





'''simple docstring'''





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  :   Any						      =get_logger(__name__)


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




def     a__ (					lowercase__ = None  ):






   '''simple docstring'''






   UpperCAmelCase_											=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(__a  ,    __a  ):
      ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()  )
   elif isinstance(__a  ,    __a  ):
      ua += "; " + user_agent
   return ua




def     a__ (					lowercase__  ,    lowercase__ = None  ,    lowercase__ = None  ):






   '''simple docstring'''






   if token is None:
      UpperCAmelCase_											=HfFolder.get_token()
   if organization is None:
      UpperCAmelCase_											=whoami(__a  )["name"]
      return F'{username}/{model_id}'
   else:
      return F'{organization}/{model_id}'




def     a__ (					lowercase__  ,    lowercase__  ):






   '''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(__a  ,    "local_rank"  ) and args.local_rank not in [-1, 0]:
      return

   UpperCAmelCase_											=args.hub_token if hasattr(__a  ,    "hub_token"  ) else None
   UpperCAmelCase_											=get_full_repo_name(__a  ,    token=__a  )

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

   UpperCAmelCase_											=os.path.join(args.output_dir  ,    "README.md"  )
   model_card.save(__a  )




def     a__ (					lowercase__  ,    lowercase__ = None  ):






   '''simple docstring'''






   if resolved_file is None or commit_hash is not None:
      return commit_hash
   UpperCAmelCase_											=str(Path(__a  ).as_posix()  )
   UpperCAmelCase_											=re.search(R"snapshots/([^/]+)/"  ,    __a  )
   if search is None:
      return None
   UpperCAmelCase_											=search.groups()[0]
   return commit_hash if REGEX_COMMIT_HASH.match(__a  ) 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  :   List[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     a__ (					lowercase__ = None  ,    lowercase__ = None  ):






   '''simple docstring'''






   if new_cache_dir is None:
      UpperCAmelCase_											=DIFFUSERS_CACHE
   if old_cache_dir is None:
      UpperCAmelCase_											=old_diffusers_cache

   UpperCAmelCase_											=Path(__a  ).expanduser()
   UpperCAmelCase_											=Path(__a  ).expanduser()
   for old_blob_path in old_cache_dir.glob("**/blobs/*"  ):
      if old_blob_path.is_file() and not old_blob_path.is_symlink():
         UpperCAmelCase_											=new_cache_dir / old_blob_path.relative_to(__a  )
         new_blob_path.parent.mkdir(parents=__a  ,    exist_ok=__a  )
         os.replace(__a  ,    __a  )
         try:
            os.symlink(__a  ,    __a  )
         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  :   List[str]						      =os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
     __lowercase  :   List[str]						      =0
else:
     with open(cache_version_file) as f:
          try:
               __lowercase  :   Tuple						      =int(f.read())
          except ValueError:
               __lowercase  :   Optional[int]						      =0

if cache_version < 1:
     __lowercase  :   Dict						      =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  :   Union[str, Any]						      ="""\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     a__ (					lowercase__  ,    lowercase__ = None  ):






   '''simple docstring'''






   if variant is not None:
      UpperCAmelCase_											=weights_name.split("."  )
      UpperCAmelCase_											=splits[:-1] + [variant] + splits[-1:]
      UpperCAmelCase_											=".".join(__a  )

   return weights_name





def     a__ (					lowercase__  ,    *,
    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__=None  ,    ):






   '''simple docstring'''






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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowercase  :   Optional[Any]						      ={
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[Any]						      =[
         """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GroupViTModel""",
         """GroupViTPreTrainedModel""",
         """GroupViTTextModel""",
         """GroupViTVisionModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[str]						      =[
         """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFGroupViTModel""",
         """TFGroupViTPreTrainedModel""",
         """TFGroupViTTextModel""",
         """TFGroupViTVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         GroupViTConfig,
         GroupViTOnnxConfig,
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_groupvit import (
              GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              GroupViTModel,
              GroupViTPreTrainedModel,
              GroupViTTextModel,
              GroupViTVisionModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_groupvit import (
              TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGroupViTModel,
              TFGroupViTPreTrainedModel,
              TFGroupViTTextModel,
              TFGroupViTVisionModel,
          )

else:
     import sys

     __lowercase  :   str						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




