





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 lowerCAmelCase__(__snake_case = 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(__snake_case ,__snake_case							):
		ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()							)
	elif isinstance(__snake_case ,__snake_case							):
		ua += "; " + user_agent
	return ua





def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None							)       ->   Union[str, Any]:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   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(__snake_case ,'''local_rank'''							) and args.local_rank not in [-1, 0]:
		return

	lowerCamelCase__              =	args.hub_token if hasattr(__snake_case ,'''hub_token'''							) else None
	lowerCamelCase__              =	get_full_repo_name(__snake_case ,token=__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=__snake_case ,model_name=__snake_case ,repo_name=__snake_case ,dataset_name=args.dataset_name if hasattr(__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(__snake_case ,'''gradient_accumulation_steps'''							) else None
	    ) ,adam_betaa=args.adam_betaa if hasattr(__snake_case ,'''adam_beta1'''							) else None ,adam_betaa=args.adam_betaa if hasattr(__snake_case ,'''adam_beta2'''							) else None ,adam_weight_decay=args.adam_weight_decay if hasattr(__snake_case ,'''adam_weight_decay'''							) else None ,adam_epsilon=args.adam_epsilon if hasattr(__snake_case ,'''adam_epsilon'''							) else None ,lr_scheduler=args.lr_scheduler if hasattr(__snake_case ,'''lr_scheduler'''							) else None ,lr_warmup_steps=args.lr_warmup_steps if hasattr(__snake_case ,'''lr_warmup_steps'''							) else None ,ema_inv_gamma=args.ema_inv_gamma if hasattr(__snake_case ,'''ema_inv_gamma'''							) else None ,ema_power=args.ema_power if hasattr(__snake_case ,'''ema_power'''							) else None ,ema_max_decay=args.ema_max_decay if hasattr(__snake_case ,'''ema_max_decay'''							) else None ,mixed_precision=args.mixed_precision ,)

	lowerCamelCase__              =	os.path.join(args.output_dir ,'''README.md'''							)
	model_card.save(__snake_case							)





def lowerCAmelCase__(__snake_case ,__snake_case = None							)       ->   str:







	'''simple docstring'''







	if resolved_file is None or commit_hash is not None:
		return commit_hash
	lowerCamelCase__              =	str(Path(__snake_case							).as_posix()							)
	lowerCamelCase__              =	re.search(R'''snapshots/([^/]+)/''' ,__snake_case							)
	if search is None:
		return None
	lowerCamelCase__              =	search.groups()[0]
	return commit_hash if REGEX_COMMIT_HASH.match(__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 lowerCAmelCase__(__snake_case = None ,__snake_case = 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(__snake_case							).expanduser()
	lowerCamelCase__              =	Path(__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(__snake_case							)
			new_blob_path.parent.mkdir(parents=__snake_case ,exist_ok=__snake_case							)
			os.replace(__snake_case ,__snake_case							)
			try:
				os.symlink(__snake_case ,__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 lowerCAmelCase__(__snake_case ,__snake_case = None							)       ->   str:







	'''simple docstring'''







	if variant is not None:
		lowerCamelCase__              =	weights_name.split('''.'''							)
		lowerCamelCase__              =	splits[:-1] + [variant] + splits[-1:]
		lowerCamelCase__              =	'''.'''.join(__snake_case							)

	return weights_name





def lowerCAmelCase__(__snake_case ,*,
    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case=None ,)       ->   List[str]:







	'''simple docstring'''







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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a						=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTNeoForCausalLM",
			    "GPTNeoForQuestionAnswering",
			    "GPTNeoForSequenceClassification",
			    "GPTNeoForTokenClassification",
			    "GPTNeoModel",
			    "GPTNeoPreTrainedModel",
			    "load_tf_weights_in_gpt_neo",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FlaxGPTNeoForCausalLM",
			    "FlaxGPTNeoModel",
			    "FlaxGPTNeoPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			_a						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



