



'''simple docstring'''





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


__a          =		transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      if isinstance(_lowerCAmelCase     ,   torch.Tensor		):
            return image
      elif isinstance(_lowerCAmelCase     ,   PIL.Image.Image		):
            snake_case__  :    List[Any]    						=      [image]

      snake_case__  :    str    						=      [trans(img.convert("""RGB"""		)		) for img in image]
      snake_case__  :    List[Any]    						=      torch.stack(_lowerCAmelCase		)
      return image







class     UpperCAmelCase_       (       A__	):
    """simple docstring"""







    def __init__(    self					:     Union[str, Any] ,     snake_case_					:     int ,     snake_case_					:     List[Any]						):
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          snake_case__  :    Union[str, Any]    						=      DDIMScheduler.from_config(scheduler.config						)

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




    def lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     Union[str, Any]						):
          if strength < 0 or strength > 1:
                raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}"						)




    def lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     List[str] ,     snake_case_					:     Any ,     snake_case_					:     List[Any]						):
          # get the original timestep using init_timestep
          snake_case__  :    Optional[int]    						=      min(int(num_inference_steps * strength						) ,     __snake_case						)

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

          return timesteps, num_inference_steps - t_start




    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     List[str] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Tuple ,     snake_case_					:     str=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__  :    Optional[Any]    						=      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__  :    Optional[int]    						=      init_latents.shape
          snake_case__  :    Tuple    						=      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__  :    List[Any]    						=      self.scheduler.add_noise(__snake_case ,     __snake_case ,     __snake_case						)
          snake_case__  :    Any    						=      init_latents

          return latents




    @torch.no_grad()
    def __call__(    self					:     Union[str, Any] ,     snake_case_					:     Union[torch.FloatTensor, PIL.Image.Image] = None ,     snake_case_					:     float = 0.8 ,     snake_case_					:     int = 1 ,     snake_case_					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None ,     snake_case_					:     float = 0.0 ,     snake_case_					:     int = 50 ,     snake_case_					:     Optional[bool] = None ,     snake_case_					:     Optional[str] = "pil" ,     snake_case_					:     bool = True ,     ):
          self.check_inputs(__snake_case						)

          # 2. Preprocess image
          snake_case__  :    int    						=      preprocess(__snake_case						)

          # 3. set timesteps
          self.scheduler.set_timesteps(__snake_case ,     device=self.device						)
          snake_case__  :    Union[str, Any]    						=      self.get_timesteps(__snake_case ,     __snake_case ,     self.device						)
          snake_case__  :    List[Any]    						=      timesteps[:1].repeat(__snake_case						)

          # 4. Prepare latent variables
          snake_case__  :    Union[str, Any]    						=      self.prepare_latents(__snake_case ,     __snake_case ,     __snake_case ,     self.unet.dtype ,     self.device ,     __snake_case						)
          snake_case__  :    Dict    						=      latents

          # 5. Denoising loop
          for t in self.progress_bar(__snake_case						):
                # 1. predict noise model_output
                snake_case__  :    List[Any]    						=      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__  :    Any    						=      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__  :    Optional[int]    						=      (image / 2 + 0.5).clamp(0 ,     1						)
          snake_case__  :    List[Any]    						=      image.cpu().permute(0 ,     2 ,     3 ,     1						).numpy()
          if output_type == "pil":
                snake_case__  :    Dict    						=      self.numpy_to_pil(__snake_case						)

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

          return ImagePipelineOutput(images=__snake_case						)





'''simple docstring'''






import argparse
import os
import re

import packaging.version


UpperCamelCase__:   Union[str, Any]								=			"examples/"
UpperCamelCase__:   Optional[Any]								=			{
    "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
    "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
UpperCamelCase__:   Optional[int]								=			{
    "init": "src/diffusers/__init__.py",
    "setup": "setup.py",
}
UpperCamelCase__:   List[Any]								=			"README.md"


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						int  )      ->   Optional[int]:
							with open(_lowerCAmelCase   ,      '''r'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														UpperCAmelCase :       Optional[int] 							=		f.read()
							UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		REPLACE_PATTERNS[pattern]
							UpperCAmelCase :       List[Any] 							=		replace.replace('''VERSION'''   ,      _lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		re_pattern.sub(_lowerCAmelCase   ,      _lowerCAmelCase  )
							with open(_lowerCAmelCase   ,      '''w'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														f.write(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Any  )      ->   Optional[int]:
							for folder, directories, fnames in os.walk(_lowerCAmelCase  ):
														# Removing some of the folders with non-actively maintained examples from the walk
														if "research_projects" in directories:
																					directories.remove('''research_projects'''  )
														if "legacy" in directories:
																					directories.remove('''legacy'''  )
														for fname in fnames:
																					if fname.endswith('''.py'''  ):
																												update_version_in_file(os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )   ,      _lowerCAmelCase   ,      pattern='''examples'''  )


def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						str=False  )      ->   List[str]:
							for pattern, fname in REPLACE_FILES.items():
														update_version_in_file(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
							if not patch:
														update_version_in_examples(_lowerCAmelCase  )


def  snake_case_     (					)      ->   Optional[Any]:
							UpperCAmelCase :       Optional[int] 							=		'''🤗 Transformers currently provides the following architectures'''
							UpperCAmelCase :       Optional[int] 							=		'''1. Want to contribute a new model?'''
							with open(_lowerCAmelCase   ,      '''r'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														UpperCAmelCase :       Optional[Any] 							=		f.readlines()

							# Find the start of the list.
							UpperCAmelCase :       List[Any] 							=		0
							while not lines[start_index].startswith(_start_prompt  ):
														start_index += 1
							start_index += 1

							UpperCAmelCase :       Optional[Any] 							=		start_index
							# Update the lines in the model list.
							while not lines[index].startswith(_end_prompt  ):
														if lines[index].startswith('''1.'''  ):
																					UpperCAmelCase :       Optional[int] 							=		lines[index].replace(
																					    '''https://huggingface.co/docs/diffusers/main/model_doc'''   ,      '''https://huggingface.co/docs/diffusers/model_doc'''   ,      )
														index += 1

							with open(_lowerCAmelCase   ,      '''w'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														f.writelines(_lowerCAmelCase  )


def  snake_case_     (					)      ->   Optional[Any]:
							with open(REPLACE_FILES['''init''']   ,      '''r'''  ) as f:
														UpperCAmelCase :       Union[str, Any] 							=		f.read()
							UpperCAmelCase :       int 							=		REPLACE_PATTERNS['''init'''][0].search(_lowerCAmelCase  ).groups()[0]
							return packaging.version.parse(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]=False  )      ->   Any:
							UpperCAmelCase :       Optional[Any] 							=		get_version()
							if patch and default_version.is_devrelease:
														raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''  )
							if default_version.is_devrelease:
														UpperCAmelCase :       Optional[int] 							=		default_version.base_version
							elif patch:
														UpperCAmelCase :       Union[str, Any] 							=		f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
							else:
														UpperCAmelCase :       Union[str, Any] 							=		f"""{default_version.major}.{default_version.minor + 1}.0"""

							# Now let's ask nicely if that's the right one.
							UpperCAmelCase :       Dict 							=		input(f"""Which version are you releasing? [{default_version}]"""  )
							if len(_lowerCAmelCase  ) == 0:
														UpperCAmelCase :       Tuple 							=		default_version

							print(f"""Updating version to {version}."""  )
							global_version_update(_lowerCAmelCase   ,      patch=_lowerCAmelCase  )


def  snake_case_     (					)      ->   Any:
							UpperCAmelCase :       List[Any] 							=		get_version()
							UpperCAmelCase :       List[str] 							=		f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
							UpperCAmelCase :       List[Any] 							=		current_version.base_version

							# Check with the user we got that right.
							UpperCAmelCase :       Optional[int] 							=		input(f"""Which version are we developing now? [{dev_version}]"""  )
							if len(_lowerCAmelCase  ) == 0:
														UpperCAmelCase :       Dict 							=		dev_version

							print(f"""Updating version to {version}."""  )
							global_version_update(_lowerCAmelCase  )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
						UpperCamelCase__:   Union[str, Any]								=			argparse.ArgumentParser()
						parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
						parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
						UpperCamelCase__:   Optional[Any]								=			parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print("Nothing to do after a patch :-)")
						else:
												post_release_work()





