



'''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 			__magic_name__		(					_UpperCamelCase     ):







							def __init__(							self      : Any			,_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 							__lowercase    (							self      : Dict			,_UpperCAmelCase      : Tuple	):
									with torch.no_grad():
											_a  :   str				 =							self.vae.encode(tfms.ToTensor()(_UpperCAmelCase	).unsqueeze(0	).to(self.device	) * 2 - 1	)
											_a  :   int				 =							0.1_82_15 * latent.latent_dist.sample()
									return latent







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : str	):
									_a  :   int				 =							(1 / 0.1_82_15) * latent
									with torch.no_grad():
											_a  :   str				 =							self.vae.decode(_UpperCAmelCase	).sample
									_a  :   Optional[Any]				 =							(img / 2 + 0.5).clamp(0			,1	)
									_a  :   List[Any]				 =							img.detach().cpu().permute(0			,2			,3			,1	).numpy()
									_a  :   Dict				 =							(img * 255).round().astype('uint8'	)
									return Image.fromarray(img[0]	)







							def 							__lowercase    (							self      : Union[str, Any]			,_UpperCAmelCase      : Union[str, Any]	):
									_a  :   str				 =							self.tokenizer(
									    _UpperCAmelCase			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=_UpperCAmelCase			,return_tensors='pt'			,)

									_a  :   List[Any]				 =							self.text_encoder(text_input.input_ids.to(self.device	)	)[0]

									_a  :   List[Any]				 =							self.tokenizer(
									    ''			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=_UpperCAmelCase			,return_tensors='pt'			,)

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

									return torch.cat([uncond_embedding, text_embedding]	)







							def __call__(							self      : Tuple			,_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			,):
									_a  :   Tuple				 =							steps - int(kmin * steps	)
									_a  :   int				 =							steps - int(kmax * steps	)

									_a  :   Optional[Any]				 =							self.prep_text(_UpperCAmelCase	)

									self.scheduler.set_timesteps(_UpperCAmelCase	)

									_a  :   List[str]				 =							img.size
									_a  :   Union[str, Any]				 =							self.encode(_UpperCAmelCase	)

									torch.manual_seed(_UpperCAmelCase	)
									_a  :   List[str]				 =							torch.randn(
									    (1, self.unet.config.in_channels, height // 8, width // 8)			,).to(self.device	)

									_a  :   Tuple				 =							self.scheduler.add_noise(
									    _UpperCAmelCase			,_UpperCAmelCase			,timesteps=self.scheduler.timesteps[tmax]			,)

									_a  :   Optional[int]				 =							torch.cat([latents] * 2	)

									_a  :   str				 =							self.scheduler.scale_model_input(_UpperCAmelCase			,self.scheduler.timesteps[tmax]	)

									with torch.no_grad():
											_a  :   Union[str, Any]				 =							self.unet(
											    _UpperCAmelCase			,self.scheduler.timesteps[tmax]			,encoder_hidden_states=_UpperCAmelCase			,).sample

									_a  :   Any				 =							pred.chunk(2	)
									_a  :   Tuple				 =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

									_a  :   int				 =							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
															_a  :   Optional[Any]				 =							self.scheduler.add_noise(
															    _UpperCAmelCase			,_UpperCAmelCase			,timesteps=_UpperCAmelCase			,)

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

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

													_a  :   List[Any]				 =							self.scheduler.scale_model_input(_UpperCAmelCase			,_UpperCAmelCase	)

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

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

													_a  :   Any				 =							self.scheduler.step(_UpperCAmelCase			,_UpperCAmelCase			,_UpperCAmelCase	).prev_sample

									return self.decode(_UpperCAmelCase	)









'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__lowerCAmelCase		    =     logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					List[str]            =      ['pixel_values']







							def __init__(							self      : int			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : Dict[str, int] = None			,_UpperCAmelCase      : PILImageResampling = PILImageResampling.BILINEAR			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : bool = False			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : Union[int, float] = 1 / 255			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : Optional[Union[float, List[float]]] = None			,_UpperCAmelCase      : Optional[Union[float, List[float]]] = None			,**_UpperCAmelCase      : Optional[Any]			,):
									super().__init__(**_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							size if size is not None else {'height': 2560, 'width': 1920}
									if isinstance(_UpperCAmelCase			,(tuple, list)	):
											# The previous feature extractor size parameter was in (width, height) format
											_a  :   Union[str, Any]				 =							size[::-1]
									_a  :   Dict				 =							get_size_dict(_UpperCAmelCase	)

