


import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


UpperCamelCase					:       Any			    =     {
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}



def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a			,				__a			,				__a      )	->   int:
      if got_ver is None or want_ver is None:
            raise ValueError(
                f'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
                f''' reinstalling {pkg}.'''      )
      if not ops[op](version.parse(__a      )			,				version.parse(__a      )      ):
            raise ImportError(
                f'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'''      )



def UpperCamelCase_				(				__a			,				__a = None      )	->   None:
      a__	:  Optional[int]     =   f'''\n{hint}''' if hint is not None else ""

      # non-versioned check
      if re.match(R"^[\w_\-\d]+$"			,				__a      ):
            a__,	a__,	a__	:  Dict     =   requirement, None, None
      else:
            a__	:  Any     =   re.findall(R"^([^!=<>\s]+)([\s!=<>]{1,2}.+)"			,				__a      )
            if not match:
                  raise ValueError(
                      "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
                      f''' got {requirement}'''      )
            a__,	a__	:  Optional[Any]     =   match[0]
            a__	:  str     =   want_full.split(","      )  # there could be multiple requirements
            a__	:  Tuple     =   {}
            for w in want_range:
                  a__	:  List[Any]     =   re.findall(R"^([\s!=<>]{1,2})(.+)"			,				__a      )
                  if not match:
                        raise ValueError(
                            "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
                            f''' but got {requirement}'''      )
                  a__,	a__	:  Tuple     =   match[0]
                  a__	:  Optional[int]     =   want_ver
                  if op not in ops:
                        raise ValueError(f'''{requirement}: need one of {list(ops.keys()      )}, but got {op}'''      )

    # special case
      if pkg == "python":
            a__	:  Union[str, Any]     =   ".".join([str(__a      ) for x in sys.version_info[:3]]      )
            for op, want_ver in wanted.items():
                  _compare_versions(__a			,				__a			,				__a			,				__a			,				__a			,				__a      )
            return

      # check if any version is installed
      try:
            a__	:  Optional[int]     =   importlib.metadata.version(__a      )
      except importlib.metadata.PackageNotFoundError:
            raise importlib.metadata.PackageNotFoundError(
                f'''The \'{requirement}\' distribution was not found and is required by this application. {hint}'''      )

      # check that the right version is installed if version number or a range was provided
      if want_ver is not None:
            for op, want_ver in wanted.items():
                  _compare_versions(__a			,				__a			,				__a			,				__a			,				__a			,				__a      )



def UpperCamelCase_				(				__a      )	->   str:
      a__	:  Dict     =   "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
      return require_version(__a			,				__a      )





"""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     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Union[str, Any]:


    """simple docstring"""





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







class _lowerCAmelCase							( snake_case_      ):







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

       '''simple docstring'''




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







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

       '''simple docstring'''




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

       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Optional[int]    =       preprocess(UpperCamelCase__		)

       snake_case   ,snake_case       : List[str]    =       image.shape[-2:]

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

       snake_case       : int    =       randn_tensor(UpperCamelCase__		,      generator=UpperCamelCase__		,      device=self.device		,      dtype=UpperCamelCase__		)

       snake_case       : List[Any]    =       image.to(device=self.device		,      dtype=UpperCamelCase__		)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(UpperCamelCase__		,      device=self.device		)
       snake_case       : Optional[Any]    =       self.scheduler.timesteps

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

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

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

       if output_type == "pil":
           snake_case       : Union[str, Any]    =       self.numpy_to_pil(UpperCamelCase__		)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__		)





