


'''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        UpperCAmelCase_  (SCREAMING_SNAKE_CASE__  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					)    ->					Union[str, Any]:
         super().__init__()

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







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					List[str]:
         with torch.no_grad():
              __lowerCamelCase       :     Union[str, Any]        =		self.vae.encode(tfms.ToTensor()(_UpperCAmelCase     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
              __lowerCamelCase       :     Optional[Any]        =		0.1_8_2_1_5 * latent.latent_dist.sample()
         return latent







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					Dict:
         __lowerCamelCase       :     Optional[int]        =		(1 / 0.1_8_2_1_5) * latent
         with torch.no_grad():
              __lowerCamelCase       :     int        =		self.vae.decode(_UpperCAmelCase     ).sample
         __lowerCamelCase       :     Union[str, Any]        =		(img / 2 + 0.5).clamp(0      ,					1     )
         __lowerCamelCase       :     List[str]        =		img.detach().cpu().permute(0      ,					2      ,					3      ,					1     ).numpy()
         __lowerCamelCase       :     Optional[Any]        =		(img * 2_55).round().astype('uint8'     )
         return Image.fromarray(img[0]     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					List[str]:
         __lowerCamelCase       :     Tuple        =		self.tokenizer(
             _UpperCAmelCase      ,					padding='max_length'      ,					max_length=self.tokenizer.model_max_length      ,					truncation=_UpperCAmelCase      ,					return_tensors='pt'      ,					)

         __lowerCamelCase       :     Optional[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       :     Union[str, Any]        =		self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

         return torch.cat([uncond_embedding, text_embedding]     )







    def __call__(							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = 0.3      ,					SCREAMING_SNAKE_CASE_ = 0.6      ,					SCREAMING_SNAKE_CASE_ = 0.5      ,					SCREAMING_SNAKE_CASE_ = 42      ,					SCREAMING_SNAKE_CASE_ = 50      ,					SCREAMING_SNAKE_CASE_ = 7.5      ,					)    ->					Image.Image:
         __lowerCamelCase       :     List[Any]        =		steps - int(kmin * steps     )
         __lowerCamelCase       :     Dict        =		steps - int(kmax * steps     )

         __lowerCamelCase       :     Any        =		self.prep_text(_UpperCAmelCase     )

         self.scheduler.set_timesteps(_UpperCAmelCase     )

         __lowerCamelCase       :     Dict        =		img.size
         __lowerCamelCase       :     Optional[Any]        =		self.encode(_UpperCAmelCase     )

         torch.manual_seed(_UpperCAmelCase     )
         __lowerCamelCase       :     Optional[Any]        =		torch.randn(
             (1, self.unet.config.in_channels, height // 8, width // 8)      ,					).to(self.device     )

         __lowerCamelCase       :     List[Any]        =		self.scheduler.add_noise(
             _UpperCAmelCase      ,					_UpperCAmelCase      ,					timesteps=self.scheduler.timesteps[tmax]      ,					)

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

         __lowerCamelCase       :     Optional[int]        =		self.scheduler.scale_model_input(_UpperCAmelCase      ,					self.scheduler.timesteps[tmax]     )

         with torch.no_grad():
              __lowerCamelCase       :     Optional[int]        =		self.unet(
                  _UpperCAmelCase      ,					self.scheduler.timesteps[tmax]      ,					encoder_hidden_states=_UpperCAmelCase      ,					).sample

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

         __lowerCamelCase       :     Tuple        =		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[str]        =		self.scheduler.add_noise(
                            _UpperCAmelCase      ,					_UpperCAmelCase      ,					timesteps=_UpperCAmelCase      ,					)

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

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

                   __lowerCamelCase       :     Dict        =		self.scheduler.scale_model_input(_UpperCAmelCase      ,					_UpperCAmelCase     )

                   with torch.no_grad():
                        __lowerCamelCase       :     Optional[Any]        =		self.unet(
                            _UpperCAmelCase      ,					_UpperCAmelCase      ,					encoder_hidden_states=_UpperCAmelCase      ,					).sample

                   __lowerCamelCase       :     str        =		pred.chunk(2     )
                   __lowerCamelCase       :     List[str]        =		pred_uncond + guidance_scale * (pred_text - pred_uncond)

                   __lowerCamelCase       :     int        =		self.scheduler.step(_UpperCAmelCase      ,					_UpperCAmelCase      ,					_UpperCAmelCase     ).prev_sample

         return self.decode(_UpperCAmelCase     )










'''simple docstring'''



import torch

from transformers import AutoTokenizer





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
         __lowerCamelCase       :     Dict        =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:

         if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Union[str, Any]        =		None
              for l in x:
                   __lowerCamelCase       :     Tuple        =		self.tokenizer(
                       SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)
                   __lowerCamelCase       :     str        =		torch.tensor([len(SCREAMING_SNAKE_CASE_     )]     )
                   if d is not None:
                        for k in d.keys():
                             __lowerCamelCase       :     List[Any]        =		torch.cat((d[k], t[k])      ,					0     )
                   else:
                        __lowerCamelCase       :     str        =		t

              __lowerCamelCase       :     List[Any]        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'     )     )
              __lowerCamelCase       :     Tuple        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'     )     )

         elif isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Optional[Any]        =		self.tokenizer(
                  SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)

         else:
              raise Exception(
                  'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                  ' strings` for supports are supported.'     )

         return d







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=0.7_0     )    ->					str:
         __lowerCamelCase       :     str        =		[]
         for idx in range(len(W_query['input_ids']     )     ):
              __lowerCamelCase       :     Optional[Any]        =		range(p_start.shape[1]     )

              __lowerCamelCase       :     List[Any]        =		[]
              for start_id in start_indexes:
                   for end_id in end_indexes:
                        if start_id < end_id:
                             output.append(
                                 (
                                     start_id,
                                     end_id,
                                     p_start[idx][start_id].item(),
                                     p_end[idx][end_id].item(),
                                 )     )

              output.sort(key=lambda SCREAMING_SNAKE_CASE_     : (tup[2] * tup[3])      ,					reverse=SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     str        =		[]
              for k in range(len(SCREAMING_SNAKE_CASE_     )     ):
                   if output[k][2] * output[k][3] >= thresh:
                        __lowerCamelCase    ,	__lowerCamelCase       :     Union[str, Any]        =		output[k][0], output[k][1]
                        __lowerCamelCase       :     List[str]        =		self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]     )
                        temp.append((decoded, output[k][2] * output[k][3])     )

              final_outputs.append(SCREAMING_SNAKE_CASE_     )

         return final_outputs






