(disable=lowercase_							)

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

          UpperCAmelCase									=					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							):




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




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

          UpperCAmelCase									=					'stabilityai/stable-diffusion-x4-upscaler'
          UpperCAmelCase									=					StableDiffusionUpscalePipeline.from_pretrained(lowercase_							)
          pipe.to(lowercase_							)
          pipe.set_progress_bar_config(disable=lowercase_							)
          pipe.enable_attention_slicing()

          UpperCAmelCase									=					'a cat sitting on a park bench'

          UpperCAmelCase									=					torch.manual_seed(0							)
          UpperCAmelCase									=					pipe(
              prompt=lowercase_   ,       image=lowercase_   ,       generator=lowercase_   ,       output_type='np'   ,       )
          UpperCAmelCase									=					output.images[0]

          assert image.shape == (5_1_2, 5_1_2, 3)
          assert np.abs(expected_image - image							).max() < 1E-3




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

          UpperCAmelCase									=					'stabilityai/stable-diffusion-x4-upscaler'
          UpperCAmelCase									=					StableDiffusionUpscalePipeline.from_pretrained(
              lowercase_   ,       torch_dtype=torch.floataa   ,       )
          pipe.to(lowercase_							)
          pipe.set_progress_bar_config(disable=lowercase_							)
          pipe.enable_attention_slicing()

          UpperCAmelCase									=					'a cat sitting on a park bench'

          UpperCAmelCase									=					torch.manual_seed(0							)
          UpperCAmelCase									=					pipe(
              prompt=lowercase_   ,       image=lowercase_   ,       generator=lowercase_   ,       output_type='np'   ,       )
          UpperCAmelCase									=					output.images[0]

          assert image.shape == (5_1_2, 5_1_2, 3)
          assert np.abs(expected_image - image							).max() < 5E-1




      def     a_      (							self							)						->					Any:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

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

          UpperCAmelCase									=					'stabilityai/stable-diffusion-x4-upscaler'
          UpperCAmelCase									=					StableDiffusionUpscalePipeline.from_pretrained(
              lowercase_   ,       torch_dtype=torch.floataa   ,       )
          pipe.to(lowercase_							)
          pipe.set_progress_bar_config(disable=lowercase_							)
          pipe.enable_attention_slicing(1							)
          pipe.enable_sequential_cpu_offload()

          UpperCAmelCase									=					'a cat sitting on a park bench'

          UpperCAmelCase									=					torch.manual_seed(0							)
          UpperCAmelCase									=					pipe(
              prompt=lowercase_   ,       image=lowercase_   ,       generator=lowercase_   ,       num_inference_steps=5   ,       output_type='np'   ,       )

          UpperCAmelCase									=					torch.cuda.max_memory_allocated()
          # make sure that less than 2.9 GB is allocated
          assert mem_bytes < 2.9 * 1_0**9





"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"encoder-decoder"
      __SCREAMING_SNAKE_CASE   :							Any							      =	True




      def __init__(							self   ,       **lowercase_							)						->					str:
          super().__init__(**lowercase_							)
          assert (
              "encoder" in kwargs and "decoder" in kwargs
          ), "Config has to be initialized with encoder and decoder config"
          UpperCAmelCase									=					kwargs.pop('encoder'							)
          UpperCAmelCase									=					encoder_config.pop('model_type'							)
          UpperCAmelCase									=					kwargs.pop('decoder'							)
          UpperCAmelCase									=					decoder_config.pop('model_type'							)

          from ..auto.configuration_auto import AutoConfig

          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					True




      @classmethod
      def     a_      (							cls   ,       lowercase_   ,       lowercase_   ,       **lowercase_							)						->					PretrainedConfig:
          logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'							)
          UpperCAmelCase									=					True
          UpperCAmelCase									=					True

          return cls(encoder=encoder_config.to_dict()   ,       decoder=decoder_config.to_dict()   ,       **lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					copy.deepcopy(self.__dict__							)
          UpperCAmelCase									=					self.encoder.to_dict()
          UpperCAmelCase									=					self.decoder.to_dict()
          UpperCAmelCase									=					self.__class__.model_type
          return output



