



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__   ):





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

											'''simple docstring'''
											super().__init__()

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





							def   UpperCamelCase_   (				self						:						List[Any] ,			__lowercase						:						List[str]		):

											'''simple docstring'''
											with torch.no_grad():
															__a					       =						self.vae.encode(tfms.ToTensor()(__lowercase		).unsqueeze(0		).to(self.device		) * 2 - 1		)
															__a					       =						0.18215 * latent.latent_dist.sample()
											return latent





							def   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						int		):

											'''simple docstring'''
											__a					       =						(1 / 0.18215) * 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						:						Any ,			__lowercase						:						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						:						str ,			__lowercase						:						Image.Image ,			__lowercase						:						str ,			__lowercase						:						float = 0.3 ,			__lowercase						:						float = 0.6 ,			__lowercase						:						float = 0.5 ,			__lowercase						:						int = 42 ,			__lowercase						:						int = 50 ,			__lowercase						:						float = 7.5 ,			):

											'''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		)





# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase__     =	"""Update an existing config file with the latest defaults while maintaining the old configuration."""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







				__a					       =						args.config_file
				if config_file is None and Path(_SCREAMING_SNAKE_CASE      ).exists():
								__a					       =						default_config_file
				elif not Path(_SCREAMING_SNAKE_CASE      ).exists():
								raise ValueError(f"The passed config file located at {config_file} doesn't exist."      )
				__a					       =						load_config_from_file(_SCREAMING_SNAKE_CASE      )

				if config_file.endswith(""".json"""      ):
								config.to_json_file(_SCREAMING_SNAKE_CASE      )
				else:
								config.to_yaml_file(_SCREAMING_SNAKE_CASE      )
				return config_file

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     int      ):
				"""simple docstring"""







				__a					       =						parser.add_parser("""update"""			,       parents=_SCREAMING_SNAKE_CASE			,       help=_SCREAMING_SNAKE_CASE			,       formatter_class=_SCREAMING_SNAKE_CASE      )
				parser.add_argument(
				    """--config_file"""			,       default=_SCREAMING_SNAKE_CASE			,       help=(
				        """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
				        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
				        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
				        """with 'huggingface'."""
				    )			,       )

				parser.set_defaults(func=_SCREAMING_SNAKE_CASE      )
				return parser

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				__a					       =						update_config(_SCREAMING_SNAKE_CASE      )
				print(f"Sucessfully updated the configuration file at {config_file}."      )
