


"""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


lowercase  							=					get_logger(__name__)


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




def 	UpperCAmelCase     (		A	:						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 	UpperCAmelCase     (		A	:						str     ,				A	:						Optional[str] = None     ,				A	:						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 	UpperCAmelCase     (		A	:						List[str]     ,				A	:						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

		_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 	UpperCAmelCase     (		A	:						Optional[str]     ,				A	:						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.
lowercase  							=					os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
lowercase  							=					os.path.join(hf_cache_home, '''diffusers''')




def 	UpperCAmelCase     (		A	:						Optional[str] = None     ,				A	:						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).


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

if cache_version < 1:
					lowercase  							=					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:
															lowercase  							=					'''\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	:						str     ,				A	:						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 	UpperCAmelCase     (		A	:						Union[str, Any]     ,				*,
    A	:						Any     ,				A	:						Tuple     ,				A	:						List[Any]     ,				A	:						List[str]     ,				A	:						int     ,				A	:						str     ,				A	:						int     ,				A	:						Tuple     ,				A	:						str     ,				A	:						str     ,				A	:						int=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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase  							=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					['''MvpTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''MvpForCausalLM''',
					    '''MvpForConditionalGeneration''',
					    '''MvpForQuestionAnswering''',
					    '''MvpForSequenceClassification''',
					    '''MvpModel''',
					    '''MvpPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


