"""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_    :		Optional[int]							=	get_logger(__name__)


lowerCamelCase_    :		Union[str, Any]							=	Path(__file__).parent / """model_card_template.md"""
lowerCamelCase_    :		Tuple							=	uuida().hex
lowerCamelCase_    :		Optional[Any]							=	os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase_    :		Dict							=	os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase_    :		Union[str, Any]							=	HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""
def 							lowerCAmelCase(     __lowerCamelCase = None						):
					__a		 =       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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase = None				,    __lowerCamelCase = None						):
					if token is None:
										__a		 =       HfFolder.get_token()
					if organization is None:
										__a		 =       whoami(__lowerCamelCase						)['name']
										return f'''{username}/{model_id}'''
					else:
										return f'''{organization}/{model_id}'''
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					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		 =       args.hub_token if hasattr(__lowerCamelCase				,    'hub_token'						) else None
					__a		 =       get_full_repo_name(__lowerCamelCase				,    token=__lowerCamelCase						)

					__a		 =       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		 =       os.path.join(args.output_dir				,    'README.md'						)
					model_card.save(__lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase = None						):
					if resolved_file is None or commit_hash is not None:
										return commit_hash
					__a		 =       str(Path(__lowerCamelCase						).as_posix()						)
					__a		 =       re.search(r'snapshots/([^/]+)/'				,    __lowerCamelCase						)
					if search is None:
										return None
					__a		 =       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_    :		Tuple							=	os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
lowerCamelCase_    :		Union[str, Any]							=	os.path.join(hf_cache_home, """diffusers""")
def 							lowerCAmelCase(     __lowerCamelCase = None				,    __lowerCamelCase = None						):
					if new_cache_dir is None:
										__a		 =       DIFFUSERS_CACHE
					if old_cache_dir is None:
										__a		 =       old_diffusers_cache

					__a		 =       Path(__lowerCamelCase						).expanduser()
					__a		 =       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		 =       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_    :		Tuple							=	os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
	lowerCamelCase_    :		Union[str, Any]							=	0
else:
	with open(cache_version_file) as f:
		try:
			lowerCamelCase_    :		List[Any]							=	int(f.read())
		except ValueError:
			lowerCamelCase_    :		Dict							=	0

if cache_version < 1:
	lowerCamelCase_    :		List[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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase = None						):
					if variant is not None:
										__a		 =       weights_name.split('.'						)
										__a		 =       splits[:-1] + [variant] + splits[-1:]
										__a		 =       '.'.join(__lowerCamelCase						)

					return weights_name



def 							lowerCAmelCase(     __lowerCamelCase				,    *,
    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=None				,    ):
					__a		 =       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		 =       os.path.join(__lowerCamelCase				,    __lowerCamelCase						)
															return model_file
										elif subfolder is not None and os.path.isfile(
										    os.path.join(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)						):
															__a		 =       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		 =       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		 =       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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase_    :		Optional[Any]							=	{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	[
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




