




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCAmelCase_						=			logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase_						=			'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__=8   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       List[Any]       =			height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     lowercase :       Optional[int]       =			width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor






def  snake_case(    __magic_name__			,		__magic_name__=5_12			,		__magic_name__=5_12   )			->							Union[str, Any]:





     '''simple docstring'''


     lowercase :       int       =			pil_image.resize((w, h)			,		resample=Image.BICUBIC			,		reducing_gap=1   )
     lowercase :       Optional[int]       =			np.array(pil_image.convert('''RGB'''   )   )
     lowercase :       Tuple       =			arr.astype(np.floataa   ) / 1_2_7.5 - 1
     lowercase :       int       =			np.transpose(__magic_name__			,		[2, 0, 1]   )
     lowercase :       Optional[int]       =			torch.from_numpy(__magic_name__   ).unsqueeze(0   )
     return image



class 						_A (  _lowerCamelCase				):






  def __init__(  self		:     Tuple				,						_A		:     UNetaDConditionModel				,						_A		:     DDPMScheduler				,						_A		:     VQModel				,						)		->   int:






       """simple docstring"""







       super().__init__()

       self.register_modules(
           unet=_A				,						scheduler=_A				,						movq=_A				,						)
       lowercase :       List[str]       =			2 ** (len(self.movq.config.block_out_channels				) - 1)






  def  __a      (  self		:     List[Any]				,						_A		:     List[str]				,						_A		:     List[str]				,						_A		:     int				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       int       =			min(int(num_inference_steps * strength				)				,						_A				)

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

       return timesteps, num_inference_steps - t_start






  def  __a      (  self		:     int				,						_A		:     Dict				,						_A		:     Any				,						_A		:     Optional[int]				,						_A		:     List[str]				,						_A		:     List[Any]				,						_A		:     List[Any]				,						_A		:     Any=None				)		->   Optional[Any]:






       """simple docstring"""







       if not isinstance(_A				,						(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(_A				)}"""				)

       lowercase :       Optional[int]       =			image.to(device=_A				,						dtype=_A				)

       lowercase :       Any       =			batch_size * num_images_per_prompt

       if image.shape[1] == 4:
            lowercase :       List[Any]       =			image

       else:
            if isinstance(_A				,						_A				) and len(_A				) != batch_size:
                 raise ValueError(
                     f"""You have passed a list of generators of length {len(_A				)}, but requested an effective batch"""
                     f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

            elif isinstance(_A				,						_A				):
                 lowercase :       Optional[int]       =			[
                     self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(_A				)
                 ]
                 lowercase :       List[str]       =			torch.cat(_A				,						dim=0				)
            else:
                 lowercase :       Union[str, Any]       =			self.movq.encode(_A				).latent_dist.sample(_A				)

            lowercase :       List[str]       =			self.movq.config.scaling_factor * init_latents

       lowercase :       Optional[int]       =			torch.cat([init_latents]				,						dim=0				)

       lowercase :       Any       =			init_latents.shape
       lowercase :       Tuple       =			randn_tensor(_A				,						generator=_A				,						device=_A				,						dtype=_A				)

       # get latents
       lowercase :       Optional[int]       =			self.scheduler.add_noise(_A				,						_A				,						_A				)

       lowercase :       Tuple       =			init_latents

       return latents






  def  __a      (  self		:     Union[str, Any]				,						_A		:     Tuple=0				)		->   Union[str, Any]:






       """simple docstring"""







       if is_accelerate_available():
            from accelerate import cpu_offload
       else:
            raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

       lowercase :       Dict       =			torch.device(f"""cuda:{gpu_id}"""				)

       lowercase :       int       =			[
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                 cpu_offload(_A				,						_A				)






  def  __a      (  self		:     Any				,						_A		:     Union[str, Any]=0				)		->   Tuple:






