"""simple docstring"""



import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class 				snake_case__     (   snake_case_,		snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			str           =		StableUnCLIPPipeline
 _snake_case   :			Optional[int]           =		TEXT_TO_IMAGE_PARAMS
 _snake_case   :			List[Any]           =		TEXT_TO_IMAGE_BATCH_PARAMS
 _snake_case   :			List[Any]           =		TEXT_TO_IMAGE_IMAGE_PARAMS
 _snake_case   :			int           =		TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 _snake_case   :			Optional[int]           =		False


 def 		a__ (				self		):
        __a     				=  32
        __a     				=  embedder_hidden_size

        # prior components

        torch.manual_seed(0		)
        __a     				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

        torch.manual_seed(0		)
        __a     				=  CLIPTextModelWithProjection(
            CLIPTextConfig(
                bos_token_id=0  ,						eos_token_id=2  ,						hidden_size=lowerCamelCase  ,						projection_dim=lowerCamelCase  ,						intermediate_size=37  ,						layer_norm_eps=1E-05  ,						num_attention_heads=4  ,						num_hidden_layers=5  ,						pad_token_id=1  ,						vocab_size=1000  ,						)		)

        torch.manual_seed(0		)
        __a     				=  PriorTransformer(
            num_attention_heads=2  ,						attention_head_dim=12  ,						embedding_dim=lowerCamelCase  ,						num_layers=1  ,						)

        torch.manual_seed(0		)
        __a     				=  DDPMScheduler(
            variance_type="fixed_small_log"  ,						prediction_type="sample"  ,						num_train_timesteps=1000  ,						clip_sample=lowerCamelCase  ,						clip_sample_range=5.0  ,						beta_schedule="squaredcos_cap_v2"  ,						)

        # regular denoising components

        torch.manual_seed(0		)
        __a     				=  StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase		)
        __a     				=  DDPMScheduler(beta_schedule="squaredcos_cap_v2"		)

        torch.manual_seed(0		)
        __a     				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

        torch.manual_seed(0		)
        __a     				=  CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0  ,						eos_token_id=2  ,						hidden_size=lowerCamelCase  ,						projection_dim=32  ,						intermediate_size=37  ,						layer_norm_eps=1E-05  ,						num_attention_heads=4  ,						num_hidden_layers=5  ,						pad_token_id=1  ,						vocab_size=1000  ,						)		)

        torch.manual_seed(0		)
        __a     				=  UNetaDConditionModel(
            sample_size=32  ,						in_channels=4  ,						out_channels=4  ,						down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")  ,						up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")  ,						block_out_channels=(32, 64)  ,						attention_head_dim=(2, 4)  ,						class_embed_type="projection"  ,						projection_class_embeddings_input_dim=embedder_projection_dim * 2  ,						cross_attention_dim=lowerCamelCase  ,						layers_per_block=1  ,						upcast_attention=lowerCamelCase  ,						use_linear_projection=lowerCamelCase  ,						)

        torch.manual_seed(0		)
        __a     				=  DDIMScheduler(
            beta_schedule="scaled_linear"  ,						beta_start=0.0_0085  ,						beta_end=0.012  ,						prediction_type="v_prediction"  ,						set_alpha_to_one=lowerCamelCase  ,						steps_offset=1  ,						)

        torch.manual_seed(0		)
        __a     				=  AutoencoderKL()

        __a     				=  {
            # prior components
            "prior_tokenizer": prior_tokenizer,
            "prior_text_encoder": prior_text_encoder,
            "prior": prior,
            "prior_scheduler": prior_scheduler,
            # image noising components
            "image_normalizer": image_normalizer,
            "image_noising_scheduler": image_noising_scheduler,
            # regular denoising components
            "tokenizer": tokenizer,
            "text_encoder": text_encoder,
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
        }

        return components


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=0		):
        if str(lowerCamelCase		).startswith("mps"		):
               __a     				=  torch.manual_seed(lowerCamelCase		)
        else:
               __a     				=  torch.Generator(device=lowerCamelCase		).manual_seed(lowerCamelCase		)
        __a     				=  {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "prior_num_inference_steps": 2,
            "output_type": "numpy",
        }
        return inputs


 def 		a__ (				self		):
        __a     				=  torch_device == "cpu"

        self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase		)






 def 		a__ (				self		):
        __a     				=  torch_device in ["cpu", "mps"]

        self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase		)






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


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


 def 		a__ (				self		):
        __a     				=  load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"		)

        __a     				=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"  ,						torch_dtype=torch.floataa		)
        pipe.to(lowerCamelCase		)
        pipe.set_progress_bar_config(disable=lowerCamelCase		)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __a     				=  torch.Generator(device="cpu"		).manual_seed(0		)
        __a     				=  pipe("anime turle"  ,						generator=lowerCamelCase  ,						output_type="np"		)

        __a     				=  output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(lowerCamelCase  ,						lowerCamelCase		)






 def 		a__ (				self		):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __a     				=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"  ,						torch_dtype=torch.floataa		)
        __a     				=  pipe.to(lowerCamelCase		)
        pipe.set_progress_bar_config(disable=lowerCamelCase		)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __a     				=  pipe(
            "anime turtle"  ,						prior_num_inference_steps=2  ,						num_inference_steps=2  ,						output_type="np"  ,						)

        __a     				=  torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			UNetaDModel
 _snake_case   :			KarrasVeScheduler


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase  ,						scheduler=lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase = 1  ,						lowerCamelCase = 50  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  self.unet.config.sample_size
        __a     				=  (batch_size, 3, img_size, img_size)

        __a     				=  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        __a     				=  randn_tensor(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase		)

        for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               __a     				=  self.scheduler.schedule[t]
               __a     				=  self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               __a				, __a     				=  self.scheduler.add_noise_to_input(lowerCamelCase  ,						lowerCamelCase  ,						generator=lowerCamelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               __a     				=  (sigma_hat / 2) * model((sample_hat + 1) / 2  ,						sigma_hat / 2		).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               if sigma_prev != 0:
                      # 6. Apply 2nd order correction
                      # The model inputs and output are adjusted by following eq. (213) in [1].
                      __a     				=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,						sigma_prev / 2		).sample
                      __a     				=  self.scheduler.step_correct(
                          lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						step_output.prev_sample  ,						step_output["derivative"]  ,						)
               __a     				=  step_output.prev_sample

        __a     				=  (sample / 2 + 0.5).clamp(0  ,						1		)
        __a     				=  sample.cpu().permute(0  ,						2  ,						3  ,						1		).numpy()
        if output_type == "pil":
               __a     				=  self.numpy_to_pil(lowerCamelCase		)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase		)
