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      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







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







  @property
  def       __A   (							self       :				Dict      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     1
        SCREAMING_SNAKE_CASE_      =     3
        SCREAMING_SNAKE_CASE_      =     (32, 32)

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







  @property
  def       __A   (							self       :				List[str]      )     ->		List[Any]:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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=__magic_name__     ,   only_cross_attention=(True, True, False)     ,   num_class_embeds=100     ,   )
        return model







  @property
  def       __A   (							self       :				Dict      )     ->		Optional[Any]:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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       __A   (							self       :				int      )     ->		List[Any]:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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=1_000     ,   hidden_act="gelu"     ,   projection_dim=512     ,   )
        return CLIPTextModel(__magic_name__      )







  def       __A   (							self       :				Any      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     "cpu"  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_      =     self.dummy_cond_unet_upscale
        SCREAMING_SNAKE_CASE_      =     DDPMScheduler()
        SCREAMING_SNAKE_CASE_      =     DDIMScheduler(prediction_type="v_prediction"      )
        SCREAMING_SNAKE_CASE_      =     self.dummy_vae
        SCREAMING_SNAKE_CASE_      =     self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_      =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

        SCREAMING_SNAKE_CASE_      =     self.dummy_image.cpu().permute(0     ,   2     ,   3     ,   1      )[0]
        SCREAMING_SNAKE_CASE_      =     Image.fromarray(np.uinta(__magic_name__      )      ).convert("RGB"      ).resize((64, 64)      )

        # make sure here that pndm scheduler skips prk
        SCREAMING_SNAKE_CASE_      =     StableDiffusionUpscalePipeline(
            unet=__magic_name__     ,   low_res_scheduler=__magic_name__     ,   scheduler=__magic_name__     ,   vae=__magic_name__     ,   text_encoder=__magic_name__     ,   tokenizer=__magic_name__     ,   max_noise_level=350     ,   )
        SCREAMING_SNAKE_CASE_      =     sd_pipe.to(__magic_name__      )
        sd_pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "A painting of a squirrel eating a burger"
        SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   image=__magic_name__     ,   generator=__magic_name__     ,   guidance_scale=6.0     ,   noise_level=20     ,   num_inference_steps=2     ,   output_type="np"     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   image=__magic_name__     ,   generator=__magic_name__     ,   guidance_scale=6.0     ,   noise_level=20     ,   num_inference_steps=2     ,   output_type="np"     ,   return_dict=__magic_name__     ,   )[0]

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

        SCREAMING_SNAKE_CASE_      =     low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)
        SCREAMING_SNAKE_CASE_      =     np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]      )

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

        SCREAMING_SNAKE_CASE_      =     self.dummy_image.cpu().permute(0     ,   2     ,   3     ,   1      )[0]
        SCREAMING_SNAKE_CASE_      =     Image.fromarray(np.uinta(__magic_name__      )      ).convert("RGB"      ).resize((64, 64)      )

        # make sure here that pndm scheduler skips prk
        SCREAMING_SNAKE_CASE_      =     StableDiffusionUpscalePipeline(
            unet=__magic_name__     ,   low_res_scheduler=__magic_name__     ,   scheduler=__magic_name__     ,   vae=__magic_name__     ,   text_encoder=__magic_name__     ,   tokenizer=__magic_name__     ,   max_noise_level=350     ,   )
        SCREAMING_SNAKE_CASE_      =     sd_pipe.to(__magic_name__      )
        sd_pipe.set_progress_bar_config(disable=__magic_name__      )

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

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   image=__magic_name__     ,   generator=__magic_name__     ,   num_images_per_prompt=2     ,   guidance_scale=6.0     ,   noise_level=20     ,   num_inference_steps=2     ,   output_type="np"     ,   )
        SCREAMING_SNAKE_CASE_      =     output.images
        assert image.shape[0] == 2







  @unittest.skipIf(torch_device != "cuda"     ,   "This test requires a GPU"      )
  def       __A   (							self       :				List[str]      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     self.dummy_cond_unet_upscale
        SCREAMING_SNAKE_CASE_      =     DDPMScheduler()
        SCREAMING_SNAKE_CASE_      =     DDIMScheduler(prediction_type="v_prediction"      )
        SCREAMING_SNAKE_CASE_      =     self.dummy_vae
        SCREAMING_SNAKE_CASE_      =     self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_      =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

        SCREAMING_SNAKE_CASE_      =     self.dummy_image.cpu().permute(0     ,   2     ,   3     ,   1      )[0]
        SCREAMING_SNAKE_CASE_      =     Image.fromarray(np.uinta(__magic_name__      )      ).convert("RGB"      ).resize((64, 64)      )

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

        # make sure here that pndm scheduler skips prk
        SCREAMING_SNAKE_CASE_      =     StableDiffusionUpscalePipeline(
            unet=__magic_name__     ,   low_res_scheduler=__magic_name__     ,   scheduler=__magic_name__     ,   vae=__magic_name__     ,   text_encoder=__magic_name__     ,   tokenizer=__magic_name__     ,   max_noise_level=350     ,   )
        SCREAMING_SNAKE_CASE_      =     sd_pipe.to(__magic_name__      )
        sd_pipe.set_progress_bar_config(disable=__magic_name__      )

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

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



@slow
@require_torch_gpu
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Union[str, Any]      )     ->		Tuple:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def       __A   (							self       :				List[str]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-upscale/low_res_cat.png"      )
        SCREAMING_SNAKE_CASE_      =     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
            "/upsampled_cat.npy"      )

        SCREAMING_SNAKE_CASE_      =     "stabilityai/stable-diffusion-x4-upscaler"
        SCREAMING_SNAKE_CASE_      =     StableDiffusionUpscalePipeline.from_pretrained(__magic_name__      )
        pipe.to(__magic_name__      )
        pipe.set_progress_bar_config(disable=__magic_name__      )
        pipe.enable_attention_slicing()

        SCREAMING_SNAKE_CASE_      =     "a cat sitting on a park bench"

        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     pipe(
            prompt=__magic_name__     ,   image=__magic_name__     ,   generator=__magic_name__     ,   output_type="np"     ,   )
        SCREAMING_SNAKE_CASE_      =     output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image      ).max() < 1e-3







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

        SCREAMING_SNAKE_CASE_      =     "stabilityai/stable-diffusion-x4-upscaler"
        SCREAMING_SNAKE_CASE_      =     StableDiffusionUpscalePipeline.from_pretrained(
            __magic_name__     ,   torch_dtype=torch.floataa     ,   )
        pipe.to(__magic_name__      )
        pipe.set_progress_bar_config(disable=__magic_name__      )
        pipe.enable_attention_slicing()

        SCREAMING_SNAKE_CASE_      =     "a cat sitting on a park bench"

        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     pipe(
            prompt=__magic_name__     ,   image=__magic_name__     ,   generator=__magic_name__     ,   output_type="np"     ,   )
        SCREAMING_SNAKE_CASE_      =     output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image      ).max() < 5e-1







  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

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

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

        SCREAMING_SNAKE_CASE_      =     "a cat sitting on a park bench"

        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     pipe(
            prompt=__magic_name__     ,   image=__magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=5     ,   output_type="np"     ,   )

        SCREAMING_SNAKE_CASE_      =     torch.cuda.max_memory_allocated()
        # make sure that less than 2.9 GB is allocated
        assert mem_bytes < 2.9 * 10**9



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  42
  lowerCamelCase__   =  42


if is_transformers_available() and is_torch_available():
      from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


