"""simple docstring"""



import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 				snake_case__     (   snake_case_,		unittest.TestCase   ):
 _snake_case   :			List[str]           =		KandinskyVaaPriorPipeline
 _snake_case   :			int           =		["""prompt"""]
 _snake_case   :			List[Any]           =		["""prompt""", """negative_prompt"""]
 _snake_case   :			str           =		[
     """num_images_per_prompt""",
     """generator""",
     """num_inference_steps""",
     """latents""",
     """negative_prompt""",
     """guidance_scale""",
     """output_type""",
     """return_dict""",
 ]
 _snake_case   :			Union[str, Any]           =		False


 @property
 def 		a__ (				self		):
        return 32


 @property
 def 		a__ (				self		):
        return 32


 @property
 def 		a__ (				self		):
        return self.time_input_dim


 @property
 def 		a__ (				self		):
        return self.time_input_dim * 4


 @property
 def 		a__ (				self		):
        return 100


 @property
 def 		a__ (				self		):
        __a     				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
        return tokenizer


 @property
 def 		a__ (				self		):
        torch.manual_seed(0		)
        __a     				=  CLIPTextConfig(
            bos_token_id=0  ,						eos_token_id=2  ,						hidden_size=self.text_embedder_hidden_size  ,						projection_dim=self.text_embedder_hidden_size  ,						intermediate_size=37  ,						layer_norm_eps=1E-05  ,						num_attention_heads=4  ,						num_hidden_layers=5  ,						pad_token_id=1  ,						vocab_size=1000  ,						)
        return CLIPTextModelWithProjection(lowerCamelCase		)


 @property
 def 		a__ (				self		):
        torch.manual_seed(0		)

        __a     				=  {
            "num_attention_heads": 2,
            "attention_head_dim": 12,
            "embedding_dim": self.text_embedder_hidden_size,
            "num_layers": 1,
        }

        __a     				=  PriorTransformer(**lowerCamelCase		)
        # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
        __a     				=  nn.Parameter(torch.ones(model.clip_std.shape		)		)
        return model


 @property
 def 		a__ (				self		):
        torch.manual_seed(0		)
        __a     				=  CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size  ,						image_size=224  ,						projection_dim=self.text_embedder_hidden_size  ,						intermediate_size=37  ,						num_attention_heads=4  ,						num_channels=3  ,						num_hidden_layers=5  ,						patch_size=14  ,						)

        __a     				=  CLIPVisionModelWithProjection(lowerCamelCase		)
        return model


 @property
 def 		a__ (				self		):
        __a     				=  CLIPImageProcessor(
            crop_size=224  ,						do_center_crop=lowerCamelCase  ,						do_normalize=lowerCamelCase  ,						do_resize=lowerCamelCase  ,						image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,						image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,						resample=3  ,						size=224  ,						)

        return image_processor


 def 		a__ (				self		):
        __a     				=  self.dummy_prior
        __a     				=  self.dummy_image_encoder
        __a     				=  self.dummy_text_encoder
        __a     				=  self.dummy_tokenizer
        __a     				=  self.dummy_image_processor

        __a     				=  UnCLIPScheduler(
            variance_type="fixed_small_log"  ,						prediction_type="sample"  ,						num_train_timesteps=1000  ,						clip_sample=lowerCamelCase  ,						clip_sample_range=10.0  ,						)

        __a     				=  {
            "prior": prior,
            "image_encoder": image_encoder,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "scheduler": scheduler,
            "image_processor": image_processor,
        }

        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": "horse",
            "generator": generator,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs


 def 		a__ (				self		):
        __a     				=  "cpu"

        __a     				=  self.get_dummy_components()

        __a     				=  self.pipeline_class(**lowerCamelCase		)
        __a     				=  pipe.to(lowerCamelCase		)

        pipe.set_progress_bar_config(disable=lowerCamelCase		)

        __a     				=  pipe(**self.get_dummy_inputs(lowerCamelCase		)		)
        __a     				=  output.image_embeds

        __a     				=  pipe(
            **self.get_dummy_inputs(lowerCamelCase		)  ,						return_dict=lowerCamelCase  ,						)[0]

        __a     				=  image[0, -10:]
        __a     				=  image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        __a     				=  np.array(
            [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]		)

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


 @skip_mps
 def 		a__ (				self		):
        __a     				=  torch_device == "cpu"
        __a     				=  True
        __a     				=  False

        self._test_inference_batch_single_identical(
            test_max_difference=lowerCamelCase  ,						relax_max_difference=lowerCamelCase  ,						test_mean_pixel_difference=lowerCamelCase  ,						)






 @skip_mps
 def 		a__ (				self		):
        __a     				=  torch_device == "cpu"
        __a     				=  False

        self._test_attention_slicing_forward_pass(
            test_max_difference=lowerCamelCase  ,						test_mean_pixel_difference=lowerCamelCase  ,						)

"""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		)
