
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

lowercase  : List[str]						   =							"""
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""

@dataclass
class __snake_case		(				lowerCAmelCase					):
    _a		:						Union[PIL.Image.Image, np.ndarray]






class __snake_case		(				lowerCAmelCase					):
    def __init__(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           super().__init__()

           self.register_modules(
               prior=snake_case		,image_encoder=snake_case		,image_processor=snake_case		,scheduler=snake_case		,renderer=snake_case		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           if latents is None:
                  lowercase							:					Any	     =				randn_tensor(snake_case		,generator=snake_case		,device=snake_case		,dtype=snake_case				)
           else:
                  if latents.shape != shape:
                         raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"				)
                  lowercase							:					Tuple	     =				latents.to(snake_case				)

           lowercase							:					Optional[int]	     =				latents * scheduler.init_noise_sigma
           return latents
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=0				):



           '''simple docstring'''







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

           lowercase							:					Union[str, Any]	     =				torch.device(f"cuda:{gpu_id}"				)

           lowercase							:					Any	     =				[self.image_encoder, self.prior]
           for cpu_offloaded_model in models:
                  if cpu_offloaded_model is not None:
                         cpu_offload(snake_case		,snake_case				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.device != torch.device("""meta"""				) or not hasattr(self.image_encoder		,"""_hf_hook"""				):
                  return self.device
           for module in self.image_encoder.modules():
                  if (
                      hasattr(snake_case		,"""_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
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case				) and isinstance(image[0]		,torch.Tensor				):
                  lowercase							:					Optional[Any]	     =				torch.cat(snake_case		,axis=0				) if image[0].ndim == 4 else torch.stack(snake_case		,axis=0				)

           if not isinstance(snake_case		,torch.Tensor				):
                  lowercase							:					List[str]	     =				self.image_processor(snake_case		,return_tensors="""pt"""				).pixel_values[0].unsqueeze(0				)

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

           lowercase							:					Any	     =				self.image_encoder(snake_case				)["""last_hidden_state"""]
           lowercase							:					Any	     =				image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           lowercase							:					int	     =				image_embeds.repeat_interleave(snake_case		,dim=0				)

           if do_classifier_free_guidance:
                  lowercase							:					Union[str, Any]	     =				torch.zeros_like(snake_case				)

                  # For classifier free guidance, we need to do two forward passes.
                  # Here we concatenate the unconditional and text embeddings into a single batch
                  # to avoid doing two forward passes
                  lowercase							:					Optional[int]	     =				torch.cat([negative_image_embeds, image_embeds]				)

           return image_embeds
    @torch.no_grad()
    @replace_example_docstring(snake_case				)
    def __call__(  self		,snake_case		,snake_case = 1		,snake_case = 25		,snake_case = None		,snake_case = None		,snake_case = 4.0		,snake_case = 64		,snake_case = "pil"		,snake_case = True		,):



           '''simple docstring'''







           if isinstance(snake_case		,PIL.Image.Image				):
                  lowercase							:					List[str]	     =				1
           elif isinstance(snake_case		,torch.Tensor				):
                  lowercase							:					Dict	     =				image.shape[0]
           elif isinstance(snake_case		,snake_case				) and isinstance(image[0]		,(torch.Tensor, PIL.Image.Image)				):
                  lowercase							:					List[Any]	     =				len(snake_case				)
           else:
                  raise ValueError(
                      f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(snake_case				)}"				)

           lowercase							:					str	     =				self._execution_device

           lowercase							:					Optional[Any]	     =				batch_size * num_images_per_prompt

           lowercase							:					Optional[int]	     =				guidance_scale > 1.0
           lowercase							:					Union[str, Any]	     =				self._encode_image(snake_case		,snake_case		,snake_case		,snake_case				)

           # prior

           self.scheduler.set_timesteps(snake_case		,device=snake_case				)
           lowercase							:					List[str]	     =				self.scheduler.timesteps

           lowercase							:					List[str]	     =				self.prior.config.num_embeddings
           lowercase							:					str	     =				self.prior.config.embedding_dim

           lowercase							:					Any	     =				self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)		,image_embeds.dtype		,snake_case		,snake_case		,snake_case		,self.scheduler		,)

           # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
           lowercase							:					List[str]	     =				latents.reshape(latents.shape[0]		,snake_case		,snake_case				)

           for i, t in enumerate(self.progress_bar(snake_case				)				):
                  # expand the latents if we are doing classifier free guidance
                  lowercase							:					str	     =				torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                  lowercase							:					Union[str, Any]	     =				self.scheduler.scale_model_input(snake_case		,snake_case				)

                  lowercase							:					Union[str, Any]	     =				self.prior(
                      snake_case		,timestep=snake_case		,proj_embedding=snake_case		,).predicted_image_embedding

                  # remove the variance
                  lowercase							:					Tuple	     =				noise_pred.split(
                      scaled_model_input.shape[2]		,dim=2				)  # batch_size, num_embeddings, embedding_dim

                  if do_classifier_free_guidance is not None:
                         lowercase							:					Any	     =				noise_pred.chunk(2				)
                         lowercase							:					List[str]	     =				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                  lowercase							:					Union[str, Any]	     =				self.scheduler.step(
                      snake_case		,timestep=snake_case		,sample=snake_case		,).prev_sample

           if output_type == "latent":
                  return ShapEPipelineOutput(images=snake_case				)

           lowercase							:					Any	     =				[]
           for i, latent in enumerate(snake_case				):
                  print()
                  lowercase							:					Union[str, Any]	     =				self.renderer.decode(
                      latent[None, :]		,snake_case		,size=snake_case		,ray_batch_size=4096		,n_coarse_samples=64		,n_fine_samples=128		,)

                  images.append(snake_case				)

           lowercase							:					Optional[int]	     =				torch.stack(snake_case				)

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

           lowercase							:					str	     =				images.cpu().numpy()

           if output_type == "pil":
                  lowercase							:					Optional[int]	     =				[self.numpy_to_pil(snake_case				) for image in images]

           # Offload last model to CPU
           if hasattr(self		,"""final_offload_hook"""				) and self.final_offload_hook is not None:
                  self.final_offload_hook.offload()

           if not return_dict:
                  return (images,)

           return ShapEPipelineOutput(images=snake_case				)


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


lowercase  : Tuple						   =							get_logger(__name__)


lowercase  : Optional[int]						   =							Path(__file__).parent / """model_card_template.md"""
lowercase  : Dict						   =							uuida().hex
lowercase  : Tuple						   =							os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
lowercase  : str						   =							os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
lowercase  : Tuple						   =							HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""







def 						_snake_case(  SCREAMING_SNAKE_CASE__ = None    )			->   str:
       lowercase							:					str	     =				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(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
              ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()    )
       elif isinstance(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
              ua += "; " + user_agent
       return ua







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = None    )			->   Dict:
       if token is None:
              lowercase							:					Optional[int]	     =				HfFolder.get_token()
       if organization is None:
              lowercase							:					int	     =				whoami(SCREAMING_SNAKE_CASE__    )["""name"""]
              return f"{username}/{model_id}"
       else:
              return f"{organization}/{model_id}"







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Tuple:
       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(SCREAMING_SNAKE_CASE__ ,     """local_rank"""    ) and args.local_rank not in [-1, 0]:
              return

       lowercase							:					str	     =				args.hub_token if hasattr(SCREAMING_SNAKE_CASE__ ,     """hub_token"""    ) else None
       lowercase							:					int	     =				get_full_repo_name(SCREAMING_SNAKE_CASE__ ,     token=SCREAMING_SNAKE_CASE__    )

       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=SCREAMING_SNAKE_CASE__ ,     model_name=SCREAMING_SNAKE_CASE__ ,     repo_name=SCREAMING_SNAKE_CASE__ ,     dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE__ ,     """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(SCREAMING_SNAKE_CASE__ ,     """gradient_accumulation_steps"""    ) else None
           ) ,     adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__ ,     """adam_beta1"""    ) else None ,     adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE__ ,     """adam_beta2"""    ) else None ,     adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE__ ,     """adam_weight_decay"""    ) else None ,     adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE__ ,     """adam_epsilon"""    ) else None ,     lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE__ ,     """lr_scheduler"""    ) else None ,     lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE__ ,     """lr_warmup_steps"""    ) else None ,     ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE__ ,     """ema_inv_gamma"""    ) else None ,     ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE__ ,     """ema_power"""    ) else None ,     ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE__ ,     """ema_max_decay"""    ) else None ,     mixed_precision=args.mixed_precision ,     )

       lowercase							:					str	     =				os.path.join(args.output_dir ,     """README.md"""    )
       model_card.save(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = None    )			->   Optional[Any]:
       if resolved_file is None or commit_hash is not None:
              return commit_hash
       lowercase							:					List[Any]	     =				str(Path(SCREAMING_SNAKE_CASE__    ).as_posix()    )
       lowercase							:					Any	     =				re.search(R"""snapshots/([^/]+)/""" ,     SCREAMING_SNAKE_CASE__    )
       if search is None:
              return None
       lowercase							:					List[Any]	     =				search.groups()[0]
       return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE__    ) 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.
lowercase  : Optional[Any]						   =							os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
lowercase  : Optional[int]						   =							os.path.join(hf_cache_home, """diffusers""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = None    )			->   None:
       if new_cache_dir is None:
              lowercase							:					Union[str, Any]	     =				DIFFUSERS_CACHE
       if old_cache_dir is None:
              lowercase							:					List[str]	     =				old_diffusers_cache

       lowercase							:					Dict	     =				Path(SCREAMING_SNAKE_CASE__    ).expanduser()
       lowercase							:					int	     =				Path(SCREAMING_SNAKE_CASE__    ).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							:					Any	     =				new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE__    )
                     new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__ ,     exist_ok=SCREAMING_SNAKE_CASE__    )
                     os.replace(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                     try:
                            os.symlink(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                     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).


lowercase  : Dict						   =							os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
  lowercase  : Any						   =							0
else:
  with open(cache_version_file) as f:
    try:
      lowercase  : List[Any]						   =							int(f.read())
    except ValueError:
      lowercase  : int						   =							0

if cache_version < 1:
  lowercase  : Union[str, Any]						   =							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:
      lowercase  : int						   =							"""\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(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = None    )			->   str:
       if variant is not None:
              lowercase							:					List[str]	     =				weights_name.split("""."""    )
              lowercase							:					Optional[Any]	     =				splits[:-1] + [variant] + splits[-1:]
              lowercase							:					int	     =				""".""".join(SCREAMING_SNAKE_CASE__    )

       return weights_name







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     *,
    SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=None ,     )			->   Optional[Any]:
       lowercase							:					Optional[int]	     =				str(SCREAMING_SNAKE_CASE__    )
       if os.path.isfile(SCREAMING_SNAKE_CASE__    ):
              return pretrained_model_name_or_path
       elif os.path.isdir(SCREAMING_SNAKE_CASE__    ):
              if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )    ):
                     # Load from a PyTorch checkpoint
                     lowercase							:					List[Any]	     =				os.path.join(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                     return model_file
              elif subfolder is not None and os.path.isfile(
                  os.path.join(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )    ):
                     lowercase							:					Any	     =				os.path.join(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                     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(SCREAMING_SNAKE_CASE__    ).base_version    ) >= version.parse("""0.20.0"""    )
              ):
                     try:
                            lowercase							:					Any	     =				hf_hub_download(
                                SCREAMING_SNAKE_CASE__ ,     filename=_add_variant(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     cache_dir=SCREAMING_SNAKE_CASE__ ,     force_download=SCREAMING_SNAKE_CASE__ ,     proxies=SCREAMING_SNAKE_CASE__ ,     resume_download=SCREAMING_SNAKE_CASE__ ,     local_files_only=SCREAMING_SNAKE_CASE__ ,     use_auth_token=SCREAMING_SNAKE_CASE__ ,     user_agent=SCREAMING_SNAKE_CASE__ ,     subfolder=SCREAMING_SNAKE_CASE__ ,     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." ,     SCREAMING_SNAKE_CASE__ ,     )
                            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(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )} 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(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )}' so that the correct variant file can be added." ,     SCREAMING_SNAKE_CASE__ ,     )
              try:
                     # 2. Load model file as usual
                     lowercase							:					int	     =				hf_hub_download(
                         SCREAMING_SNAKE_CASE__ ,     filename=SCREAMING_SNAKE_CASE__ ,     cache_dir=SCREAMING_SNAKE_CASE__ ,     force_download=SCREAMING_SNAKE_CASE__ ,     proxies=SCREAMING_SNAKE_CASE__ ,     resume_download=SCREAMING_SNAKE_CASE__ ,     local_files_only=SCREAMING_SNAKE_CASE__ ,     use_auth_token=SCREAMING_SNAKE_CASE__ ,     user_agent=SCREAMING_SNAKE_CASE__ ,     subfolder=SCREAMING_SNAKE_CASE__ ,     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}"    )
