




"""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


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 							lowercase					(snake_case__   : Union[Dict, str, None] = None )     ->  List[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(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 							lowercase					(snake_case__   : str      ,       snake_case__   : Optional[str] = None      ,       snake_case__   : Optional[str] = None )     ->  Optional[int]:


				'''simple docstring'''






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





def 							lowercase					(snake_case__   : List[str]      ,       snake_case__   : str )     ->  Union[str, 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(SCREAMING_SNAKE_CASE__      ,       """local_rank""" ) and args.local_rank not in [-1, 0]:
								return

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

				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=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      ,       )

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





def 							lowercase					(snake_case__   : Optional[str]      ,       snake_case__   : Optional[str] = None )     ->  Dict:


				'''simple docstring'''






				if resolved_file is None or commit_hash is not None:
								return commit_hash
				lowerCAmelCase  =				str(Path(SCREAMING_SNAKE_CASE__ ).as_posix() )
				lowerCAmelCase  =				re.search(R"""snapshots/([^/]+)/"""      ,       SCREAMING_SNAKE_CASE__ )
				if search is None:
								return None
				lowerCAmelCase  =				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.
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 							lowercase					(snake_case__   : Optional[str] = None      ,       snake_case__   : Optional[str] = None )     ->  int:


				'''simple docstring'''






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

				lowerCAmelCase  =				Path(SCREAMING_SNAKE_CASE__ ).expanduser()
				lowerCAmelCase  =				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():
												lowerCAmelCase  =				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).


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 							lowercase					(snake_case__   : str      ,       snake_case__   : Optional[str] = None )     ->  Union[str, Any]:


				'''simple docstring'''






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

				return weights_name





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


				'''simple docstring'''






				lowerCAmelCase  =				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
												lowerCAmelCase  =				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__ ) ):
												lowerCAmelCase  =				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:
																lowerCAmelCase  =				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
												lowerCAmelCase  =				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}''' )











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a      =							{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