       """simple docstring"""







       if is_accelerate_available() and is_accelerate_version('''>='''				,						'''0.17.0.dev0'''				):
            from accelerate import cpu_offload_with_hook
       else:
            raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''				)

       lowercase :       str       =			torch.device(f"""cuda:{gpu_id}"""				)

       if self.device.type != "cpu":
            self.to('''cpu'''				,						silence_dtype_warnings=_A				)
            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       lowercase :       List[str]       =			None
       for cpu_offloaded_model in [self.unet, self.movq]:
            lowercase      ,	lowercase :       int       =			cpu_offload_with_hook(_A				,						_A				,						prev_module_hook=_A				)

       # We'll offload the last model manually.
       lowercase :       str       =			hook






  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def  __a      (  self		:     Tuple				)		->   List[Any]:






       """simple docstring"""







       if not hasattr(self.unet				,						'''_hf_hook'''				):
            return self.device
       for module in self.unet.modules():
            if (
                hasattr(_A				,						'''_hf_hook'''				)
                and hasattr(module._hf_hook				,						'''execution_device'''				)
                and module._hf_hook.execution_device is not None
            ):
                 return torch.device(module._hf_hook.execution_device				)
       return self.device






  @torch.no_grad()
  @replace_example_docstring(_A				)
  def __call__(  self		:     Tuple				,						_A		:     Union[torch.FloatTensor, List[torch.FloatTensor]]				,						_A		:     Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]				,						_A		:     Union[torch.FloatTensor, List[torch.FloatTensor]]				,						_A		:     int = 512				,						_A		:     int = 512				,						_A		:     int = 100				,						_A		:     float = 4.0				,						_A		:     float = 0.3				,						_A		:     int = 1				,						_A		:     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						_A		:     Optional[str] = "pil"				,						_A		:     bool = True				,						)		->   str:






       """simple docstring"""







       lowercase :       Tuple       =			self._execution_device

       lowercase :       int       =			guidance_scale > 1.0

       if isinstance(_A				,						_A				):
            lowercase :       Any       =			torch.cat(_A				,						dim=0				)
       lowercase :       Tuple       =			image_embeds.shape[0]
       if isinstance(_A				,						_A				):
            lowercase :       Dict       =			torch.cat(_A				,						dim=0				)

       if do_classifier_free_guidance:
            lowercase :       List[str]       =			image_embeds.repeat_interleave(_A				,						dim=0				)
            lowercase :       Optional[Any]       =			negative_image_embeds.repeat_interleave(_A				,						dim=0				)

       lowercase :       Optional[Any]       =			torch.cat([negative_image_embeds, image_embeds]				,						dim=0				).to(dtype=self.unet.dtype				,						device=_A				)

       if not isinstance(_A				,						_A				):
            lowercase :       Union[str, Any]       =			[image]
       if not all(isinstance(_A				,						(PIL.Image.Image, torch.Tensor)				) for i in image				):
            raise ValueError(
                f"""Input is in incorrect format: {[type(_A				) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""				)

       lowercase :       Optional[int]       =			torch.cat([prepare_image(_A				,						_A				,						_A				) for i in image]				,						dim=0				)
       lowercase :       Any       =			image.to(dtype=image_embeds.dtype				,						device=_A				)

       lowercase :       List[str]       =			self.movq.encode(_A				)['''latents''']
       lowercase :       Optional[Any]       =			latents.repeat_interleave(_A				,						dim=0				)
       self.scheduler.set_timesteps(_A				,						device=_A				)
       lowercase      ,	lowercase :       Optional[Any]       =			self.get_timesteps(_A				,						_A				,						_A				)
       lowercase :       Optional[int]       =			timesteps[:1].repeat(batch_size * num_images_per_prompt				)
       lowercase      ,	lowercase :       Dict       =			downscale_height_and_width(_A				,						_A				,						self.movq_scale_factor				)
       lowercase :       Optional[int]       =			self.prepare_latents(
           _A				,						_A				,						_A				,						_A				,						image_embeds.dtype				,						_A				,						_A				)
       for i, t in enumerate(self.progress_bar(_A				)				):
            # expand the latents if we are doing classifier free guidance
            lowercase :       List[Any]       =			torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

            lowercase :       Union[str, Any]       =			{'''image_embeds''': image_embeds}
            lowercase :       Optional[Any]       =			self.unet(
                sample=_A				,						timestep=_A				,						encoder_hidden_states=_A				,						added_cond_kwargs=_A				,						return_dict=_A				,						)[0]

