






"""simple docstring"""
import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor



class 		a_      (	unittest.TestCase ):







      '''simple docstring'''

      @property
      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						1
       lowerCamelCase__     :			List[str]     				=						3
       lowerCamelCase__     :			Union[str, Any]     				=						8
       lowerCamelCase__     :			int     				=						8

       lowerCamelCase__     :			Dict     				=						torch.rand((batch_size, num_channels, height, width)					)

       return sample




      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       if isinstance(image[0],     PIL.Image.Image					):
        return np.stack([np.array(_lowerCamelCase					) for i in image],     axis=0					)
       elif isinstance(_lowerCamelCase,     torch.Tensor					):
        return image.cpu().numpy().transpose(0,     2,     3,     1					)
       return image




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						VaeImageProcessor(do_resize=_lowerCamelCase,     do_normalize=_lowerCamelCase					)

       lowerCamelCase__     :			str     				=						self.dummy_sample
       lowerCamelCase__     :			List[str]     				=						self.to_np(_lowerCamelCase					)

       for output_type in ["pt", "np", "pil"]:
        lowerCamelCase__     :			Union[str, Any]     				=						image_processor.postprocess(
            image_processor.preprocess(_lowerCamelCase					),     output_type=_lowerCamelCase,     )
        lowerCamelCase__     :			Optional[Any]     				=						self.to_np(_lowerCamelCase					)
        lowerCamelCase__     :			Union[str, Any]     				=						(input_np * 2_5_5).round() if output_type == 'pil' else input_np
        assert (
            np.abs(in_np - out_np					).max() < 1e-6
        ), f'''decoded output does not match input for output_type {output_type}'''




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						VaeImageProcessor(do_resize=_lowerCamelCase,     do_normalize=_lowerCamelCase					)
       lowerCamelCase__     :			Optional[Any]     				=						self.dummy_sample.cpu().numpy().transpose(0,     2,     3,     1					)

       for output_type in ["pt", "np", "pil"]:
        lowerCamelCase__     :			Union[str, Any]     				=						image_processor.postprocess(image_processor.preprocess(_lowerCamelCase					),     output_type=_lowerCamelCase					)

        lowerCamelCase__     :			Dict     				=						self.to_np(_lowerCamelCase					)
        lowerCamelCase__     :			str     				=						(input_np * 2_5_5).round() if output_type == 'pil' else input_np
        assert (
            np.abs(in_np - out_np					).max() < 1e-6
        ), f'''decoded output does not match input for output_type {output_type}'''




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						VaeImageProcessor(do_resize=_lowerCamelCase,     do_normalize=_lowerCamelCase					)

       lowerCamelCase__     :			Optional[int]     				=						self.dummy_sample.cpu().numpy().transpose(0,     2,     3,     1					)
       lowerCamelCase__     :			Dict     				=						image_processor.numpy_to_pil(_lowerCamelCase					)

       for output_type in ["pt", "np", "pil"]:
        lowerCamelCase__     :			List[str]     				=						image_processor.postprocess(image_processor.preprocess(_lowerCamelCase					),     output_type=_lowerCamelCase					)
        for i, o in zip(_lowerCamelCase,     _lowerCamelCase					):
         lowerCamelCase__     :			Tuple     				=						np.array(_lowerCamelCase					)
         lowerCamelCase__     :			List[str]     				=						self.to_np(_lowerCamelCase					) if output_type == 'pil' else (self.to_np(_lowerCamelCase					) * 2_5_5).round()
         assert (
             np.abs(in_np - out_np					).max() < 1e-6
         ), f'''decoded output does not match input for output_type {output_type}'''




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						VaeImageProcessor(do_resize=_lowerCamelCase,     do_normalize=_lowerCamelCase					)

       lowerCamelCase__     :			str     				=						self.dummy_sample
       lowerCamelCase__     :			Dict     				=						input_pt_ad.squeeze(0					)

       lowerCamelCase__     :			List[Any]     				=						image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase					),     output_type='np',     )
       lowerCamelCase__     :			Optional[Any]     				=						image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase					),     output_type='np',     )

       lowerCamelCase__     :			Dict     				=						self.to_np(self.dummy_sample					)
       lowerCamelCase__     :			int     				=						input_np_ad.squeeze(0					)

       lowerCamelCase__     :			Any     				=						image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase					),     output_type='np',     )
       lowerCamelCase__     :			List[Any]     				=						image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase					),     output_type='np',     )

       assert np.abs(out_pt_ad - out_pt_ad					).max() < 1e-6
       assert np.abs(out_np_ad - out_np_ad					).max() < 1e-6





