



"""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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[Dict, str, None] = None			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Optional[str] = None				,    SCREAMING_SNAKE_CASE__ :  Optional[str] = None			):



					"""simple docstring"""





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







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

					snake_case_					:		Optional[int] 					=					args.hub_token if hasattr(SCREAMING_SNAKE_CASE__				,    """hub_token"""			) else None
					snake_case_					:		Union[str, Any] 					=					get_full_repo_name(SCREAMING_SNAKE_CASE__				,    token=SCREAMING_SNAKE_CASE__			)

					snake_case_					:		Optional[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=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				,    )

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[str]				,    SCREAMING_SNAKE_CASE__ :  Optional[str] = None			):



					"""simple docstring"""





					if resolved_file is None or commit_hash is not None:
										return commit_hash
					snake_case_					:		Optional[int] 					=					str(Path(SCREAMING_SNAKE_CASE__			).as_posix()			)
					snake_case_					:		List[str] 					=					re.search(R"""snapshots/([^/]+)/"""				,    SCREAMING_SNAKE_CASE__			)
					if search is None:
										return None
					snake_case_					:		Union[str, Any] 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[str] = None				,    SCREAMING_SNAKE_CASE__ :  Optional[str] = None			):



					"""simple docstring"""





					if new_cache_dir is None:
										snake_case_					:		Tuple 					=					DIFFUSERS_CACHE
					if old_cache_dir is None:
										snake_case_					:		List[Any] 					=					old_diffusers_cache

					snake_case_					:		List[str] 					=					Path(SCREAMING_SNAKE_CASE__			).expanduser()
					snake_case_					:		Union[str, Any] 					=					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():
															snake_case_					:		Union[str, Any] 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Optional[str] = None			):



					"""simple docstring"""





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

					return weights_name







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    *,
    SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  int=None				,    ):



					"""simple docstring"""





					snake_case_					:		int 					=					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
															snake_case_					:		str 					=					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__			)			):
															snake_case_					:		List[str] 					=					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:
																				snake_case_					:		Tuple 					=					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
															snake_case_					:		List[str] 					=					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"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""upernet"""







						def __init__(self   ,		lowercase__=None   ,		lowercase__=5_12   ,		lowercase__=0.02   ,		lowercase__=[1, 2, 3, 6]   ,		lowercase__=True   ,		lowercase__=0.4   ,		lowercase__=3_84   ,		lowercase__=2_56   ,		lowercase__=1   ,		lowercase__=False   ,		lowercase__=2_55   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )

											if backbone_config is None:
																logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""       )
																snake_case_					:		List[Any] 					=					CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
											elif isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		Tuple 					=					backbone_config.get("""model_type"""       )
																snake_case_					:		List[str] 					=					CONFIG_MAPPING[backbone_model_type]
																snake_case_					:		List[Any] 					=					config_class.from_dict(lowercase__       )

											snake_case_					:		List[Any] 					=					backbone_config
											snake_case_					:		Optional[Any] 					=					hidden_size
											snake_case_					:		Any 					=					initializer_range
											snake_case_					:		str 					=					pool_scales
											snake_case_					:		Dict 					=					use_auxiliary_head
											snake_case_					:		str 					=					auxiliary_loss_weight
											snake_case_					:		List[str] 					=					auxiliary_in_channels
											snake_case_					:		Optional[Any] 					=					auxiliary_channels
											snake_case_					:		Any 					=					auxiliary_num_convs
											snake_case_					:		List[Any] 					=					auxiliary_concat_input
											snake_case_					:		List[str] 					=					loss_ignore_index







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					copy.deepcopy(self.__dict__       )
											snake_case_					:		Union[str, Any] 					=					self.backbone_config.to_dict()
											snake_case_					:		Any 					=					self.__class__.model_type
											return output