            if do_classifier_free_guidance:
                 lowercase      ,	lowercase :       Tuple       =			noise_pred.split(latents.shape[1]				,						dim=1				)
                 lowercase      ,	lowercase :       Optional[int]       =			noise_pred.chunk(2				)
                 lowercase      ,	lowercase :       str       =			variance_pred.chunk(2				)
                 lowercase :       Dict       =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 lowercase :       Optional[Any]       =			torch.cat([noise_pred, variance_pred_text]				,						dim=1				)

            if not (
                hasattr(self.scheduler.config				,						'''variance_type'''				)
                and self.scheduler.config.variance_type in ["learned", "learned_range"]
            ):
                 lowercase      ,	lowercase :       Any       =			noise_pred.split(latents.shape[1]				,						dim=1				)

            # compute the previous noisy sample x_t -> x_t-1
            lowercase :       Optional[int]       =			self.scheduler.step(
                _A				,						_A				,						_A				,						generator=_A				,						)[0]

       # post-processing
       lowercase :       Optional[int]       =			self.movq.decode(_A				,						force_not_quantize=_A				)['''sample''']

       if output_type not in ["pt", "np", "pil"]:
            raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""				)

       if output_type in ["np", "pil"]:
            lowercase :       Any       =			image * 0.5 + 0.5
            lowercase :       Optional[int]       =			image.clamp(0				,						1				)
            lowercase :       List[Any]       =			image.cpu().permute(0				,						2				,						3				,						1				).float().numpy()

       if output_type == "pil":
            lowercase :       Dict       =			self.numpy_to_pil(_A				)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=_A				)





import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


lowerCAmelCase_						=			get_logger(__name__)


lowerCAmelCase_						=			Path(__file__).parent / 'model_card_template.md'
lowerCAmelCase_						=			uuida().hex
lowerCAmelCase_						=			os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase_						=			os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase_						=			HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'






def  snake_case(    __magic_name__ = None   )			->							str:





     '''simple docstring'''


     lowercase :       List[Any]       =			F"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
     if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
          return ua + "; telemetry/off"
     if is_torch_available():
          ua += F"""; torch/{_torch_version}"""
     if is_flax_available():
          ua += F"""; jax/{_jax_version}"""
          ua += F"""; flax/{_flax_version}"""
     if is_onnx_available():
          ua += F"""; onnxruntime/{_onnxruntime_version}"""
     # CI will set this value to True
     if os.environ.get('''DIFFUSERS_IS_CI'''			,		''''''   ).upper() in ENV_VARS_TRUE_VALUES:
          ua += "; is_ci/true"
     if isinstance(__magic_name__			,		__magic_name__   ):
          ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()   )
     elif isinstance(__magic_name__			,		__magic_name__   ):
          ua += "; " + user_agent
     return ua






def  snake_case(    __magic_name__			,		__magic_name__ = None			,		__magic_name__ = None   )			->							Optional[Any]:





     '''simple docstring'''


     if token is None:
          lowercase :       int       =			HfFolder.get_token()
     if organization is None:
          lowercase :       List[str]       =			whoami(__magic_name__   )['''name''']
          return F"""{username}/{model_id}"""
     else:
          return F"""{organization}/{model_id}"""






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


     if not is_jinja_available():
          raise ValueError(
              '''Modelcard rendering is based on Jinja templates.'''
              ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
              ''' To install it, please run `pip install Jinja2`.'''   )

     if hasattr(__magic_name__			,		'''local_rank'''   ) and args.local_rank not in [-1, 0]:
          return

     lowercase :       Optional[Any]       =			args.hub_token if hasattr(__magic_name__			,		'''hub_token'''   ) else None
     lowercase :       int       =			get_full_repo_name(__magic_name__			,		token=__magic_name__   )

