



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 					lowercase						(	__UpperCamelCase ):




       '''simple docstring'''







       def __init__(						self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       )					->		str:

           """simple docstring"""
           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		,       _snake_case   )					->		Optional[Any]:

           """simple docstring"""
           with torch.no_grad():
               UpperCAmelCase     =    self.vae.encode(tfms.ToTensor()(_snake_case   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
               UpperCAmelCase     =    0.1_8215 * latent.latent_dist.sample()
           return latent






       def 				snake_case_ (						self		,       _snake_case   )					->		Union[str, Any]:

           """simple docstring"""
           UpperCAmelCase     =    (1 / 0.1_8215) * latent
           with torch.no_grad():
               UpperCAmelCase     =    self.vae.decode(_snake_case   ).sample
           UpperCAmelCase     =    (img / 2 + 0.5).clamp(0		,       1   )
           UpperCAmelCase     =    img.detach().cpu().permute(0		,       2		,       3		,       1   ).numpy()
           UpperCAmelCase     =    (img * 255).round().astype('''uint8'''   )
           return Image.fromarray(img[0]   )






       def 				snake_case_ (						self		,       _snake_case   )					->		str:

           """simple docstring"""
           UpperCAmelCase     =    self.tokenizer(
               _snake_case		,       padding='''max_length'''		,       max_length=self.tokenizer.model_max_length		,       truncation=_snake_case		,       return_tensors='''pt'''		,       )

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

           UpperCAmelCase     =    self.tokenizer(
               ''''''		,       padding='''max_length'''		,       max_length=self.tokenizer.model_max_length		,       truncation=_snake_case		,       return_tensors='''pt'''		,       )

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

           return torch.cat([uncond_embedding, text_embedding]   )






       def __call__(						self		,       _snake_case		,       _snake_case		,       _snake_case = 0.3		,       _snake_case = 0.6		,       _snake_case = 0.5		,       _snake_case = 42		,       _snake_case = 50		,       _snake_case = 7.5		,       )					->		int:

           """simple docstring"""
           UpperCAmelCase     =    steps - int(kmin * steps   )
           UpperCAmelCase     =    steps - int(kmax * steps   )

           UpperCAmelCase     =    self.prep_text(_snake_case   )

           self.scheduler.set_timesteps(_snake_case   )

           UpperCAmelCase     =    img.size
           UpperCAmelCase     =    self.encode(_snake_case   )

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

           UpperCAmelCase     =    self.scheduler.add_noise(
               _snake_case		,       _snake_case		,       timesteps=self.scheduler.timesteps[tmax]		,       )

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

           UpperCAmelCase     =    self.scheduler.scale_model_input(_snake_case		,       self.scheduler.timesteps[tmax]   )

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

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

           UpperCAmelCase     =    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
                       UpperCAmelCase     =    self.scheduler.add_noise(
                           _snake_case		,       _snake_case		,       timesteps=_snake_case		,       )

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

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

                   UpperCAmelCase     =    self.scheduler.scale_model_input(_snake_case		,       _snake_case   )

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

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

                   UpperCAmelCase     =    self.scheduler.step(_snake_case		,       _snake_case		,       _snake_case   ).prev_sample

           return self.decode(_snake_case   )








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import CamembertModel


@require_torch
@require_sentencepiece
@require_tokenizers
class        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      device=a						,      dtype=torch.float						,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(torch.allclose(output[:, :3, :3]						,      a						,      atol=1e-4				)				)

