


'''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


__snake_case							      =     get_logger(__name__)


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


def 			a      (			__a = None   )  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :Optional[int]												= 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(__a ,					__a   ):
										ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()   )
					elif isinstance(__a ,					__a   ):
										ua += "; " + user_agent
					return ua


def 			a      (			__a ,					__a = None ,					__a = None   )  ->	Optional[Any]:



					'''simple docstring'''






					if token is None:
										UpperCamelCase__   :Optional[int]												= HfFolder.get_token()
					if organization is None:
										UpperCamelCase__   :Optional[Any]												= whoami(__a   )['''name''']
										return f'''{username}/{model_id}'''
					else:
										return f'''{organization}/{model_id}'''


def 			a      (			__a ,					__a   )  ->	List[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(__a ,					'''local_rank'''   ) and args.local_rank not in [-1, 0]:
										return

					UpperCamelCase__   :Any												= args.hub_token if hasattr(__a ,					'''hub_token'''   ) else None
					UpperCamelCase__   :List[Any]												= get_full_repo_name(__a ,					token=__a   )

					UpperCamelCase__   :Union[str, Any]												= 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=__a ,					model_name=__a ,					repo_name=__a ,					dataset_name=args.dataset_name if hasattr(__a ,					'''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(__a ,					'''gradient_accumulation_steps'''   ) else None
					    ) ,					adam_betaa=args.adam_betaa if hasattr(__a ,					'''adam_beta1'''   ) else None ,					adam_betaa=args.adam_betaa if hasattr(__a ,					'''adam_beta2'''   ) else None ,					adam_weight_decay=args.adam_weight_decay if hasattr(__a ,					'''adam_weight_decay'''   ) else None ,					adam_epsilon=args.adam_epsilon if hasattr(__a ,					'''adam_epsilon'''   ) else None ,					lr_scheduler=args.lr_scheduler if hasattr(__a ,					'''lr_scheduler'''   ) else None ,					lr_warmup_steps=args.lr_warmup_steps if hasattr(__a ,					'''lr_warmup_steps'''   ) else None ,					ema_inv_gamma=args.ema_inv_gamma if hasattr(__a ,					'''ema_inv_gamma'''   ) else None ,					ema_power=args.ema_power if hasattr(__a ,					'''ema_power'''   ) else None ,					ema_max_decay=args.ema_max_decay if hasattr(__a ,					'''ema_max_decay'''   ) else None ,					mixed_precision=args.mixed_precision ,					)

					UpperCamelCase__   :Union[str, Any]												= os.path.join(args.output_dir ,					'''README.md'''   )
					model_card.save(__a   )


def 			a      (			__a ,					__a = None   )  ->	int:



					'''simple docstring'''






					if resolved_file is None or commit_hash is not None:
										return commit_hash
					UpperCamelCase__   :str												= str(Path(__a   ).as_posix()   )
					UpperCamelCase__   :Union[str, Any]												= re.search(R'''snapshots/([^/]+)/''' ,					__a   )
					if search is None:
										return None
					UpperCamelCase__   :int												= search.groups()[0]
					return commit_hash if REGEX_COMMIT_HASH.match(__a   ) 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.
__snake_case							      =     os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
__snake_case							      =     os.path.join(hf_cache_home, '''diffusers''')


def 			a      (			__a = None ,					__a = None   )  ->	None:



					'''simple docstring'''






					if new_cache_dir is None:
										UpperCamelCase__   :str												= DIFFUSERS_CACHE
					if old_cache_dir is None:
										UpperCamelCase__   :Tuple												= old_diffusers_cache

					UpperCamelCase__   :Optional[int]												= Path(__a   ).expanduser()
					UpperCamelCase__   :Dict												= Path(__a   ).expanduser()
					for old_blob_path in old_cache_dir.glob('''**/blobs/*'''   ):
										if old_blob_path.is_file() and not old_blob_path.is_symlink():
															UpperCamelCase__   :Tuple												= new_cache_dir / old_blob_path.relative_to(__a   )
															new_blob_path.parent.mkdir(parents=__a ,					exist_ok=__a   )
															os.replace(__a ,					__a   )
															try:
																				os.symlink(__a ,					__a   )
															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).


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

if cache_version < 1:
		__snake_case							      =     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:
						__snake_case							      =     '''\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      (			__a ,					__a = None   )  ->	str:



					'''simple docstring'''






					if variant is not None:
										UpperCamelCase__   :List[str]												= weights_name.split('''.'''   )
										UpperCamelCase__   :Any												= splits[:-1] + [variant] + splits[-1:]
										UpperCamelCase__   :Dict												= '''.'''.join(__a   )

					return weights_name







def 			a      (			__a ,					*,
    __a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a ,					__a=None ,					)  ->	List[Any]:



					'''simple docstring'''






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



					'''simple docstring'''






					if len(__a   ) != len(__a   ):
										raise ValueError('''String lengths must match!'''   )

					UpperCamelCase__   :Union[str, Any]												= 0

					for chara, chara in zip(__a ,					__a   ):
										if chara != chara:
															count += 1

					return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()