     lowercase :       Dict       =			ModelCard.from_template(
         card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
             language='''en'''			,		license='''apache-2.0'''			,		library_name='''diffusers'''			,		tags=[]			,		datasets=args.dataset_name			,		metrics=[]			,		)			,		template_path=__magic_name__			,		model_name=__magic_name__			,		repo_name=__magic_name__			,		dataset_name=args.dataset_name if hasattr(__magic_name__			,		'''dataset_name'''   ) else None			,		learning_rate=args.learning_rate			,		train_batch_size=args.train_batch_size			,		eval_batch_size=args.eval_batch_size			,		gradient_accumulation_steps=(
             args.gradient_accumulation_steps if hasattr(__magic_name__			,		'''gradient_accumulation_steps'''   ) else None
         )			,		adam_betaa=args.adam_betaa if hasattr(__magic_name__			,		'''adam_beta1'''   ) else None			,		adam_betaa=args.adam_betaa if hasattr(__magic_name__			,		'''adam_beta2'''   ) else None			,		adam_weight_decay=args.adam_weight_decay if hasattr(__magic_name__			,		'''adam_weight_decay'''   ) else None			,		adam_epsilon=args.adam_epsilon if hasattr(__magic_name__			,		'''adam_epsilon'''   ) else None			,		lr_scheduler=args.lr_scheduler if hasattr(__magic_name__			,		'''lr_scheduler'''   ) else None			,		lr_warmup_steps=args.lr_warmup_steps if hasattr(__magic_name__			,		'''lr_warmup_steps'''   ) else None			,		ema_inv_gamma=args.ema_inv_gamma if hasattr(__magic_name__			,		'''ema_inv_gamma'''   ) else None			,		ema_power=args.ema_power if hasattr(__magic_name__			,		'''ema_power'''   ) else None			,		ema_max_decay=args.ema_max_decay if hasattr(__magic_name__			,		'''ema_max_decay'''   ) else None			,		mixed_precision=args.mixed_precision			,		)

     lowercase :       Any       =			os.path.join(args.output_dir			,		'''README.md'''   )
     model_card.save(__magic_name__   )






def  snake_case(    __magic_name__			,		__magic_name__ = None   )			->							int:





     '''simple docstring'''


     if resolved_file is None or commit_hash is not None:
          return commit_hash
     lowercase :       Dict       =			str(Path(__magic_name__   ).as_posix()   )
     lowercase :       Any       =			re.search(r'''snapshots/([^/]+)/'''			,		__magic_name__   )
     if search is None:
          return None
     lowercase :       List[Any]       =			search.groups()[0]
     return commit_hash if REGEX_COMMIT_HASH.match(__magic_name__   ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowerCAmelCase_						=			os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
lowerCAmelCase_						=			os.path.join(hf_cache_home, 'diffusers')






def  snake_case(    __magic_name__ = None			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     if new_cache_dir is None:
          lowercase :       str       =			DIFFUSERS_CACHE
     if old_cache_dir is None:
          lowercase :       List[str]       =			old_diffusers_cache

     lowercase :       str       =			Path(__magic_name__   ).expanduser()
     lowercase :       Dict       =			Path(__magic_name__   ).expanduser()
     for old_blob_path in old_cache_dir.glob('''**/blobs/*'''   ):
          if old_blob_path.is_file() and not old_blob_path.is_symlink():
               lowercase :       List[Any]       =			new_cache_dir / old_blob_path.relative_to(__magic_name__   )
               new_blob_path.parent.mkdir(parents=__magic_name__			,		exist_ok=__magic_name__   )
               os.replace(__magic_name__			,		__magic_name__   )
               try:
                    os.symlink(__magic_name__			,		__magic_name__   )
               except OSError:
                    logger.warning(
                        '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'''   )
     # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


lowerCAmelCase_						=			os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
 lowerCAmelCase_						=			0
else:
 with open(cache_version_file) as f:
  try:
   lowerCAmelCase_						=			int(f.read())
  except ValueError:
   lowerCAmelCase_						=			0

if cache_version < 1:
 lowerCAmelCase_						=			os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
 if old_cache_is_not_empty:
  logger.warning(
      'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
      'existing cached models. This is a one-time operation, you can interrupt it or run it '
      'later by calling `diffusers.utils.hub_utils.move_cache()`.'
  )
  try:
   move_cache()
  except Exception as e:
   lowerCAmelCase_						=			'\n'.join(traceback.format_tb(e.__traceback__))
   logger.error(
       f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
       'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
       'message and we will do our best to help.'
   )

if cache_version < 1:
 try:
  os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
  with open(cache_version_file, 'w') as f:
   f.write('1')
 except Exception:
  logger.warning(
      f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
      'the directory exists and can be written to.'
  )






def  snake_case(    __magic_name__			,		__magic_name__ = None   )			->							str:





