






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







class    lowercase__			(							snake_case__   ):


			def __init__(      self     :  int   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  Any   ,       snake_case__     :  int   ,       ):
										super().__init__()
										self.register_modules(vqvae=__lowerCamelCase   ,       unet=__lowerCamelCase   ,       scheduler=__lowerCamelCase					)


			@torch.no_grad()
			def __call__(      self     :  Tuple   ,       snake_case__     :  str = None   ,       snake_case__     :  int = 1   ,       snake_case__     :  str = 100   ,       snake_case__     :  Any = 0.0   ,       snake_case__     :  Optional[Any] = None   ,       snake_case__     :  Optional[Any] = "pil"   ,       snake_case__     :  Dict = True   ,       ):
										if isinstance(__lowerCamelCase   ,       PIL.Image.Image					):
																	lowerCamelCase_       :							Tuple									=1
										elif isinstance(__lowerCamelCase   ,       torch.Tensor					):
																	lowerCamelCase_       :							Union[str, Any]									=image.shape[0]
										else:
																	raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase					)}"""					)

										if isinstance(__lowerCamelCase   ,       PIL.Image.Image					):
																	lowerCamelCase_       :							Union[str, Any]									=preprocess(__lowerCamelCase					)

										lowerCamelCase_       :							Union[str, Any]									=image.shape[-2:]

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

										lowerCamelCase_       :							Union[str, Any]									=randn_tensor(__lowerCamelCase   ,       generator=__lowerCamelCase   ,       device=self.device   ,       dtype=__lowerCamelCase					)

										lowerCamelCase_       :							List[Any]									=image.to(device=self.device   ,       dtype=__lowerCamelCase					)

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

										# scale the initial noise by the standard deviation required by the scheduler
										lowerCamelCase_       :							List[str]									=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_       :							str									="eta" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
										lowerCamelCase_       :							Optional[int]									={}
										if accepts_eta:
																	lowerCamelCase_       :							List[Any]									=eta

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

										# decode the image latents with the VQVAE
										lowerCamelCase_       :							Union[str, Any]									=self.vqvae.decode(__lowerCamelCase					).sample
										lowerCamelCase_       :							Dict									=torch.clamp(__lowerCamelCase   ,       -1.0   ,       1.0					)
										lowerCamelCase_       :							Tuple									=image / 2 + 0.5
										lowerCamelCase_       :							int									=image.cpu().permute(0   ,       2   ,       3   ,       1					).numpy()

										if output_type == "pil":
																	lowerCamelCase_       :							Optional[int]									=self.numpy_to_pil(__lowerCamelCase					)

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=__lowerCamelCase					)







import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCAmelCase__      		= object()

# For specifying empty leaf dict `{}`
lowerCAmelCase__      		= object()
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Union[str, Any]						):
       _A      :       str				    = tuple((re.compile(x + "$"						) for x in qs)						)
       for i in range(len(UpperCamelCase__						) - len(UpperCamelCase__						) + 1						):
              _A      :       Tuple				    = [x.match(UpperCamelCase__						) for x, y in zip(UpperCamelCase__				,							ks[i:]						)]
              if matches and all(UpperCamelCase__						):
                     return True
       return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       def replace(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[int]						):
              for rule, replacement in rules:
                     if _match(UpperCamelCase__				,							UpperCamelCase__						):
                            return replacement
              return val

       return replace
def 							_UpperCAmelCase				():
       return [
           # embeddings
           (("transformer", "wpe", "embedding"), P("mp"				,							UpperCamelCase__						)),
           (("transformer", "wte", "embedding"), P("mp"				,							UpperCamelCase__						)),
           # atention
           (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("attention", "out_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("attention", "out_proj", "bias"), None),
           # mlp
           (("mlp", "c_fc", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("mlp", "c_fc", "bias"), P("mp"						)),
           (("mlp", "c_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("mlp", "c_proj", "bias"), None),
           # layer norms
           ((r"ln_\d+", "bias"), None),
           ((r"\d+", r"ln_\d+", "scale"), None),
           (("ln_f", "bias"), None),
           (("ln_f", "scale"), None),
       ]






def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       _A      :       int				    = _get_partition_rules()
       _A      :       Optional[int]				    = _replacement_rules(UpperCamelCase__						)
       _A      :       Optional[int]				    = {k: _unmatched for k in flatten_dict(UpperCamelCase__						)}
       _A      :       List[str]				    = {k: replace(UpperCamelCase__				,							UpperCamelCase__						) for k, v in initd.items()}
       assert _unmatched not in result.values(), "Incomplete partition spec."
       return freeze(unflatten_dict(UpperCamelCase__						)						)
