
"""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 SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	def __init__(  self :Tuple       ,    __magic_name__ :Tuple       ,    __magic_name__ :Union[str, Any]       ,    __magic_name__ :str       ,    __magic_name__ :List[Any]       ,    __magic_name__ :List[Any]       ,    )			->   List[Any]:

			'''simple docstring'''






			super().__init__()

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


	def    _UpperCamelCase							(  self :Optional[Any]       ,    __magic_name__ :List[Any]			)			->   Any:

			'''simple docstring'''






			with torch.no_grad():
					a__   		=     self.vae.encode(tfms.ToTensor()(lowercase__			).unsqueeze(0			).to(self.device			) * 2 - 1			)
					a__   		=     0.18_215 * latent.latent_dist.sample()
			return latent


	def    _UpperCamelCase							(  self :List[str]       ,    __magic_name__ :Tuple			)			->   str:

			'''simple docstring'''






			a__   		=     (1 / 0.18_215) * latent
			with torch.no_grad():
					a__   		=     self.vae.decode(lowercase__			).sample
			a__   		=     (img / 2 + 0.5).clamp(0       ,    1			)
			a__   		=     img.detach().cpu().permute(0       ,    2       ,    3       ,    1			).numpy()
			a__   		=     (img * 255).round().astype('''uint8'''			)
			return Image.fromarray(img[0]			)


	def    _UpperCamelCase							(  self :Dict       ,    __magic_name__ :Tuple			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     self.tokenizer(
			    lowercase__       ,    padding='''max_length'''       ,    max_length=self.tokenizer.model_max_length       ,    truncation=lowercase__       ,    return_tensors='''pt'''       ,    )

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

			a__   		=     self.tokenizer(
			    ''''''       ,    padding='''max_length'''       ,    max_length=self.tokenizer.model_max_length       ,    truncation=lowercase__       ,    return_tensors='''pt'''       ,    )

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

			return torch.cat([uncond_embedding, text_embedding]			)






	def __call__(  self :Dict       ,    __magic_name__ :str       ,    __magic_name__ :int       ,    __magic_name__ :Optional[int] = 0.3       ,    __magic_name__ :int = 0.6       ,    __magic_name__ :Optional[int] = 0.5       ,    __magic_name__ :Tuple = 42       ,    __magic_name__ :Optional[Any] = 50       ,    __magic_name__ :List[str] = 7.5       ,    )			->   Image.Image:

			'''simple docstring'''






			a__   		=     steps - int(kmin * steps			)
			a__   		=     steps - int(kmax * steps			)

			a__   		=     self.prep_text(lowercase__			)

			self.scheduler.set_timesteps(lowercase__			)

			a__		,	a__   		=     img.size
			a__   		=     self.encode(lowercase__			)

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

			a__   		=     self.scheduler.add_noise(
			    lowercase__       ,    lowercase__       ,    timesteps=self.scheduler.timesteps[tmax]       ,    )

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

			a__   		=     self.scheduler.scale_model_input(lowercase__       ,    self.scheduler.timesteps[tmax]			)

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

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

			a__   		=     self.scheduler.step(lowercase__       ,    self.scheduler.timesteps[tmax]       ,    lowercase__			).prev_sample

			for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
					if i > tmax:
							if i < tmin:  # layout generation phase
									a__   		=     self.scheduler.add_noise(
									    lowercase__       ,    lowercase__       ,    timesteps=lowercase__       ,    )

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

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

							a__   		=     self.scheduler.scale_model_input(lowercase__       ,    lowercase__			)

							with torch.no_grad():
									a__   		=     self.unet(
									    lowercase__       ,    lowercase__       ,    encoder_hidden_states=lowercase__       ,    ).sample

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

							a__   		=     self.scheduler.step(lowercase__       ,    lowercase__       ,    lowercase__			).prev_sample

			return self.decode(lowercase__			)






"""simple docstring"""




from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case					  =						logging.get_logger(__name__)




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							["input_ids", "attention_mask"]


				def __init__(							self						,      lowercase__="[PAD]"						,      lowercase__="[BOS]"						,      lowercase__="[EOS]"						,      lowercase__="[MASK]"						,      lowercase__="[CLS]"						,      lowercase__="[SEP]"						,      lowercase__=2048						,      **lowercase__						,      )       ->       None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else pad_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else bos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else eos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else mask_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else cls_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else sep_token

									super().__init__(
									    pad_token=lowercase__						,      bos_token=lowercase__						,      eos_token=lowercase__						,      mask_token=lowercase__						,      cls_token=lowercase__						,      sep_token=lowercase__						,      model_max_length=lowercase__						,      **lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      2**8  # utf is 8 bits

									# define special tokens dict
									SCREAMING_SNAKE_CASE           =      {
									    self.pad_token: 0,
									    self.bos_token: 1,
									    self.eos_token: 2,
									    self.mask_token: 3,
									    self.cls_token: 4,
									    self.sep_token: 5,
									}
									SCREAMING_SNAKE_CASE           =      len(self.special_tokens_encoder     )
									SCREAMING_SNAKE_CASE           =      {v: k for k, v in self.special_tokens_encoder.items()}


				def 				A   (							self     )       ->       Dict[str, int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder     )
									for i in range(self._utf_vocab_size     ):
														SCREAMING_SNAKE_CASE           =      chr(lowercase__     )
														SCREAMING_SNAKE_CASE           =      i + len(self.special_tokens_encoder     )
									return vocab


				@property
				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







									return self._utf_vocab_size + self._num_special_tokens


				def 				A   (							self						,      lowercase__						,      lowercase__ = None						,      lowercase__ = False     )       ->       List[int]:
									"""simple docstring"""







									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowercase__						,      token_ids_a=lowercase__						,      already_has_special_tokens=lowercase__     )

									# normal case: some special tokens
									if token_ids_a is None:
														return [1] + [0] * len(lowercase__     ) + [1]
									return [1] + ([0] * len(lowercase__     )) + [1] + ([0] * len(lowercase__     )) + [1]


				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       List[int]:
									"""simple docstring"""







									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									else:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


				def 				A   (							self						,      lowercase__     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      [chr(lowercase__     ) for i in text.encode('utf-8'     )]
									return tokens


				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									if token in self.special_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_encoder[token]
									elif len(lowercase__     ) != 1:
														SCREAMING_SNAKE_CASE           =      self.unk_token_id
									else:
														SCREAMING_SNAKE_CASE           =      ord(lowercase__     ) + self._num_special_tokens
									return token_id


				def 				A   (							self						,      lowercase__     )       ->       int:
									"""simple docstring"""







									if index in self.special_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_decoder[index]
									else:
														SCREAMING_SNAKE_CASE           =      chr(index - self._num_special_tokens     )
									return token


				def 				A   (							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      b''
									for token in tokens:
														if token in self.special_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.added_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.special_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														elif token in self.added_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														else:
																			SCREAMING_SNAKE_CASE           =      bytes([ord(lowercase__     )]     )
														bstring += tok_string
									SCREAMING_SNAKE_CASE           =      bstring.decode('utf-8'						,      errors='replace'     )
									return string




				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       Tuple[str]:
									"""simple docstring"""







									return ()
