



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    _SCREAMING_SNAKE_CASE						(a_			):





     def __init__(							self			:				Any  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				Union[str, Any]  ,	UpperCamelCase			:				Any  ,	)->Optional[int]:
      super().__init__()

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





     def 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				Dict					)->List[Any]:
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE							:     Any							=						self.vae.encode(tfms.ToTensor()(_snake_case					).unsqueeze(0					).to(self.device					) * 2 - 1					)
       __SCREAMING_SNAKE_CASE							:     int							=						0.1_8_2_1_5 * latent.latent_dist.sample()
      return latent





     def 						__snake_case     (							self			:				Optional[Any]  ,	UpperCamelCase			:				Tuple					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						(1 / 0.1_8_2_1_5) * latent
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE							:     Tuple							=						self.vae.decode(_snake_case					).sample
      __SCREAMING_SNAKE_CASE							:     str							=						(img / 2 + 0.5).clamp(0  ,	1					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						img.detach().cpu().permute(0  ,	2  ,	3  ,	1					).numpy()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						(img * 2_5_5).round().astype("uint8"					)
      return Image.fromarray(img[0]					)





     def 						__snake_case     (							self			:				Tuple  ,	UpperCamelCase			:				List[str]					)->int:
      __SCREAMING_SNAKE_CASE							:     int							=						self.tokenizer(
          _snake_case  ,	padding="max_length"  ,	max_length=self.tokenizer.model_max_length  ,	truncation=_snake_case  ,	return_tensors="pt"  ,	)

      __SCREAMING_SNAKE_CASE							:     Any							=						self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.tokenizer(
          ""  ,	padding="max_length"  ,	max_length=self.tokenizer.model_max_length  ,	truncation=_snake_case  ,	return_tensors="pt"  ,	)

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

      return torch.cat([uncond_embedding, text_embedding]					)






     def __call__(							self			:				Tuple  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				List[Any]  ,	UpperCamelCase			:				Tuple = 0.3  ,	UpperCamelCase			:				Optional[Any] = 0.6  ,	UpperCamelCase			:				Optional[Any] = 0.5  ,	UpperCamelCase			:				List[Any] = 4_2  ,	UpperCamelCase			:				Union[str, Any] = 5_0  ,	UpperCamelCase			:				Union[str, Any] = 7.5  ,	)->Image.Image:
      __SCREAMING_SNAKE_CASE							:     int							=						steps - int(kmin * steps					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						steps - int(kmax * steps					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.prep_text(_snake_case					)

      self.scheduler.set_timesteps(_snake_case					)

      __SCREAMING_SNAKE_CASE							:     Any							=						img.size
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.encode(_snake_case					)

      torch.manual_seed(_snake_case					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)  ,	).to(self.device					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.scheduler.add_noise(
          _snake_case  ,	_snake_case  ,	timesteps=self.scheduler.timesteps[tmax]  ,	)

      __SCREAMING_SNAKE_CASE							:     Any							=						torch.cat([latents] * 2					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.scheduler.scale_model_input(_snake_case  ,	self.scheduler.timesteps[tmax]					)

      with torch.no_grad():
       __SCREAMING_SNAKE_CASE							:     int							=						self.unet(
           _snake_case  ,	self.scheduler.timesteps[tmax]  ,	encoder_hidden_states=_snake_case  ,	).sample

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

      __SCREAMING_SNAKE_CASE							:     Tuple							=						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
         __SCREAMING_SNAKE_CASE							:     List[str]							=						self.scheduler.add_noise(
             _snake_case  ,	_snake_case  ,	timesteps=_snake_case  ,	)

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

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

        __SCREAMING_SNAKE_CASE							:     Tuple							=						self.scheduler.scale_model_input(_snake_case  ,	_snake_case					)

        with torch.no_grad():
         __SCREAMING_SNAKE_CASE							:     str							=						self.unet(
             _snake_case  ,	_snake_case  ,	encoder_hidden_states=_snake_case  ,	).sample

        __SCREAMING_SNAKE_CASE							:     str							=						pred.chunk(2					)
        __SCREAMING_SNAKE_CASE							:     Any							=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.scheduler.step(_snake_case  ,	_snake_case  ,	_snake_case					).prev_sample

      return self.decode(_snake_case					)









'''simple docstring'''


import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
						import torch



class        lowercase    (	unittest.TestCase,       a_			):


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   int													=       load_tool('speech-to-text')
								self.tool.setup()


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   str													=       self.tool(torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')





							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[Any]													=       self.tool(audio=torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')






