
'''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 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	def __init__(						self				: Optional[int]					,						UpperCamelCase_				: AutoencoderKL					,						UpperCamelCase_				: CLIPTextModel					,						UpperCamelCase_				: CLIPTokenizer					,						UpperCamelCase_				: UNetaDConditionModel					,						UpperCamelCase_				: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]					,						):
			super().__init__()

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




	def 	__A      (						self				: List[Any]					,						UpperCamelCase_				: Optional[int]      ):
			with torch.no_grad():
					lowerCAmelCase_   :       Dict    							=self.vae.encode(tfms.ToTensor()(UpperCamelCase_      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
					lowerCAmelCase_   :       str    							=0.1_8_2_1_5 * latent.latent_dist.sample()
			return latent




	def 	__A      (						self				: Optional[int]					,						UpperCamelCase_				: Union[str, Any]      ):
			lowerCAmelCase_   :       Dict    							=(1 / 0.1_8_2_1_5) * latent
			with torch.no_grad():
					lowerCAmelCase_   :       str    							=self.vae.decode(UpperCamelCase_      ).sample
			lowerCAmelCase_   :       List[Any]    							=(img / 2 + 0.5).clamp(0					,						1      )
			lowerCAmelCase_   :       str    							=img.detach().cpu().permute(0					,						2					,						3					,						1      ).numpy()
			lowerCAmelCase_   :       Dict    							=(img * 255).round().astype('''uint8'''      )
			return Image.fromarray(img[0]      )




	def 	__A      (						self				: Union[str, Any]					,						UpperCamelCase_				: Union[str, Any]      ):
			lowerCAmelCase_   :       List[Any]    							=self.tokenizer(
			    UpperCamelCase_					,						padding='''max_length'''					,						max_length=self.tokenizer.model_max_length					,						truncation=UpperCamelCase_					,						return_tensors='''pt'''					,						)

			lowerCAmelCase_   :       int    							=self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

			lowerCAmelCase_   :       Tuple    							=self.tokenizer(
			    ''''''					,						padding='''max_length'''					,						max_length=self.tokenizer.model_max_length					,						truncation=UpperCamelCase_					,						return_tensors='''pt'''					,						)

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

			return torch.cat([uncond_embedding, text_embedding]      )





	def __call__(						self				: Union[str, Any]					,						UpperCamelCase_				: Image.Image					,						UpperCamelCase_				: str					,						UpperCamelCase_				: float = 0.3					,						UpperCamelCase_				: float = 0.6					,						UpperCamelCase_				: float = 0.5					,						UpperCamelCase_				: int = 42					,						UpperCamelCase_				: int = 50					,						UpperCamelCase_				: float = 7.5					,						):
			lowerCAmelCase_   :       Dict    							=steps - int(kmin * steps      )
			lowerCAmelCase_   :       int    							=steps - int(kmax * steps      )

			lowerCAmelCase_   :       List[Any]    							=self.prep_text(UpperCamelCase_      )

			self.scheduler.set_timesteps(UpperCamelCase_      )

			lowerCAmelCase_							,		lowerCAmelCase_   :       Union[str, Any]    							=img.size
			lowerCAmelCase_   :       Optional[Any]    							=self.encode(UpperCamelCase_      )

			torch.manual_seed(UpperCamelCase_      )
			lowerCAmelCase_   :       List[Any]    							=torch.randn(
			    (1, self.unet.config.in_channels, height // 8, width // 8)					,						).to(self.device      )

			lowerCAmelCase_   :       Any    							=self.scheduler.add_noise(
			    UpperCamelCase_					,						UpperCamelCase_					,						timesteps=self.scheduler.timesteps[tmax]					,						)

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

			lowerCAmelCase_   :       Dict    							=self.scheduler.scale_model_input(UpperCamelCase_					,						self.scheduler.timesteps[tmax]      )

