






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


a_							=    get_logger(__name__)


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


def        _a  (     UpperCamelCase_					:				Union[Dict, str, None] = None   ) ->   str:
      """simple docstring"""







      lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Optional[str] = None    ,   UpperCamelCase_					:				Optional[str] = None   ) ->   str:
      """simple docstring"""







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


def        _a  (     UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Optional[Any]   ) ->   Optional[int]:
      """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

      lowerCAmelCase__       =						args.hub_token if hasattr(UpperCamelCase_    ,   "hub_token"   ) else None
      lowerCAmelCase__       =						get_full_repo_name(UpperCamelCase_    ,   token=UpperCamelCase_   )

      lowerCAmelCase__       =						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    ,   )

      lowerCAmelCase__       =						os.path.join(args.output_dir    ,   "README.md"   )
      model_card.save(UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[str]    ,   UpperCamelCase_					:				Optional[str] = None   ) ->   Optional[int]:
      """simple docstring"""







      if resolved_file is None or commit_hash is not None:
            return commit_hash
      lowerCAmelCase__       =						str(Path(UpperCamelCase_   ).as_posix()   )
      lowerCAmelCase__       =						re.search(R"snapshots/([^/]+)/"    ,   UpperCamelCase_   )
      if search is None:
            return None
      lowerCAmelCase__       =						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.
a_							=    os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
a_							=    os.path.join(hf_cache_home, '''diffusers''')


def        _a  (     UpperCamelCase_					:				Optional[str] = None    ,   UpperCamelCase_					:				Optional[str] = None   ) ->   None:
      """simple docstring"""







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

      lowerCAmelCase__       =						Path(UpperCamelCase_   ).expanduser()
      lowerCAmelCase__       =						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():
                  lowerCAmelCase__       =						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).


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

if cache_version < 1:
 a_							=    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:
   a_							=    '''\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  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Optional[str] = None   ) ->   str:
      """simple docstring"""







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

      return weights_name





def        _a  (     UpperCamelCase_					:				List[str]    ,   *,
    UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Optional[int]=None    ,   ) ->   Any:
      """simple docstring"""







      lowerCAmelCase__       =						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
                  lowerCAmelCase__       =						os.path.join(UpperCamelCase_    ,   UpperCamelCase_   )
                  return model_file
            elif subfolder is not None and os.path.isfile(
                os.path.join(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )   ):
                  lowerCAmelCase__       =						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:
                        lowerCAmelCase__       =						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
                  lowerCAmelCase__       =						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}"   )














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ['''PLBartTokenizer''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''PLBartForCausalLM''',
	    '''PLBartForConditionalGeneration''',
	    '''PLBartForSequenceClassification''',
	    '''PLBartModel''',
	    '''PLBartPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)