									_a  :   int				 =							do_resize
									_a  :   Any				 =							size
									_a  :   Any				 =							resample
									_a  :   int				 =							do_thumbnail
									_a  :   Tuple				 =							do_align_long_axis
									_a  :   Optional[Any]				 =							do_pad
									_a  :   Any				 =							do_rescale
									_a  :   List[str]				 =							rescale_factor
									_a  :   Any				 =							do_normalize
									_a  :   Union[str, Any]				 =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_a  :   Any				 =							image_std if image_std is not None else IMAGENET_STANDARD_STD







							def 							__lowercase    (							self      : Optional[Any]			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Dict[str, int]			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None	):
									_a ,      _a  :   List[Any]				 =							get_image_size(_UpperCAmelCase	)
									_a ,      _a  :   List[str]				 =							size['height'], size['width']

									if (output_width < output_height and input_width > input_height) or (
									    output_width > output_height and input_width < input_height
									):
											_a  :   Optional[int]				 =							np.rotaa(_UpperCAmelCase			,3	)

									if data_format is not None:
											_a  :   Any				 =							to_channel_dimension_format(_UpperCAmelCase			,_UpperCAmelCase	)

									return image







							def 							__lowercase    (							self      : Any			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Dict[str, int]			,_UpperCAmelCase      : bool = False			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,):
									_a ,      _a  :   List[Any]				 =							size['height'], size['width']
									_a ,      _a  :   str				 =							get_image_size(_UpperCAmelCase	)

									_a  :   List[str]				 =							output_width - input_width
									_a  :   Union[str, Any]				 =							output_height - input_height

									if random_padding:
											_a  :   Union[str, Any]				 =							np.random.randint(low=0			,high=delta_height + 1	)
											_a  :   Any				 =							np.random.randint(low=0			,high=delta_width + 1	)
									else:
											_a  :   Tuple				 =							delta_height // 2
											_a  :   List[Any]				 =							delta_width // 2

									_a  :   Dict				 =							delta_height - pad_top
									_a  :   Optional[int]				 =							delta_width - pad_left

									_a  :   int				 =							((pad_top, pad_bottom), (pad_left, pad_right))
									return pad(_UpperCAmelCase			,_UpperCAmelCase			,data_format=_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[Any]			,*_UpperCAmelCase      : int			,**_UpperCAmelCase      : List[str]	):
									logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'	)
									return self.pad_image(*_UpperCAmelCase			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : Dict			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Dict[str, int]			,_UpperCAmelCase      : PILImageResampling = PILImageResampling.BICUBIC			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,**_UpperCAmelCase      : Union[str, Any]			,):
									_a ,      _a  :   Any				 =							get_image_size(_UpperCAmelCase	)
									_a ,      _a  :   Union[str, Any]				 =							size['height'], size['width']

									# We always resize to the smallest of either the input or output size.
									_a  :   Dict				 =							min(_UpperCAmelCase			,_UpperCAmelCase	)
									_a  :   List[Any]				 =							min(_UpperCAmelCase			,_UpperCAmelCase	)

									if height == input_height and width == input_width:
											return image

									if input_height > input_width:
											_a  :   Any				 =							int(input_width * height / input_height	)
									elif input_width > input_height:
											_a  :   int				 =							int(input_height * width / input_width	)

									return resize(
									    _UpperCAmelCase			,size=(height, width)			,resample=_UpperCAmelCase			,reducing_gap=2.0			,data_format=_UpperCAmelCase			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Dict[str, int]			,_UpperCAmelCase      : PILImageResampling = PILImageResampling.BICUBIC			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,**_UpperCAmelCase      : List[Any]			,):
									_a  :   List[Any]				 =							get_size_dict(_UpperCAmelCase	)
									_a  :   int				 =							min(size['height']			,size['width']	)
									_a  :   Dict				 =							get_resize_output_image_size(_UpperCAmelCase			,size=_UpperCAmelCase			,default_to_square=_UpperCAmelCase	)
									_a  :   int				 =							resize(_UpperCAmelCase			,size=_UpperCAmelCase			,resample=_UpperCAmelCase			,data_format=_UpperCAmelCase			,**_UpperCAmelCase	)
									return resized_image







							def 							__lowercase    (							self      : int			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Union[int, float]			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,**_UpperCAmelCase      : Optional[Any]			,):
									return rescale(_UpperCAmelCase			,scale=_UpperCAmelCase			,data_format=_UpperCAmelCase			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Union[float, List[float]]			,_UpperCAmelCase      : Union[float, List[float]]			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,**_UpperCAmelCase      : Dict			,):
									return normalize(_UpperCAmelCase			,mean=_UpperCAmelCase			,std=_UpperCAmelCase			,data_format=_UpperCAmelCase			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : Optional[int]			,_UpperCAmelCase      : ImageInput			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : Dict[str, int] = None			,_UpperCAmelCase      : PILImageResampling = None			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : bool = False			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : float = None			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : Optional[Union[float, List[float]]] = None			,_UpperCAmelCase      : Optional[Union[float, List[float]]] = None			,_UpperCAmelCase      : Optional[Union[str, TensorType]] = None			,_UpperCAmelCase      : Optional[ChannelDimension] = ChannelDimension.FIRST			,**_UpperCAmelCase      : int			,):
									_a  :   List[str]				 =							do_resize if do_resize is not None else self.do_resize
									_a  :   Optional[Any]				 =							size if size is not None else self.size
									if isinstance(_UpperCAmelCase			,(tuple, list)	):
											# Previous feature extractor had size in (width, height) format
											_a  :   Optional[int]				 =							size[::-1]
									_a  :   Tuple				 =							get_size_dict(_UpperCAmelCase	)
									_a  :   Optional[int]				 =							resample if resample is not None else self.resample
									_a  :   Union[str, Any]				 =							do_thumbnail if do_thumbnail is not None else self.do_thumbnail
									_a  :   int				 =							do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
									_a  :   Optional[Any]				 =							do_pad if do_pad is not None else self.do_pad
									_a  :   str				 =							do_rescale if do_rescale is not None else self.do_rescale
									_a  :   Dict				 =							rescale_factor if rescale_factor is not None else self.rescale_factor
									_a  :   Dict				 =							do_normalize if do_normalize is not None else self.do_normalize
									_a  :   Optional[int]				 =							image_mean if image_mean is not None else self.image_mean
									_a  :   Dict				 =							image_std if image_std is not None else self.image_std

									_a  :   Optional[Any]				 =							make_list_of_images(_UpperCAmelCase	)

									if not valid_images(_UpperCAmelCase	):
											raise ValueError(
											    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
											    'torch.Tensor, tf.Tensor or jax.ndarray.'	)

									if do_resize and size is None:
											raise ValueError('Size must be specified if do_resize is True.'	)

									if do_rescale and rescale_factor is None:
											raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

									if do_pad and size is None:
											raise ValueError('Size must be specified if do_pad is True.'	)

									if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('Image mean and std must be specified if do_normalize is True.'	)

									# All transformations expect numpy arrays.
									_a  :   Dict				 =							[to_numpy_array(_UpperCAmelCase	) for image in images]

									if do_align_long_axis:
											_a  :   Optional[Any]				 =							[self.align_long_axis(_UpperCAmelCase			,size=_UpperCAmelCase	) for image in images]

									if do_resize:
											_a  :   List[Any]				 =							[self.resize(image=_UpperCAmelCase			,size=_UpperCAmelCase			,resample=_UpperCAmelCase	) for image in images]

									if do_thumbnail:
											_a  :   Dict				 =							[self.thumbnail(image=_UpperCAmelCase			,size=_UpperCAmelCase	) for image in images]

									if do_pad:
											_a  :   List[str]				 =							[self.pad_image(image=_UpperCAmelCase			,size=_UpperCAmelCase			,random_padding=_UpperCAmelCase	) for image in images]

									if do_rescale:
											_a  :   List[str]				 =							[self.rescale(image=_UpperCAmelCase			,scale=_UpperCAmelCase	) for image in images]

									if do_normalize:
											_a  :   List[str]				 =							[self.normalize(image=_UpperCAmelCase			,mean=_UpperCAmelCase			,std=_UpperCAmelCase	) for image in images]

									_a  :   Optional[Any]				 =							[to_channel_dimension_format(_UpperCAmelCase			,_UpperCAmelCase	) for image in images]

									_a  :   List[Any]				 =							{'pixel_values': images}
									return BatchFeature(data=_UpperCAmelCase			,tensor_type=_UpperCAmelCase	)




