

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_UpperCAmelCase		:       Dict						    =	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							if isinstance(UpperCamelCase__       ,							torch.Tensor				):
														return image
							elif isinstance(UpperCamelCase__       ,							PIL.Image.Image				):
														snake_case_          =   [image]

							snake_case_          =   [trans(img.convert('RGB'				)				) for img in image]
							snake_case_          =   torch.stack(UpperCamelCase__				)
							return image







class 			lowercase    (	lowercase_      ):




				def __init__( self				,       snake_case				,       snake_case	):
											super().__init__()

											# make sure scheduler can always be converted to DDIM
											snake_case_          =   DDIMScheduler.from_config(scheduler.config	)

											self.register_modules(unet=snake_case				,       scheduler=snake_case	)




				def    a							( self				,       snake_case	):
											if strength < 0 or strength > 1:
																		raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case	):
											# get the original timestep using init_timestep
											snake_case_          =   min(int(num_inference_steps * strength	)				,       snake_case	)

											snake_case_          =   max(num_inference_steps - init_timestep				,       0	)
											snake_case_          =   self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=None	):
											if not isinstance(snake_case				,       (torch.Tensor, PIL.Image.Image, list)	):
																		raise ValueError(
																		    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case	)}'''	)

											snake_case_          =   image.to(device=snake_case				,       dtype=snake_case	)

											if isinstance(snake_case				,       snake_case	) and len(snake_case	) != batch_size:
																		raise ValueError(
																		    F'''You have passed a list of generators of length {len(snake_case	)}, but requested an effective batch'''
																		    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

											snake_case_          =   init_latents.shape
											snake_case_          =   randn_tensor(snake_case				,       generator=snake_case				,       device=snake_case				,       dtype=snake_case	)

											# get latents
											print('add noise to latents at timestep'				,       snake_case	)
											snake_case_          =   self.scheduler.add_noise(snake_case				,       snake_case				,       snake_case	)
											snake_case_          =   init_latents

											return latents




				@torch.no_grad()
				def __call__( self				,       snake_case = None				,       snake_case = 0.8				,       snake_case = 1				,       snake_case = None				,       snake_case = 0.0				,       snake_case = 50				,       snake_case = None				,       snake_case = "pil"				,       snake_case = True				,       ):
											self.check_inputs(snake_case	)

											# 2. Preprocess image
											snake_case_          =   preprocess(snake_case	)

											# 3. set timesteps
											self.scheduler.set_timesteps(snake_case				,       device=self.device	)
											snake_case_    ,   snake_case_          =   self.get_timesteps(snake_case				,       snake_case				,       self.device	)
											snake_case_          =   timesteps[:1].repeat(snake_case	)

											# 4. Prepare latent variables
											snake_case_          =   self.prepare_latents(snake_case				,       snake_case				,       snake_case				,       self.unet.dtype				,       self.device				,       snake_case	)
											snake_case_          =   latents

											# 5. Denoising loop
											for t in self.progress_bar(snake_case	):
																		# 1. predict noise model_output
																		snake_case_          =   self.unet(snake_case				,       snake_case	).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		snake_case_          =   self.scheduler.step(
																		    snake_case				,       snake_case				,       snake_case				,       eta=snake_case				,       use_clipped_model_output=snake_case				,       generator=snake_case				,       ).prev_sample

											snake_case_          =   (image / 2 + 0.5).clamp(0				,       1	)
											snake_case_          =   image.cpu().permute(0				,       2				,       3				,       1	).numpy()
											if output_type == "pil":
																		snake_case_          =   self.numpy_to_pil(snake_case	)

											if not return_dict:
																		return (image, latent_timestep.item())

											return ImagePipelineOutput(images=snake_case	)







# Lint as: python3
import itertools
import os
import re


_UpperCAmelCase		:       str						    =	re.compile(R"""([A-Z]+)([A-Z][a-z])""")
_UpperCAmelCase		:       Dict						    =	re.compile(R"""([a-z\d])([A-Z])""")

_UpperCAmelCase		:       Dict						    =	re.compile(R"""(?<!_)_(?!_)""")
_UpperCAmelCase		:       Tuple						    =	re.compile(R"""(_{2,})""")

_UpperCAmelCase		:       Any						    =	R"""^\w+(\.\w+)*$"""

_UpperCAmelCase		:       List[str]						    =	R"""<>:/\|?*"""





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   _uppercase_uppercase_re.sub(r'\1_\2'       ,							UpperCamelCase__				)
							snake_case_          =   _lowercase_uppercase_re.sub(r'\1_\2'       ,							UpperCamelCase__				)
							return name.lower()





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   _single_underscore_re.split(UpperCamelCase__				)
							snake_case_          =   [_multiple_underscores_re.split(UpperCamelCase__				) for n in name]
							return "".join(n.capitalize() for n in itertools.chain.from_iterable(UpperCamelCase__				) if n != ''				)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							if os.path.basename(UpperCamelCase__				) != name:
														raise ValueError(F'''Should be a dataset name, not a path: {name}'''				)
							return camelcase_to_snakecase(UpperCamelCase__				)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if os.path.basename(UpperCamelCase__				) != name:
														raise ValueError(F'''Should be a dataset name, not a path: {name}'''				)
							if not re.match(_split_re       ,							UpperCamelCase__				):
														raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''				)
							return F'''{filename_prefix_for_name(UpperCamelCase__				)}-{split}'''





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None				):







							'''simple docstring'''






							snake_case_          =   filename_prefix_for_split(UpperCamelCase__       ,							UpperCamelCase__				)
							if filetype_suffix:
														prefix += F'''.{filetype_suffix}'''
							snake_case_          =   os.path.join(UpperCamelCase__       ,							UpperCamelCase__				)
							return F'''{filepath}*'''





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None				):







							'''simple docstring'''






							snake_case_          =   filename_prefix_for_split(UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   os.path.join(UpperCamelCase__       ,							UpperCamelCase__				)

							if shard_lengths:
														snake_case_          =   len(UpperCamelCase__				)
														snake_case_          =   [F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(UpperCamelCase__				)]
														if filetype_suffix:
																					snake_case_          =   [filename + F'''.{filetype_suffix}''' for filename in filenames]
														return filenames
							else:
														snake_case_          =   prefix
														if filetype_suffix:
																					filename += F'''.{filetype_suffix}'''
														return [filename]




