



"""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   :							Any										=   get_logger(__name__)


__A   :							Union[str, Any]										=   Path(__file__).parent / "model_card_template.md"
__A   :							int										=   uuida().hex
__A   :							Optional[Any]										=   os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
__A   :							Union[str, Any]										=   os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
__A   :							Union[str, Any]										=   HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[Dict, str, None] = None				):






				'''simple docstring'''



				_UpperCAmelCase					=    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Optional[str] = None					,     _SCREAMING_SNAKE_CASE   :						Optional[str] = None				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Optional[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(_SCREAMING_SNAKE_CASE					,     '''local_rank'''				) and args.local_rank not in [-1, 0]:
								return

				_UpperCAmelCase					=    args.hub_token if hasattr(_SCREAMING_SNAKE_CASE					,     '''hub_token'''				) else None
				_UpperCAmelCase					=    get_full_repo_name(_SCREAMING_SNAKE_CASE					,     token=_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    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					,     )

				_UpperCAmelCase					=    os.path.join(args.output_dir					,     '''README.md'''				)
				model_card.save(_SCREAMING_SNAKE_CASE				)







def  lowercase	(      _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
				_UpperCAmelCase					=    str(Path(_SCREAMING_SNAKE_CASE				).as_posix()				)
				_UpperCAmelCase					=    re.search(r'''snapshots/([^/]+)/'''					,     _SCREAMING_SNAKE_CASE				)
				if search is None:
								return None
				_UpperCAmelCase					=    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   :							Dict										=   os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
__A   :							Optional[int]										=   os.path.join(hf_cache_home, "diffusers")







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[str] = None					,     _SCREAMING_SNAKE_CASE   :						Optional[str] = None				):






				'''simple docstring'''



				if new_cache_dir is None:
								_UpperCAmelCase					=    DIFFUSERS_CACHE
				if old_cache_dir is None:
								_UpperCAmelCase					=    old_diffusers_cache

				_UpperCAmelCase					=    Path(_SCREAMING_SNAKE_CASE				).expanduser()
				_UpperCAmelCase					=    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():
												_UpperCAmelCase					=    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   :							Optional[Any]										=   os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
					__A   :							Optional[Any]										=   0
else:
					with open(cache_version_file) as f:
										try:
															__A   :							Tuple										=   int(f.read())
										except ValueError:
															__A   :							int										=   0

if cache_version < 1:
					__A   :							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:
															__A   :							Optional[int]										=   "\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  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Optional[str] = None				):






				'''simple docstring'''



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

				return weights_name







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






				'''simple docstring'''



				_UpperCAmelCase					=    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
												_UpperCAmelCase					=    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				)				):
												_UpperCAmelCase					=    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:
																_UpperCAmelCase					=    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
												_UpperCAmelCase					=    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"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A   :							Optional[int]										=   logging.get_logger(__name__)

__A   :							Optional[Any]										=   {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 			_a (					lowerCAmelCase			,							lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""bit"""
			UpperCamelCase__			      =	["""preactivation""", """bottleneck"""]
			UpperCamelCase__			      =	["""SAME""", """VALID"""]



			def __init__(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    Optional[int]=6_4				,   __UpperCamelCase     :    List[Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,   __UpperCamelCase     :    Any=[3, 4, 6, 3]				,   __UpperCamelCase     :    str="preactivation"				,   __UpperCamelCase     :    Union[str, Any]="relu"				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Dict=3_2				,   __UpperCamelCase     :    List[str]=0.0				,   __UpperCamelCase     :    Any=False				,   __UpperCamelCase     :    Union[str, Any]=3_2				,   __UpperCamelCase     :    List[str]=1				,   __UpperCamelCase     :    List[str]=None				,   __UpperCamelCase     :    Optional[Any]=None				,   **__UpperCamelCase     :    List[str]				,   )->List[Any]:
							super().__init__(**__UpperCamelCase   )
							if layer_type not in self.layer_types:
											raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
							if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
															_UpperCAmelCase					=    global_padding.upper()
											else:
															raise ValueError(F'Padding strategy {global_padding} not supported'   )
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    embedding_size
							_UpperCAmelCase					=    hidden_sizes
							_UpperCAmelCase					=    depths
							_UpperCAmelCase					=    layer_type
							_UpperCAmelCase					=    hidden_act
							_UpperCAmelCase					=    global_padding
							_UpperCAmelCase					=    num_groups
							_UpperCAmelCase					=    drop_path_rate
							_UpperCAmelCase					=    embedding_dynamic_padding
							_UpperCAmelCase					=    output_stride
							_UpperCAmelCase					=    width_factor

							_UpperCAmelCase					=    ['''stem'''] + [F'stage{idx}' for idx in range(1				,   len(__UpperCamelCase   ) + 1   )]
							_UpperCAmelCase		,						_UpperCAmelCase					=    get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase				,   out_indices=__UpperCamelCase				,   stage_names=self.stage_names   )



