


'''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


__lowerCAmelCase       		=      get_logger(__name__)


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





def 		_UpperCAmelCase   (							__A							:   List[str] = None      ):
				a_		:  Dict											=      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 		_UpperCAmelCase   (							__A							:   Any						,		__A							:   List[str] = None						,		__A							:   Union[str, Any] = None      ):





				if token is None:
								a_		:  List[str]											=      HfFolder.get_token()
				if organization is None:
								a_		:  List[Any]											=      whoami(A_      )['''name''']
								return f'{username}/{model_id}'
				else:
								return f'{organization}/{model_id}'





def 		_UpperCAmelCase   (							__A							:   List[str]						,		__A							:   int      ):





				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

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

				a_		:  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=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						,		)

				a_		:  Optional[Any]											=      os.path.join(args.output_dir						,		'''README.md'''      )
				model_card.save(A_      )





def 		_UpperCAmelCase   (							__A							:   List[Any]						,		__A							:   List[str] = None      ):





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





def 		_UpperCAmelCase   (							__A							:   Optional[int] = None						,		__A							:   Dict = None      ):





				if new_cache_dir is None:
								a_		:  Optional[int]											=      DIFFUSERS_CACHE
				if old_cache_dir is None:
								a_		:  Union[str, Any]											=      old_diffusers_cache

				a_		:  List[Any]											=      Path(A_      ).expanduser()
				a_		:  Union[str, Any]											=      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():
												a_		:  Optional[Any]											=      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).


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

if cache_version < 1:
		__lowerCAmelCase       		=      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:
						__lowerCAmelCase       		=      '\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 		_UpperCAmelCase   (							__A							:   List[str]						,		__A							:   Optional[int] = None      ):





				if variant is not None:
								a_		:  Any											=      weights_name.split('''.'''      )
								a_		:  Optional[int]											=      splits[:-1] + [variant] + splits[-1:]
								a_		:  Tuple											=      '''.'''.join(A_      )

				return weights_name





def 		_UpperCAmelCase   (							__A							:   Optional[Any]						,		*,
    __A							:   Union[str, Any]						,		__A							:   int						,		__A							:   int						,		__A							:   Union[str, Any]						,		__A							:   Tuple						,		__A							:   Union[str, Any]						,		__A							:   Optional[int]						,		__A							:   str						,		__A							:   Union[str, Any]						,		__A							:   Optional[Any]						,		__A							:   Union[str, Any]=None						,		):
				a_		:  Optional[Any]											=      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
												a_		:  Union[str, Any]											=      os.path.join(A_						,		A_      )
												return model_file
								elif subfolder is not None and os.path.isfile(
								    os.path.join(A_						,		A_						,		A_      )      ):
												a_		:  List[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:
																a_		:  Optional[int]											=      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
												a_		:  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'''







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE_       )
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				field(default="image-classification"	,   metadata={"include_in_asdict_even_if_is_default": True}       )
			snake_case__  =				Features({"image": Image()}       )
			snake_case__  =				Features({"labels": ClassLabel}       )
			snake_case__  =				"image"
			snake_case__  =				"labels"






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							if self.label_column not in features:
											raise ValueError(f'Column {self.label_column} is not present in features.'					)
							if not isinstance(features[self.label_column]  ,						__SCREAMING_SNAKE_CASE					):
											raise ValueError(f'Column {self.label_column} is not a ClassLabel.'					)
							a_		:  Optional[int]											=      copy.deepcopy(self					)
							a_		:  int											=      self.label_schema.copy()
							a_		:  Tuple											=      features[self.label_column]
							a_		:  str											=      label_schema
							return task_template







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}