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

			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=pred.chunk(2      )
			lowerCAmelCase_   :       List[Any]    							=pred_uncond + guidance_scale * (pred_text - pred_uncond)

			lowerCAmelCase_   :       Optional[Any]    							=self.scheduler.step(UpperCamelCase_					,						self.scheduler.timesteps[tmax]					,						UpperCamelCase_      ).prev_sample

			for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
					if i > tmax:
							if i < tmin:  # layout generation phase
									lowerCAmelCase_   :       List[Any]    							=self.scheduler.add_noise(
									    UpperCamelCase_					,						UpperCamelCase_					,						timesteps=UpperCamelCase_					,						)

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

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

							lowerCAmelCase_   :       Dict    							=self.scheduler.scale_model_input(UpperCamelCase_					,						UpperCamelCase_      )

							with torch.no_grad():
									lowerCAmelCase_   :       Dict    							=self.unet(
									    UpperCamelCase_					,						UpperCamelCase_					,						encoder_hidden_states=UpperCamelCase_					,						).sample

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

							lowerCAmelCase_   :       Tuple    							=self.scheduler.step(UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_      ).prev_sample

			return self.decode(UpperCamelCase_      )








'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase	=    logging.get_logger(__name__)

__lowercase	=    {
    '''google/pegasus-x-base''': '''https://huggingface.co/google/pegasus-x-base/resolve/main/config.json''',
    '''google/pegasus-x-large''': '''https://huggingface.co/google/pegasus-x-large/resolve/main/config.json''',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		'''pegasus_x'''
	_UpperCamelCase							:  Optional[int]								=		['''past_key_values''']
	_UpperCamelCase							:  str								=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(						self				: Any					,						UpperCamelCase_				: Tuple=96103					,						UpperCamelCase_				: List[str]=16384					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: Dict=4096					,						UpperCamelCase_				: str=16					,						UpperCamelCase_				: Union[str, Any]=16					,						UpperCamelCase_				: str=4096					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Dict=0.0					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: List[str]="gelu"					,						UpperCamelCase_				: Dict=1024					,						UpperCamelCase_				: str=0.1					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Optional[Any]=0.0					,						UpperCamelCase_				: str=0.0_2					,						UpperCamelCase_				: Optional[Any]=0					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Tuple=0					,						UpperCamelCase_				: Optional[int]=1					,						UpperCamelCase_				: List[Any]=1					,						UpperCamelCase_				: List[Any]=32					,						UpperCamelCase_				: Optional[int]=512					,						UpperCamelCase_				: Any=True					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_   :       List[Any]    							=vocab_size
			lowerCAmelCase_   :       Any    							=max_position_embeddings
			lowerCAmelCase_   :       int    							=d_model
			lowerCAmelCase_   :       List[Any]    							=encoder_ffn_dim
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=encoder_attention_heads
			lowerCAmelCase_   :       List[Any]    							=decoder_ffn_dim
			lowerCAmelCase_   :       List[Any]    							=decoder_layers
			lowerCAmelCase_   :       List[str]    							=decoder_attention_heads
			lowerCAmelCase_   :       str    							=dropout
			lowerCAmelCase_   :       Any    							=attention_dropout
			lowerCAmelCase_   :       Any    							=activation_dropout
			lowerCAmelCase_   :       Optional[int]    							=activation_function
			lowerCAmelCase_   :       List[Any]    							=init_std
			lowerCAmelCase_   :       List[str]    							=encoder_layerdrop
			lowerCAmelCase_   :       Optional[int]    							=decoder_layerdrop
			lowerCAmelCase_   :       int    							=use_cache
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=scale_embedding  # scale factor will be sqrt(d_model) if True

			lowerCAmelCase_   :       Optional[int]    							=num_global_tokens
			lowerCAmelCase_   :       List[str]    							=block_size
			lowerCAmelCase_   :       Optional[int]    							=stagger_local_blocks

			super().__init__(
			    pad_token_id=UpperCamelCase_					,						eos_token_id=UpperCamelCase_					,						is_encoder_decoder=UpperCamelCase_					,						decoder_start_token_id=UpperCamelCase_					,						forced_eos_token_id=UpperCamelCase_					,						**UpperCamelCase_					,						)




	@property
	def 	__A      (						self				: List[str]      ):
			return self.encoder_attention_heads





	@property
	def 	__A      (						self				: int      ):
			return self.d_model