      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			str     				=						VaeImageProcessor(do_resize=_lowerCamelCase,     do_normalize=_lowerCamelCase					)

       lowerCamelCase__     :			str     				=						self.dummy_sample
       lowerCamelCase__     :			str     				=						list(_lowerCamelCase					)

       lowerCamelCase__     :			Union[str, Any]     				=						image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase					),     output_type='np',     )

       lowerCamelCase__     :			List[Any]     				=						image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase					),     output_type='np',     )

       lowerCamelCase__     :			str     				=						self.to_np(self.dummy_sample					)
       list(_lowerCamelCase					)

       lowerCamelCase__     :			List[Any]     				=						image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase					),     output_type='np',     )

       lowerCamelCase__     :			Dict     				=						image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase					),     output_type='np',     )

       assert np.abs(out_pt_ad - out_pt_list					).max() < 1e-6
       assert np.abs(out_np_ad - out_np_list					).max() < 1e-6


"""simple docstring"""


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, ):
										super().__init__()

										self.register_modules(vae=_lowerCamelCase	, text_encoder=_lowerCamelCase	, tokenizer=_lowerCamelCase	, unet=_lowerCamelCase	, scheduler=_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase      ):
										with torch.no_grad():
																lowerCamelCase__					=self.vae.encode(tfms.ToTensor()(_lowerCamelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
																lowerCamelCase__					=0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=(1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
																lowerCamelCase__					=self.vae.decode(_lowerCamelCase      ).sample
										lowerCamelCase__					=(img / 2 + 0.5).clamp(0	, 1      )
										lowerCamelCase__					=img.detach().cpu().permute(0	, 2	, 3	, 1      ).numpy()
										lowerCamelCase__					=(img * 255).round().astype("uint8"      )
										return Image.fromarray(img[0]      )

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=self.tokenizer(
										    _lowerCamelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

										lowerCamelCase__					=self.tokenizer(
										    ""	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

										return torch.cat([uncond_embedding, text_embedding]      )






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = 0.3	, _lowerCamelCase = 0.6	, _lowerCamelCase = 0.5	, _lowerCamelCase = 42	, _lowerCamelCase = 50	, _lowerCamelCase = 7.5	, ):
										lowerCamelCase__					=steps - int(kmin * steps      )
										lowerCamelCase__					=steps - int(kmax * steps      )

										lowerCamelCase__					=self.prep_text(_lowerCamelCase      )

										self.scheduler.set_timesteps(_lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					=img.size
										lowerCamelCase__					=self.encode(_lowerCamelCase      )

										torch.manual_seed(_lowerCamelCase      )
										lowerCamelCase__					=torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)	, ).to(self.device      )

										lowerCamelCase__					=self.scheduler.add_noise(
										    _lowerCamelCase	, _lowerCamelCase	, timesteps=self.scheduler.timesteps[tmax]	, )

										lowerCamelCase__					=torch.cat([latents] * 2      )

										lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, self.scheduler.timesteps[tmax]      )

										with torch.no_grad():
																lowerCamelCase__					=self.unet(
																    _lowerCamelCase	, self.scheduler.timesteps[tmax]	, encoder_hidden_states=_lowerCamelCase	, ).sample

										lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
										lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

										lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, self.scheduler.timesteps[tmax]	, _lowerCamelCase      ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
																if i > tmax:
																						if i < tmin:  # layout generation phase
																												lowerCamelCase__					=self.scheduler.add_noise(
																												    _lowerCamelCase	, _lowerCamelCase	, timesteps=_lowerCamelCase	, )

																												lowerCamelCase__					=(mix_factor * latents) + (
																												    1 - mix_factor
																												) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																												lowerCamelCase__					=torch.cat([input] * 2      )

																						else:  # content generation phase
																												lowerCamelCase__					=torch.cat([latents] * 2      )

																						lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, _lowerCamelCase      )

																						with torch.no_grad():
																												lowerCamelCase__					=self.unet(
																												    _lowerCamelCase	, _lowerCamelCase	, encoder_hidden_states=_lowerCamelCase	, ).sample

																						lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
																						lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

																						lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ).prev_sample

										return self.decode(_lowerCamelCase      )


