



'''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


UpperCAmelCase     		=   get_logger(__name__)


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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any] = None							)      ->						Union[str, Any]:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Any = None							,		_SCREAMING_SNAKE_CASE					:						Optional[int] = None							)      ->						str:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						List[str]:







	"""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(lowerCAmelCase__							,		"""local_rank"""							) and args.local_rank not in [-1, 0]:
		return

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

	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=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							,		)

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						List[Any] = None							)      ->						Tuple:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict = None							,		_SCREAMING_SNAKE_CASE					:						int = None							)      ->						List[Any]:







	"""simple docstring"""
	if new_cache_dir is None:
		lowerCAmelCase   =     DIFFUSERS_CACHE
	if old_cache_dir is None:
		lowerCAmelCase   =     old_diffusers_cache

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


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

if cache_version < 1:
				UpperCAmelCase     		=   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:
												UpperCAmelCase     		=   "\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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						Dict = None							)      ->						Tuple:







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

	return weights_name






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							,		*,
    _SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						List[Any]=None							,		)      ->						Union[str, Any]:







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




'''simple docstring'''

class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     None:
							lowerCAmelCase   =     {}  # Mapping from char to TrieNode
							lowerCAmelCase   =     False


						def     __snake_case   ( self     ,	A_				)     ->     None:
							for word in words:
								self.insert(A_				)


						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     self
							for char in word:
								if char not in curr.nodes:
									lowerCAmelCase   =     TrieNode()
								lowerCAmelCase   =     curr.nodes[char]
							lowerCAmelCase   =     True


						def     __snake_case   ( self     ,	A_				)     ->     bool:
							lowerCAmelCase   =     self
							for char in word:
								if char not in curr.nodes:
									return False
								lowerCAmelCase   =     curr.nodes[char]
							return curr.is_leaf




						def     __snake_case   ( self     ,	A_				)     ->     None:

							def _delete(A_     ,	A_     ,	A_				) -> bool:
								if index == len(A_				):
									# If word does not exist
									if not curr.is_leaf:
										return False
									lowerCAmelCase   =     False
									return len(curr.nodes				) == 0
								lowerCAmelCase   =     word[index]
								lowerCAmelCase   =     curr.nodes.get(A_				)
								# If char not in current trie node
								if not char_node:
									return False
								# Flag to check if node can be deleted
								lowerCAmelCase   =     _delete(A_     ,	A_     ,	index + 1				)
								if delete_curr:
									del curr.nodes[char]
									return len(curr.nodes				) == 0
								return delete_curr

							_delete(self     ,	A_     ,	0				)







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						TrieNode							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						None:







	"""simple docstring"""
	if node.is_leaf:
		print(_SCREAMING_SNAKE_CASE							,		end=""" """							)

	for key, value in node.nodes.items():
		print_words(_SCREAMING_SNAKE_CASE							,		word + key							)






def 		_snake_case		( )      ->						bool:







	"""simple docstring"""
	lowerCAmelCase   =     """banana bananas bandana band apple all beast""".split()
	lowerCAmelCase   =     TrieNode()
	root.insert_many(_SCREAMING_SNAKE_CASE							)
	# print_words(root, "")
	assert all(root.find(_SCREAMING_SNAKE_CASE							) for word in words							)
	assert root.find("""banana"""							)
	assert not root.find("""bandanas"""							)
	assert not root.find("""apps"""							)
	assert root.find("""apple"""							)
	assert root.find("""all"""							)
	root.delete("""all"""							)
	assert not root.find("""all"""							)
	root.delete("""banana"""							)
	assert not root.find("""banana"""							)
	assert root.find("""bananas"""							)
	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						bool							)      ->						None:







	"""simple docstring"""
	print(str(_SCREAMING_SNAKE_CASE							)							,		"""works!""" if passes else """doesn't work :("""							)






def 		_snake_case		( )      ->						None:







	"""simple docstring"""
	assert test_trie()






def 		_snake_case		( )      ->						None:







	"""simple docstring"""
	print_results("""Testing trie functionality"""							,		test_trie()							)


if __name__ == "__main__":
				main()