"""simple docstring"""



import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


SCREAMING_SNAKE_CASE__:Any			=	logging.get_logger(__name__)  # pylint: disable=invalid-name






class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=768		):
        super().__init__(lowerCamelCase		)
        __a     				=  proj_size

        __a     				=  CLIPVisionModel(lowerCamelCase		)
        __a     				=  PaintByExampleMapper(lowerCamelCase		)
        __a     				=  nn.LayerNorm(config.hidden_size		)
        __a     				=  nn.Linear(config.hidden_size  ,						self.proj_size		)

        # uncondition for scaling
        __a     				=  nn.Parameter(torch.randn((1, 1, self.proj_size)		)		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=False		):
        __a     				=  self.model(pixel_values=lowerCamelCase		)
        __a     				=  clip_output.pooler_output
        __a     				=  self.mapper(latent_states[:, None]		)
        __a     				=  self.final_layer_norm(lowerCamelCase		)
        __a     				=  self.proj_out(lowerCamelCase		)
        if return_uncond_vector:
               return latent_states, self.uncond_vector

        return latent_states






class 				snake_case__     (   nn.Module   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__()
        __a     				=  (config.num_hidden_layers + 1) // 5
        __a     				=  config.hidden_size
        __a     				=  1
        __a     				=  nn.ModuleList(
            [
                BasicTransformerBlock(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						activation_fn="gelu"  ,						attention_bias=lowerCamelCase		)
                for _ in range(lowerCamelCase		)
            ]		)






 def 		a__ (				self  ,						lowerCamelCase		):
        for block in self.blocks:
               __a     				=  block(lowerCamelCase		)

        return hidden_states

"""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		)
