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			: Optional[Any]							   =		get_logger(__name__)


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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[Dict, str, None] = None	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      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(__UpperCamelCase ,     __UpperCamelCase	):
    ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()	)
  elif isinstance(__UpperCamelCase ,     __UpperCamelCase	):
    ua += "; " + user_agent
  return ua



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       Optional[str] = None ,     __UpperCamelCase    :       Optional[str] = None	)							->					Optional[Any]:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       int	)							->					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(__UpperCamelCase ,     """local_rank"""	) and args.local_rank not in [-1, 0]:
    return

  SCREAMING_SNAKE_CASE__										=      args.hub_token if hasattr(__UpperCamelCase ,     """hub_token"""	) else None
  SCREAMING_SNAKE_CASE__										=      get_full_repo_name(__UpperCamelCase ,     token=__UpperCamelCase	)

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

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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[str] ,     __UpperCamelCase    :       Optional[str] = None	)							->					Union[str, Any]:







  """simple docstring"""





  if resolved_file is None or commit_hash is not None:
    return commit_hash
  SCREAMING_SNAKE_CASE__										=      str(Path(__UpperCamelCase	).as_posix()	)
  SCREAMING_SNAKE_CASE__										=      re.search(r"""snapshots/([^/]+)/""" ,     __UpperCamelCase	)
  if search is None:
    return None
  SCREAMING_SNAKE_CASE__										=      search.groups()[0]
  return commit_hash if REGEX_COMMIT_HASH.match(__UpperCamelCase	) 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			: int							   =		os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
__lowerCamelCase			: str							   =		os.path.join(hf_cache_home, '''diffusers''')



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[str] = None ,     __UpperCamelCase    :       Optional[str] = None	)							->					None:







  """simple docstring"""





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

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

if cache_version < 1:
   __lowerCamelCase			: 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:
         __lowerCamelCase			: Tuple							   =		'''\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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       Optional[str] = None	)							->					str:







  """simple docstring"""





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

  return weights_name






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     *,
    __UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Union[str, Any]=None ,     )							->					List[Any]:







  """simple docstring"""





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

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	__SCREAMING_SNAKE_CASE  (					)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )	)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""" ,     type=__UpperCamelCase ,     default=1 ,     help="""Number of TPU cores to use (1 or 8)."""	)

  # positional
  parser.add_argument(
      """training_script""" ,     type=__UpperCamelCase ,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ) ,     )

  # rest from the training program
  parser.add_argument("""training_script_args""" ,     nargs=__UpperCamelCase	)

  return parser.parse_args()



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      parse_args()

  # Import training_script as a module.
  SCREAMING_SNAKE_CASE__										=      Path(args.training_script	)
  sys.path.append(str(script_fpath.parent.resolve()	)	)
  SCREAMING_SNAKE_CASE__										=      script_fpath.stem
  SCREAMING_SNAKE_CASE__										=      importlib.import_module(__UpperCamelCase	)

  # Patch sys.argv
  SCREAMING_SNAKE_CASE__										=      [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

  xmp.spawn(mod._mp_fn ,     args=() ,     nprocs=args.num_cores	)


if __name__ == "__main__":
   main()
