

'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			List[Any]:
					lowerCamelCase_				,lowerCamelCase_  				=      image.size
					lowerCamelCase_				,lowerCamelCase_  				=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
					lowerCamelCase_  				=      image.resize((w, h)    ,resample=PIL_INTERPOLATION['lanczos']	)
					lowerCamelCase_  				=      np.array(__UpperCamelCase	).astype(np.floataa	) / 255.0
					lowerCamelCase_  				=      image[None].transpose(0    ,3    ,1    ,2	)
					lowerCamelCase_  				=      torch.from_numpy(__UpperCamelCase	)
					return 2.0 * image - 1.0







class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			)  ->     Optional[Any]:






							'''simple docstring'''



							super().__init__()
							self.register_modules(vqvae=SCREAMING_SNAKE_CASE_   ,			unet=SCREAMING_SNAKE_CASE_   ,			scheduler=SCREAMING_SNAKE_CASE_		)






		@torch.no_grad()
		def __call__(					self   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = 1   ,			SCREAMING_SNAKE_CASE_ = 100   ,			SCREAMING_SNAKE_CASE_ = 0.0   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = "pil"   ,			SCREAMING_SNAKE_CASE_ = True   ,			)  ->     Union[Tuple, ImagePipelineOutput]:






							'''simple docstring'''



							if isinstance(SCREAMING_SNAKE_CASE_   ,			PIL.Image.Image		):
												lowerCamelCase_  				=      1
							elif isinstance(SCREAMING_SNAKE_CASE_   ,			torch.Tensor		):
												lowerCamelCase_  				=      image.shape[0]
							else:
												raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(SCREAMING_SNAKE_CASE_		)}'''		)

							if isinstance(SCREAMING_SNAKE_CASE_   ,			PIL.Image.Image		):
												lowerCamelCase_  				=      preprocess(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_				,lowerCamelCase_  				=      image.shape[-2:]

							# in_channels should be 6: 3 for latents, 3 for low resolution image
							lowerCamelCase_  				=      (batch_size, self.unet.config.in_channels // 2, height, width)
							lowerCamelCase_  				=      next(self.unet.parameters()		).dtype

							lowerCamelCase_  				=      randn_tensor(SCREAMING_SNAKE_CASE_   ,			generator=SCREAMING_SNAKE_CASE_   ,			device=self.device   ,			dtype=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      image.to(device=self.device   ,			dtype=SCREAMING_SNAKE_CASE_		)

							# set timesteps and move to the correct device
							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_   ,			device=self.device		)
							lowerCamelCase_  				=      self.scheduler.timesteps

							# scale the initial noise by the standard deviation required by the scheduler
							lowerCamelCase_  				=      latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							lowerCamelCase_  				=      'eta' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
							lowerCamelCase_  				=      {}
							if accepts_eta:
												lowerCamelCase_  				=      eta

							for t in self.progress_bar(SCREAMING_SNAKE_CASE_		):
												# concat latents and low resolution image in the channel dimension.
												lowerCamelCase_  				=      torch.cat([latents, image]   ,			dim=1		)
												lowerCamelCase_  				=      self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
												# predict the noise residual
												lowerCamelCase_  				=      self.unet(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		).sample
												# compute the previous noisy sample x_t -> x_t-1
												lowerCamelCase_  				=      self.scheduler.step(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		).prev_sample

							# decode the image latents with the VQVAE
							lowerCamelCase_  				=      self.vqvae.decode(SCREAMING_SNAKE_CASE_		).sample
							lowerCamelCase_  				=      torch.clamp(SCREAMING_SNAKE_CASE_   ,			-1.0   ,			1.0		)
							lowerCamelCase_  				=      image / 2 + 0.5
							lowerCamelCase_  				=      image.cpu().permute(0   ,			2   ,			3   ,			1		).numpy()

							if output_type == "pil":
												lowerCamelCase_  				=      self.numpy_to_pil(SCREAMING_SNAKE_CASE_		)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_		)










'''simple docstring'''




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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''fnet'''


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			List[str]=3_20_00      ,						lowerCamelCase_				:			List[Any]=7_68      ,						lowerCamelCase_				:			Union[str, Any]=12      ,						lowerCamelCase_				:			Optional[Any]=30_72      ,						lowerCamelCase_				:			Optional[Any]="gelu_new"      ,						lowerCamelCase_				:			Any=0.1      ,						lowerCamelCase_				:			Union[str, Any]=5_12      ,						lowerCamelCase_				:			Optional[int]=4      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			str=1e-12      ,						lowerCamelCase_				:			Any=False      ,						lowerCamelCase_				:			str=5_12      ,						lowerCamelCase_				:			str=3      ,						lowerCamelCase_				:			int=1      ,						lowerCamelCase_				:			Optional[Any]=2      ,						**lowerCamelCase_				:			Optional[int]      ,						):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_      ,						bos_token_id=lowerCamelCase_      ,						eos_token_id=lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			max_position_embeddings
							SCREAMING_SNAKE_CASE :				str		=			hidden_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			intermediate_size
							SCREAMING_SNAKE_CASE :				int		=			hidden_act
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			initializer_range
							SCREAMING_SNAKE_CASE :				List[Any]		=			type_vocab_size
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				int		=			use_tpu_fourier_optimizations
							SCREAMING_SNAKE_CASE :				List[Any]		=			tpu_short_seq_length



