





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	(   _lowercase					):




					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      )						->  List[Any]:
							super().__init__()

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




					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  Optional[Any]:
							with torch.no_grad():
									UpperCamelCase__         =	self.vae.encode(tfms.ToTensor()(__lowerCAmelCase       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
									UpperCamelCase__         =	0.1_8_2_1_5 * latent.latent_dist.sample()
							return latent




					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  Optional[int]:
							UpperCamelCase__         =	(1 / 0.1_8_2_1_5) * latent
							with torch.no_grad():
									UpperCamelCase__         =	self.vae.decode(__lowerCAmelCase       ).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    ,      __lowerCAmelCase       )						->  Any:
							UpperCamelCase__         =	self.tokenizer(
							    __lowerCAmelCase    ,      padding="""max_length"""    ,      max_length=self.tokenizer.model_max_length    ,      truncation=__lowerCAmelCase    ,      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=__lowerCAmelCase    ,      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    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = 0.3    ,      __lowerCAmelCase = 0.6    ,      __lowerCAmelCase = 0.5    ,      __lowerCAmelCase = 42    ,      __lowerCAmelCase = 50    ,      __lowerCAmelCase = 7.5    ,      )						->  Image.Image:
							UpperCamelCase__         =	steps - int(kmin * steps       )
							UpperCamelCase__         =	steps - int(kmax * steps       )

							UpperCamelCase__         =	self.prep_text(__lowerCAmelCase       )

							self.scheduler.set_timesteps(__lowerCAmelCase       )

							UpperCamelCase__  ,			UpperCamelCase__         =	img.size
							UpperCamelCase__         =	self.encode(__lowerCAmelCase       )

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

							UpperCamelCase__         =	self.scheduler.add_noise(
							    __lowerCAmelCase    ,      __lowerCAmelCase    ,      timesteps=self.scheduler.timesteps[tmax]    ,      )

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

							UpperCamelCase__         =	self.scheduler.scale_model_input(__lowerCAmelCase    ,      self.scheduler.timesteps[tmax]       )

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

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

							UpperCamelCase__         =	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
													UpperCamelCase__         =	self.scheduler.add_noise(
													    __lowerCAmelCase    ,      __lowerCAmelCase    ,      timesteps=__lowerCAmelCase    ,      )

													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(__lowerCAmelCase    ,      __lowerCAmelCase       )

											with torch.no_grad():
													UpperCamelCase__         =	self.unet(
													    __lowerCAmelCase    ,      __lowerCAmelCase    ,      encoder_hidden_states=__lowerCAmelCase    ,      ).sample

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

											UpperCamelCase__         =	self.scheduler.step(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       ).prev_sample

							return self.decode(__lowerCAmelCase       )












import warnings

from .generation import FlaxGenerationMixin




class   _UpperCAmelCase	(   _lowercase					):
					# warning at import time
					warnings.warn(
					    "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
					    "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead." ,       _lowercase ,       )





