





'''simple docstring'''


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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class       UpperCamelCase__  (		__lowerCAmelCase     ):







 def __init__(				self			:   int , lowerCamelCase			:   int , lowerCamelCase			:   Any , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   str = None , ):




    '''simple docstring'''
    super().__init__()
    self.register_modules(transformer=UpperCamelCase_ , vae=UpperCamelCase_ , scheduler=UpperCamelCase_		)

    # create a imagenet -> id dictionary for easier use
    a__     = {}
    if idalabel is not None:
       for key, value in idalabel.items():
          for label in value.split(","		):
             a__     = int(UpperCamelCase_		)
       a__     = dict(sorted(self.labels.items()		)		)







 def 						__a   (				self			:   Any , lowerCamelCase			:   str		):




    '''simple docstring'''

    if not isinstance(UpperCamelCase_ , UpperCamelCase_		):
       a__     = list(UpperCamelCase_		)

    for l in label:
       if l not in self.labels:
          raise ValueError(
              F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''		)

    return [self.labels[l] for l in label]







 @torch.no_grad()
 def __call__(				self			:   Tuple , lowerCamelCase			:   int , lowerCamelCase			:   Optional[Any] = 4.0 , lowerCamelCase			:   Dict = None , lowerCamelCase			:   Tuple = 5_0 , lowerCamelCase			:   List[Any] = "pil" , lowerCamelCase			:   str = True , ):




    '''simple docstring'''
    a__     = len(UpperCamelCase_		)
    a__     = self.transformer.config.sample_size
    a__     = self.transformer.config.in_channels

    a__     = randn_tensor(
        shape=(batch_size, latent_channels, latent_size, latent_size) , generator=UpperCamelCase_ , device=self.device , dtype=self.transformer.dtype , )
    a__     = torch.cat([latents] * 2		) if guidance_scale > 1 else latents

    a__     = torch.tensor(UpperCamelCase_ , device=self.device		).reshape(-1		)
    a__     = torch.tensor([1_0_0_0] * batch_size , device=self.device		)
    a__     = torch.cat([class_labels, class_null] , 0		) if guidance_scale > 1 else class_labels

    # set step values
    self.scheduler.set_timesteps(UpperCamelCase_		)

    for t in self.progress_bar(self.scheduler.timesteps		):
       if guidance_scale > 1:
          a__     = latent_model_input[: len(UpperCamelCase_		) // 2]
          a__     = torch.cat([half, half] , dim=0		)
       a__     = self.scheduler.scale_model_input(UpperCamelCase_ , UpperCamelCase_		)

       a__     = t
       if not torch.is_tensor(UpperCamelCase_		):
          # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
          # This would be a good case for the `match` statement (Python 3.10+)
          a__     = latent_model_input.device.type == "mps"
          if isinstance(UpperCamelCase_ , UpperCamelCase_		):
             a__     = torch.floataa if is_mps else torch.floataa
          else:
             a__     = torch.intaa if is_mps else torch.intaa
          a__     = torch.tensor([timesteps] , dtype=UpperCamelCase_ , device=latent_model_input.device		)
       elif len(timesteps.shape		) == 0:
          a__     = timesteps[None].to(latent_model_input.device		)
       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       a__     = timesteps.expand(latent_model_input.shape[0]		)
       # predict noise model_output
       a__     = self.transformer(
           UpperCamelCase_ , timestep=UpperCamelCase_ , class_labels=UpperCamelCase_		).sample

       # perform guidance
       if guidance_scale > 1:
          a__     = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
          a__     = torch.split(UpperCamelCase_ , len(UpperCamelCase_		) // 2 , dim=0		)

          a__     = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
          a__     = torch.cat([half_eps, half_eps] , dim=0		)

          a__     = torch.cat([eps, rest] , dim=1		)

       # learned sigma
       if self.transformer.config.out_channels // 2 == latent_channels:
          a__     = torch.split(UpperCamelCase_ , UpperCamelCase_ , dim=1		)
       else:
          a__     = noise_pred

       # compute previous image: x_t -> x_t-1
       a__     = self.scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_		).prev_sample

    if guidance_scale > 1:
       a__     = latent_model_input.chunk(2 , dim=0		)
    else:
       a__     = latent_model_input

    a__     = 1 / self.vae.config.scaling_factor * latents
    a__     = self.vae.decode(UpperCamelCase_		).sample

    a__     = (samples / 2 + 0.5).clamp(0 , 1		)

    # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
    a__     = samples.cpu().permute(0 , 2 , 3 , 1		).float().numpy()

    if output_type == "pil":
       a__     = self.numpy_to_pil(UpperCamelCase_		)

    if not return_dict:
       return (samples,)

    return ImagePipelineOutput(images=UpperCamelCase_		)










'''simple docstring'''


import argparse
import os
import re

import packaging.version


lowerCAmelCase_   :   Optional[int]       =					"examples/"
lowerCAmelCase_   :   Optional[int]       =					{
    "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"),
}
lowerCAmelCase_   :   Optional[int]       =					{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
lowerCAmelCase_   :   List[Any]       =					"README.md"







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[Any]     )   ->    str:
			with open(__lowerCamelCase					,  "r"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						a__     = f.read()
			a__   ,				a__     = REPLACE_PATTERNS[pattern]
			a__     = replace.replace("VERSION"					,  __lowerCamelCase     )
			a__     = re_pattern.sub(__lowerCamelCase					,  __lowerCamelCase     )
			with open(__lowerCamelCase					,  "w"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						f.write(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]     )   ->    Dict:
			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    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     Any=False     )   ->    Optional[Any]:
			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
			if not patch:
						update_version_in_examples(__lowerCamelCase     )







def    _lowerCamelCase  ()   ->    Tuple:
			a__     = "🤗 Transformers currently provides the following architectures"
			a__     = "1. Want to contribute a new model?"
			with open(__lowerCamelCase					,  "r"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						a__     = f.readlines()

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

			a__     = start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt     ):
						if lines[index].startswith("1."     ):
									a__     = lines[index].replace(
									    "https://huggingface.co/docs/transformers/main/model_doc"					,  "https://huggingface.co/docs/transformers/model_doc"					,  )
						index += 1

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







def    _lowerCamelCase  ()   ->    Tuple:
			with open(REPLACE_FILES["init"]					,  "r"     ) as f:
						a__     = f.read()
			a__     = REPLACE_PATTERNS["init"][0].search(__lowerCamelCase     ).groups()[0]
			return packaging.version.parse(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Any=False     )   ->    int:
			a__     = 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:
						a__     = default_version.base_version
			elif patch:
						a__     = f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
			else:
						a__     = f'''{default_version.major}.{default_version.minor + 1}.0'''

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

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







def    _lowerCamelCase  ()   ->    Tuple:
			a__     = get_version()
			a__     = f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
			a__     = current_version.base_version

			# Check with the user we got that right.
			a__     = input(f'''Which version are we developing now? [{dev_version}]'''     )
			if len(__lowerCamelCase     ) == 0:
						a__     = 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__":
		lowerCAmelCase_   :   List[str]       =					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.")
		lowerCAmelCase_   :   int       =					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()



