
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	(		A_				):



   def __init__(self	:      List[str]					,		snake_case__	:      AutoencoderKL					,		snake_case__	:      CLIPTextModel					,		snake_case__	:      CLIPTokenizer					,		snake_case__	:      UNetaDConditionModel					,		snake_case__	:      Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]					,		)	->			Tuple:


          '''simple docstring'''


          super().__init__()

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



   def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      Tuple				)	->			List[Any]:


          '''simple docstring'''


          with torch.no_grad():
                 snake_case :     Optional[int]           =   self.vae.encode(tfms.ToTensor()(snake_case__				).unsqueeze(0				).to(self.device				) * 2 - 1				)
                 snake_case :     int           =   0.18215 * latent.latent_dist.sample()
          return latent



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple					,		snake_case__	:      Optional[Any]				)	->			Tuple:


          '''simple docstring'''


          snake_case :     int           =   (1 / 0.18215) * latent
          with torch.no_grad():
                 snake_case :     Any           =   self.vae.decode(snake_case__				).sample
          snake_case :     Optional[Any]           =   (img / 2 + 0.5).clamp(0					,		1				)
          snake_case :     Any           =   img.detach().cpu().permute(0					,		2					,		3					,		1				).numpy()
          snake_case :     Any           =   (img * 2_55).round().astype("uint8"				)
          return Image.fromarray(img[0]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]					,		snake_case__	:      List[Any]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     int           =   self.tokenizer(
              snake_case__					,		padding="max_length"					,		max_length=self.tokenizer.model_max_length					,		truncation=snake_case__					,		return_tensors="pt"					,		)

          snake_case :     List[str]           =   self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

          snake_case :     Dict           =   self.tokenizer(
              ""					,		padding="max_length"					,		max_length=self.tokenizer.model_max_length					,		truncation=snake_case__					,		return_tensors="pt"					,		)

          snake_case :     Any           =   self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

          return torch.cat([uncond_embedding, text_embedding]				)



   def __call__(self	:      Dict					,		snake_case__	:      Image.Image					,		snake_case__	:      str					,		snake_case__	:      float = 0.3					,		snake_case__	:      float = 0.6					,		snake_case__	:      float = 0.5					,		snake_case__	:      int = 42					,		snake_case__	:      int = 50					,		snake_case__	:      float = 7.5					,		)	->			Image.Image:


          '''simple docstring'''


          snake_case :     Dict           =   steps - int(kmin * steps				)
          snake_case :     Any           =   steps - int(kmax * steps				)

          snake_case :     Dict           =   self.prep_text(snake_case__				)

          self.scheduler.set_timesteps(snake_case__				)

          snake_case       ,      snake_case :     int           =   img.size
          snake_case :     List[Any]           =   self.encode(snake_case__				)

          torch.manual_seed(snake_case__				)
          snake_case :     List[str]           =   torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)					,		).to(self.device				)

          snake_case :     List[Any]           =   self.scheduler.add_noise(
              snake_case__					,		snake_case__					,		timesteps=self.scheduler.timesteps[tmax]					,		)

          snake_case :     List[str]           =   torch.cat([latents] * 2				)

          snake_case :     List[Any]           =   self.scheduler.scale_model_input(snake_case__					,		self.scheduler.timesteps[tmax]				)

          with torch.no_grad():
                 snake_case :     Tuple           =   self.unet(
                     snake_case__					,		self.scheduler.timesteps[tmax]					,		encoder_hidden_states=snake_case__					,		).sample

          snake_case       ,      snake_case :     Union[str, Any]           =   pred.chunk(2				)
          snake_case :     List[Any]           =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

          snake_case :     Dict           =   self.scheduler.step(snake_case__					,		self.scheduler.timesteps[tmax]					,		snake_case__				).prev_sample

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

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

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

                        snake_case :     str           =   self.scheduler.scale_model_input(snake_case__					,		snake_case__				)

                        with torch.no_grad():
                               snake_case :     Optional[int]           =   self.unet(
                                   snake_case__					,		snake_case__					,		encoder_hidden_states=snake_case__					,		).sample

                        snake_case       ,      snake_case :     Optional[Any]           =   pred.chunk(2				)
                        snake_case :     str           =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                        snake_case :     Dict           =   self.scheduler.step(snake_case__					,		snake_case__					,		snake_case__				).prev_sample

          return self.decode(snake_case__				)







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils






class 						UpperCAmelCase	(		unittest.TestCase				):



   @patch("diffusers.utils.hub_utils.get_full_repo_name"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      Mock				)	->			None:


          '''simple docstring'''


          snake_case :     str           =   "full_repo_name"
          with TemporaryDirectory() as tmpdir:
                 # Dummy args values
                 snake_case :     List[str]           =   Mock()
                 snake_case :     List[str]           =   tmpdir
                 snake_case :     Optional[int]           =   0
                 snake_case :     int           =   "hub_token"
                 snake_case :     Union[str, Any]           =   "dataset_name"
                 snake_case :     Tuple           =   0.01
                 snake_case :     int           =   10_00_00
                 snake_case :     Optional[Any]           =   1_00_00
                 snake_case :     Optional[Any]           =   0.01
                 snake_case :     Dict           =   0.02
                 snake_case :     List[Any]           =   0.03
                 snake_case :     str           =   0.0005
                 snake_case :     List[str]           =   0.000001
                 snake_case :     Tuple           =   1
                 snake_case :     Optional[int]           =   10
                 snake_case :     List[str]           =   0.001
                 snake_case :     List[Any]           =   0.1
                 snake_case :     List[str]           =   0.2
                 snake_case :     Tuple           =   True

                 # Model card mush be rendered and saved
                 diffusers.utils.hub_utils.create_model_card(snake_case__					,		model_name="model_name"				)
                 self.assertTrue((Path(snake_case__				) / "README.md").is_file()				)





