

'''simple docstring'''

import argparse
import os
import re

import packaging.version


lowercase__         =      "examples/"
lowercase__         =      {
    "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"),
}
lowercase__         =      {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
lowercase__         =      "README.md"




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




def 				UpperCamelCase(					UpperCAmelCase_     ):
 for folder, directories, fnames in os.walk(UpperCAmelCase_     ):
  # 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(UpperCAmelCase_  ,	UpperCAmelCase_     )  ,	UpperCAmelCase_  ,	pattern='examples'     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False     ):
 for pattern, fname in REPLACE_FILES.items():
  update_version_in_file(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )
 if not patch:
  update_version_in_examples(UpperCAmelCase_     )




def 				UpperCamelCase(					):
 UpperCAmelCase	:     List[Any]						 =			'🤗 Transformers currently provides the following architectures'
 UpperCAmelCase	:     Union[str, Any]						 =			'1. Want to contribute a new model?'
 with open(UpperCAmelCase_  ,	'r'  ,	encoding='utf-8'  ,	newline='\n'     ) as f:
  UpperCAmelCase	:     str						 =			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	:     int						 =			start_index
 # Update the lines in the model list.
 while not lines[index].startswith(_end_prompt     ):
  if lines[index].startswith('1.'     ):
   UpperCAmelCase	:     int						 =			lines[index].replace(
       'https://huggingface.co/docs/transformers/main/model_doc'  ,	'https://huggingface.co/docs/transformers/model_doc'  ,	)
  index += 1

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




def 				UpperCamelCase(					):
 with open(REPLACE_FILES['init']  ,	'r'     ) as f:
  UpperCAmelCase	:     List[Any]						 =			f.read()
 UpperCAmelCase	:     str						 =			REPLACE_PATTERNS['init'][0].search(UpperCAmelCase_     ).groups()[0]
 return packaging.version.parse(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_=False     ):
 UpperCAmelCase	:     str						 =			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	:     Dict						 =			default_version.base_version
 elif patch:
  UpperCAmelCase	:     Any						 =			F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
 else:
  UpperCAmelCase	:     Any						 =			F"""{default_version.major}.{default_version.minor + 1}.0"""

 # Now let's ask nicely if that's the right one.
 UpperCAmelCase	:     Union[str, Any]						 =			input(F"""Which version are you releasing? [{default_version}]"""     )
 if len(UpperCAmelCase_     ) == 0:
  UpperCAmelCase	:     Tuple						 =			default_version

 print(F"""Updating version to {version}."""     )
 global_version_update(UpperCAmelCase_  ,	patch=UpperCAmelCase_     )
 if not patch:
  print('Cleaning main README, don\'t forget to run `make fix-copies`.'     )
  clean_main_ref_in_model_list()




def 				UpperCamelCase(					):
 UpperCAmelCase	:     int						 =			get_version()
 UpperCAmelCase	:     Optional[Any]						 =			F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
 UpperCAmelCase	:     Dict						 =			current_version.base_version

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

 print(F"""Updating version to {version}."""     )
 global_version_update(UpperCAmelCase_     )
 print('Cleaning main README, don\'t forget to run `make fix-copies`.'     )
 clean_main_ref_in_model_list()


if __name__ == "__main__":
 lowercase__         =      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.")
 lowercase__         =      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()







'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					VQModel					,      lowercase_			:					UNetaDModel					,      lowercase_			:					DDIMScheduler	)						->     int:
      super().__init__()
      self.register_modules(vqvae=lowercase_					,      unet=lowercase_					,      scheduler=lowercase_	)




     @torch.no_grad()
     def __call__(		self			:					str					,      lowercase_			:					int = 1					,      lowercase_			:					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      lowercase_			:					float = 0.0					,      lowercase_			:					int = 50					,      lowercase_			:					Optional[str] = "pil"					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     Union[Tuple, ImagePipelineOutput]:
      UpperCAmelCase	:     str						 =			randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      generator=lowercase_					,      )
      UpperCAmelCase	:     Any						 =			latents.to(self.device	)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase	:     Optional[Any]						 =			latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowercase_	)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      UpperCAmelCase	:     Optional[int]						 =			'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

      UpperCAmelCase	:     Tuple						 =			{}
      if accepts_eta:
       UpperCAmelCase	:     List[str]						 =			eta

      for t in self.progress_bar(self.scheduler.timesteps	):
       UpperCAmelCase	:     Dict						 =			self.scheduler.scale_model_input(lowercase_					,      lowercase_	)
       # predict the noise residual
       UpperCAmelCase	:     Dict						 =			self.unet(lowercase_					,      lowercase_	).sample
       # compute the previous noisy sample x_t -> x_t-1
       UpperCAmelCase	:     Dict						 =			self.scheduler.step(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	).prev_sample

      # decode the image latents with the VAE
      UpperCAmelCase	:     Any						 =			self.vqvae.decode(lowercase_	).sample

      UpperCAmelCase	:     Union[str, Any]						 =			(image / 2 + 0.5).clamp(0					,      1	)
      UpperCAmelCase	:     Optional[Any]						 =			image.cpu().permute(0					,      2					,      3					,      1	).numpy()
      if output_type == "pil":
       UpperCAmelCase	:     Tuple						 =			self.numpy_to_pil(lowercase_	)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowercase_	)




