
'''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


__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 					UpperCAmelCase_		(__a      :    str = None ):






   """simple docstring"""

   _a				:	Union[str, 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(_lowerCAmelCase ,	_lowerCAmelCase ):
      ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items() )
   elif isinstance(_lowerCAmelCase ,	_lowerCAmelCase ):
      ua += "; " + user_agent
   return ua





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Dict = None ,	__a      :    str = None ):






   """simple docstring"""

   if token is None:
      _a				:	Dict			=   HfFolder.get_token()
   if organization is None:
      _a				:	int			=   whoami(_lowerCAmelCase )["""name"""]
      return f"""{username}/{model_id}"""
   else:
      return f"""{organization}/{model_id}"""





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    str ):






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

   _a				:	Union[str, Any]			=   args.hub_token if hasattr(_lowerCAmelCase ,	'hub_token' ) else None
   _a				:	Optional[Any]			=   get_full_repo_name(_lowerCAmelCase ,	token=_lowerCAmelCase )

   _a				:	int			=   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=_lowerCAmelCase ,	model_name=_lowerCAmelCase ,	repo_name=_lowerCAmelCase ,	dataset_name=args.dataset_name if hasattr(_lowerCAmelCase ,	'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(_lowerCAmelCase ,	'gradient_accumulation_steps' ) else None
       ) ,	adam_betaa=args.adam_betaa if hasattr(_lowerCAmelCase ,	'adam_beta1' ) else None ,	adam_betaa=args.adam_betaa if hasattr(_lowerCAmelCase ,	'adam_beta2' ) else None ,	adam_weight_decay=args.adam_weight_decay if hasattr(_lowerCAmelCase ,	'adam_weight_decay' ) else None ,	adam_epsilon=args.adam_epsilon if hasattr(_lowerCAmelCase ,	'adam_epsilon' ) else None ,	lr_scheduler=args.lr_scheduler if hasattr(_lowerCAmelCase ,	'lr_scheduler' ) else None ,	lr_warmup_steps=args.lr_warmup_steps if hasattr(_lowerCAmelCase ,	'lr_warmup_steps' ) else None ,	ema_inv_gamma=args.ema_inv_gamma if hasattr(_lowerCAmelCase ,	'ema_inv_gamma' ) else None ,	ema_power=args.ema_power if hasattr(_lowerCAmelCase ,	'ema_power' ) else None ,	ema_max_decay=args.ema_max_decay if hasattr(_lowerCAmelCase ,	'ema_max_decay' ) else None ,	mixed_precision=args.mixed_precision ,	)

   _a				:	List[Any]			=   os.path.join(args.output_dir ,	'README.md' )
   model_card.save(_lowerCAmelCase )





def 					UpperCAmelCase_		(__a      :    Union[str, Any] ,	__a      :    Optional[int] = None ):






   """simple docstring"""

   if resolved_file is None or commit_hash is not None:
      return commit_hash
   _a				:	List[str]			=   str(Path(_lowerCAmelCase ).as_posix() )
   _a				:	Dict			=   re.search(R'snapshots/([^/]+)/' ,	_lowerCAmelCase )
   if search is None:
      return None
   _a				:	str			=   search.groups()[0]
   return commit_hash if REGEX_COMMIT_HASH.match(_lowerCAmelCase ) 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 					UpperCAmelCase_		(__a      :    Union[str, Any] = None ,	__a      :    Union[str, Any] = None ):






   """simple docstring"""

   if new_cache_dir is None:
      _a				:	Union[str, Any]			=   DIFFUSERS_CACHE
   if old_cache_dir is None:
      _a				:	Optional[Any]			=   old_diffusers_cache

   _a				:	Dict			=   Path(_lowerCAmelCase ).expanduser()
   _a				:	Union[str, Any]			=   Path(_lowerCAmelCase ).expanduser()
   for old_blob_path in old_cache_dir.glob('**/blobs/*' ):
      if old_blob_path.is_file() and not old_blob_path.is_symlink():
         _a				:	Union[str, Any]			=   new_cache_dir / old_blob_path.relative_to(_lowerCAmelCase )
         new_blob_path.parent.mkdir(parents=_lowerCAmelCase ,	exist_ok=_lowerCAmelCase )
         os.replace(_lowerCAmelCase ,	_lowerCAmelCase )
         try:
            os.symlink(_lowerCAmelCase ,	_lowerCAmelCase )
         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 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Optional[int] = None ):






   """simple docstring"""

   if variant is not None:
      _a				:	Union[str, Any]			=   weights_name.split('.' )
      _a				:	List[Any]			=   splits[:-1] + [variant] + splits[-1:]
      _a				:	Any			=   """.""".join(_lowerCAmelCase )

   return weights_name





def 					UpperCAmelCase_		(__a      :    List[str] ,	*,
    __a      :    Union[str, Any] ,	__a      :    List[Any] ,	__a      :    Any ,	__a      :    Tuple ,	__a      :    Dict ,	__a      :    Optional[int] ,	__a      :    Any ,	__a      :    Dict ,	__a      :    int ,	__a      :    Tuple ,	__a      :    int=None ,	):






   """simple docstring"""

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


SCREAMING_SNAKE_CASE_:str             =						{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Union[str, Any]             =						[
         """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AdaptiveEmbedding""",
         """TransfoXLForSequenceClassification""",
         """TransfoXLLMHeadModel""",
         """TransfoXLModel""",
         """TransfoXLPreTrainedModel""",
         """load_tf_weights_in_transfo_xl""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any             =						[
         """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFAdaptiveEmbedding""",
         """TFTransfoXLForSequenceClassification""",
         """TFTransfoXLLMHeadModel""",
         """TFTransfoXLMainLayer""",
         """TFTransfoXLModel""",
         """TFTransfoXLPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
     from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_transfo_xl import (
              TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              AdaptiveEmbedding,
              TransfoXLForSequenceClassification,
              TransfoXLLMHeadModel,
              TransfoXLModel,
              TransfoXLPreTrainedModel,
              load_tf_weights_in_transfo_xl,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_transfo_xl import (
              TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFAdaptiveEmbedding,
              TFTransfoXLForSequenceClassification,
              TFTransfoXLLMHeadModel,
              TFTransfoXLMainLayer,
              TFTransfoXLModel,
              TFTransfoXLPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_:Dict             =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


