





"""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 		__lowercase					(       snake_case_  :  Optional[int]						)       ->Optional[Any]:



    '''simple docstring'''







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






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						):



     '''simple docstring'''




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







 @torch.no_grad()
 def __call__(   self ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 100 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						):



     '''simple docstring'''




     if isinstance(__lowerCamelCase ,						PIL.Image.Image  ):
         __A   :						List[str]									=					1
     elif isinstance(__lowerCamelCase ,						torch.Tensor  ):
         __A   :						Optional[int]									=					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  ):
         __A   :						Optional[int]									=					preprocess(__lowerCamelCase  )

     __A			,	__A   :						Tuple									=					image.shape[-2:]

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

     __A   :						Tuple									=					randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=self.device ,						dtype=__lowerCamelCase  )

     __A   :						List[str]									=					image.to(device=self.device ,						dtype=__lowerCamelCase  )

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

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

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

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

     if output_type == "pil":
         __A   :						Optional[Any]									=					self.numpy_to_pil(__lowerCamelCase  )

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=__lowerCamelCase  )











"""simple docstring"""



from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					""""""
 _lowerCamelCase				=					"""hf-legacy"""  # "hf://"" is reserved for hffs

 def __init__(   self ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(self ,						**__lowerCamelCase  )
     __A   :						int									=					repo_info
     __A   :						Optional[int]									=					token
     __A   :						int									=					None

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if self.dir_cache is None:
         __A   :						int									=					{}
         for hf_file in self.repo_info.siblings:
             # TODO(QL): add sizes
             __A   :						Tuple									=					{
                 '''name''': hf_file.rfilename,
                 '''size''': None,
                 '''type''': '''file''',
             }
             self.dir_cache.update(
                 {
                     str(__lowerCamelCase  ): {'''name''': str(__lowerCamelCase  ), '''size''': None, '''type''': '''directory'''}
                     for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
                 }  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = "rb" ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     if not isinstance(self.repo_info ,						__lowerCamelCase  ):
         raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""  )
     __A   :						Union[str, Any]									=					hf_hub_url(self.repo_info.id ,						__lowerCamelCase ,						revision=self.repo_info.sha  )
     return fsspec.open(
         __lowerCamelCase ,						mode=__lowerCamelCase ,						headers=get_authentication_headers_for_url(__lowerCamelCase ,						use_auth_token=self.token  ) ,						client_kwargs={'''trust_env''': True} ,						).open()

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




     self._get_dirs()
     __A   :						Optional[Any]									=					self._strip_protocol(__lowerCamelCase  )
     if path in self.dir_cache:
         return self.dir_cache[path]
     else:
         raise FileNotFoundError(__lowerCamelCase  )







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase=False ,						**__lowerCamelCase  ):



     '''simple docstring'''




     self._get_dirs()
     __A   :						Any									=					PurePosixPath(path.strip('''/'''  )  )
     __A   :						Any									=					{}
     for p, f in self.dir_cache.items():
         __A   :						List[Any]									=					PurePosixPath(p.strip('''/'''  )  )
         __A   :						Dict									=					p.parent
         if root == path:
             __A   :						Union[str, Any]									=					f
     __A   :						List[str]									=					list(paths.values()  )
     if detail:
         return out
     else:
         return sorted(f['''name'''] for f in out  )