     '''simple docstring'''


     if variant is not None:
          lowercase :       List[str]       =			weights_name.split('''.'''   )
          lowercase :       Any       =			splits[:-1] + [variant] + splits[-1:]
          lowercase :       Tuple       =			'''.'''.join(__magic_name__   )

     return weights_name






def  snake_case(    __magic_name__			,		*,
    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__=None			,		)			->							Dict:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			str(__magic_name__   )
     if os.path.isfile(__magic_name__   ):
          return pretrained_model_name_or_path
     elif os.path.isdir(__magic_name__   ):
          if os.path.isfile(os.path.join(__magic_name__			,		__magic_name__   )   ):
               # Load from a PyTorch checkpoint
               lowercase :       Dict       =			os.path.join(__magic_name__			,		__magic_name__   )
               return model_file
          elif subfolder is not None and os.path.isfile(
              os.path.join(__magic_name__			,		__magic_name__			,		__magic_name__   )   ):
               lowercase :       str       =			os.path.join(__magic_name__			,		__magic_name__			,		__magic_name__   )
               return model_file
          else:
               raise EnvironmentError(
                   F"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."""   )
     else:
          # 1. First check if deprecated way of loading from branches is used
          if (
              revision in DEPRECATED_REVISION_ARGS
              and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
              and version.parse(version.parse(__magic_name__   ).base_version   ) >= version.parse('''0.20.0'''   )
          ):
               try:
                    lowercase :       int       =			hf_hub_download(
                        __magic_name__			,		filename=_add_variant(__magic_name__			,		__magic_name__   )			,		cache_dir=__magic_name__			,		force_download=__magic_name__			,		proxies=__magic_name__			,		resume_download=__magic_name__			,		local_files_only=__magic_name__			,		use_auth_token=__magic_name__			,		user_agent=__magic_name__			,		subfolder=__magic_name__			,		revision=revision or commit_hash			,		)
                    warnings.warn(
                        F"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."""			,		__magic_name__			,		)
                    return model_file
               except:  # noqa: E722
                    warnings.warn(
                        F"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__magic_name__			,		__magic_name__   )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(__magic_name__			,		__magic_name__   )}' so that the correct variant file can be added."""			,		__magic_name__			,		)
          try:
               # 2. Load model file as usual
               lowercase :       Dict       =			hf_hub_download(
                   __magic_name__			,		filename=__magic_name__			,		cache_dir=__magic_name__			,		force_download=__magic_name__			,		proxies=__magic_name__			,		resume_download=__magic_name__			,		local_files_only=__magic_name__			,		use_auth_token=__magic_name__			,		user_agent=__magic_name__			,		subfolder=__magic_name__			,		revision=revision or commit_hash			,		)
               return model_file

          except RepositoryNotFoundError:
               raise EnvironmentError(
                   F"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
                   '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
                   '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
                   '''login`.'''   )
          except RevisionNotFoundError:
               raise EnvironmentError(
                   F"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
                   '''this model name. Check the model page at '''
                   F"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."""   )
          except EntryNotFoundError:
               raise EnvironmentError(
                   F"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."""   )
          except HTTPError as err:
               raise EnvironmentError(
                   F"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"""   )
          except ValueError:
               raise EnvironmentError(
                   F"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
                   F""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
                   F""" directory containing a file named {weights_name} or"""
                   ''' \nCheckout your internet connection or see how to run the library in'''
                   ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'''   )
          except EnvironmentError:
               raise EnvironmentError(
                   F"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
                   '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
                   F"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
                   F"""containing a file named {weights_name}"""   )