




"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


__A   :  Optional[int]					=      {
    'Acehnese Arabic': 'ace_Arab',
    'Acehnese Latin': 'ace_Latn',
    'Mesopotamian Arabic': 'acm_Arab',
    'Ta\'izzi-Adeni Arabic': 'acq_Arab',
    'Tunisian Arabic': 'aeb_Arab',
    'Afrikaans': 'afr_Latn',
    'South Levantine Arabic': 'ajp_Arab',
    'Akan': 'aka_Latn',
    'Amharic': 'amh_Ethi',
    'North Levantine Arabic': 'apc_Arab',
    'Modern Standard Arabic': 'arb_Arab',
    'Modern Standard Arabic Romanized': 'arb_Latn',
    'Najdi Arabic': 'ars_Arab',
    'Moroccan Arabic': 'ary_Arab',
    'Egyptian Arabic': 'arz_Arab',
    'Assamese': 'asm_Beng',
    'Asturian': 'ast_Latn',
    'Awadhi': 'awa_Deva',
    'Central Aymara': 'ayr_Latn',
    'South Azerbaijani': 'azb_Arab',
    'North Azerbaijani': 'azj_Latn',
    'Bashkir': 'bak_Cyrl',
    'Bambara': 'bam_Latn',
    'Balinese': 'ban_Latn',
    'Belarusian': 'bel_Cyrl',
    'Bemba': 'bem_Latn',
    'Bengali': 'ben_Beng',
    'Bhojpuri': 'bho_Deva',
    'Banjar Arabic': 'bjn_Arab',
    'Banjar Latin': 'bjn_Latn',
    'Standard Tibetan': 'bod_Tibt',
    'Bosnian': 'bos_Latn',
    'Buginese': 'bug_Latn',
    'Bulgarian': 'bul_Cyrl',
    'Catalan': 'cat_Latn',
    'Cebuano': 'ceb_Latn',
    'Czech': 'ces_Latn',
    'Chokwe': 'cjk_Latn',
    'Central Kurdish': 'ckb_Arab',
    'Crimean Tatar': 'crh_Latn',
    'Welsh': 'cym_Latn',
    'Danish': 'dan_Latn',
    'German': 'deu_Latn',
    'Southwestern Dinka': 'dik_Latn',
    'Dyula': 'dyu_Latn',
    'Dzongkha': 'dzo_Tibt',
    'Greek': 'ell_Grek',
    'English': 'eng_Latn',
    'Esperanto': 'epo_Latn',
    'Estonian': 'est_Latn',
    'Basque': 'eus_Latn',
    'Ewe': 'ewe_Latn',
    'Faroese': 'fao_Latn',
    'Fijian': 'fij_Latn',
    'Finnish': 'fin_Latn',
    'Fon': 'fon_Latn',
    'French': 'fra_Latn',
    'Friulian': 'fur_Latn',
    'Nigerian Fulfulde': 'fuv_Latn',
    'Scottish Gaelic': 'gla_Latn',
    'Irish': 'gle_Latn',
    'Galician': 'glg_Latn',
    'Guarani': 'grn_Latn',
    'Gujarati': 'guj_Gujr',
    'Haitian Creole': 'hat_Latn',
    'Hausa': 'hau_Latn',
    'Hebrew': 'heb_Hebr',
    'Hindi': 'hin_Deva',
    'Chhattisgarhi': 'hne_Deva',
    'Croatian': 'hrv_Latn',
    'Hungarian': 'hun_Latn',
    'Armenian': 'hye_Armn',
    'Igbo': 'ibo_Latn',
    'Ilocano': 'ilo_Latn',
    'Indonesian': 'ind_Latn',
    'Icelandic': 'isl_Latn',
    'Italian': 'ita_Latn',
    'Javanese': 'jav_Latn',
    'Japanese': 'jpn_Jpan',
    'Kabyle': 'kab_Latn',
    'Jingpho': 'kac_Latn',
    'Kamba': 'kam_Latn',
    'Kannada': 'kan_Knda',
    'Kashmiri Arabic': 'kas_Arab',
    'Kashmiri Devanagari': 'kas_Deva',
    'Georgian': 'kat_Geor',
    'Central Kanuri Arabic': 'knc_Arab',
    'Central Kanuri Latin': 'knc_Latn',
    'Kazakh': 'kaz_Cyrl',
    'Kabiyè': 'kbp_Latn',
    'Kabuverdianu': 'kea_Latn',
    'Khmer': 'khm_Khmr',
    'Kikuyu': 'kik_Latn',
    'Kinyarwanda': 'kin_Latn',
    'Kyrgyz': 'kir_Cyrl',
    'Kimbundu': 'kmb_Latn',
    'Northern Kurdish': 'kmr_Latn',
    'Kikongo': 'kon_Latn',
    'Korean': 'kor_Hang',
    'Lao': 'lao_Laoo',
    'Ligurian': 'lij_Latn',
    'Limburgish': 'lim_Latn',
    'Lingala': 'lin_Latn',
    'Lithuanian': 'lit_Latn',
    'Lombard': 'lmo_Latn',
    'Latgalian': 'ltg_Latn',
    'Luxembourgish': 'ltz_Latn',
    'Luba-Kasai': 'lua_Latn',
    'Ganda': 'lug_Latn',
    'Luo': 'luo_Latn',
    'Mizo': 'lus_Latn',
    'Standard Latvian': 'lvs_Latn',
    'Magahi': 'mag_Deva',
    'Maithili': 'mai_Deva',
    'Malayalam': 'mal_Mlym',
    'Marathi': 'mar_Deva',
    'Minangkabau Arabic ': 'min_Arab',
    'Minangkabau Latin': 'min_Latn',
    'Macedonian': 'mkd_Cyrl',
    'Plateau Malagasy': 'plt_Latn',
    'Maltese': 'mlt_Latn',
    'Meitei Bengali': 'mni_Beng',
    'Halh Mongolian': 'khk_Cyrl',
    'Mossi': 'mos_Latn',
    'Maori': 'mri_Latn',
    'Burmese': 'mya_Mymr',
    'Dutch': 'nld_Latn',
    'Norwegian Nynorsk': 'nno_Latn',
    'Norwegian Bokmål': 'nob_Latn',
    'Nepali': 'npi_Deva',
    'Northern Sotho': 'nso_Latn',
    'Nuer': 'nus_Latn',
    'Nyanja': 'nya_Latn',
    'Occitan': 'oci_Latn',
    'West Central Oromo': 'gaz_Latn',
    'Odia': 'ory_Orya',
    'Pangasinan': 'pag_Latn',
    'Eastern Panjabi': 'pan_Guru',
    'Papiamento': 'pap_Latn',
    'Western Persian': 'pes_Arab',
    'Polish': 'pol_Latn',
    'Portuguese': 'por_Latn',
    'Dari': 'prs_Arab',
    'Southern Pashto': 'pbt_Arab',
    'Ayacucho Quechua': 'quy_Latn',
    'Romanian': 'ron_Latn',
    'Rundi': 'run_Latn',
    'Russian': 'rus_Cyrl',
    'Sango': 'sag_Latn',
    'Sanskrit': 'san_Deva',
    'Santali': 'sat_Olck',
    'Sicilian': 'scn_Latn',
    'Shan': 'shn_Mymr',
    'Sinhala': 'sin_Sinh',
    'Slovak': 'slk_Latn',
    'Slovenian': 'slv_Latn',
    'Samoan': 'smo_Latn',
    'Shona': 'sna_Latn',
    'Sindhi': 'snd_Arab',
    'Somali': 'som_Latn',
    'Southern Sotho': 'sot_Latn',
    'Spanish': 'spa_Latn',
    'Tosk Albanian': 'als_Latn',
    'Sardinian': 'srd_Latn',
    'Serbian': 'srp_Cyrl',
    'Swati': 'ssw_Latn',
    'Sundanese': 'sun_Latn',
    'Swedish': 'swe_Latn',
    'Swahili': 'swh_Latn',
    'Silesian': 'szl_Latn',
    'Tamil': 'tam_Taml',
    'Tatar': 'tat_Cyrl',
    'Telugu': 'tel_Telu',
    'Tajik': 'tgk_Cyrl',
    'Tagalog': 'tgl_Latn',
    'Thai': 'tha_Thai',
    'Tigrinya': 'tir_Ethi',
    'Tamasheq Latin': 'taq_Latn',
    'Tamasheq Tifinagh': 'taq_Tfng',
    'Tok Pisin': 'tpi_Latn',
    'Tswana': 'tsn_Latn',
    'Tsonga': 'tso_Latn',
    'Turkmen': 'tuk_Latn',
    'Tumbuka': 'tum_Latn',
    'Turkish': 'tur_Latn',
    'Twi': 'twi_Latn',
    'Central Atlas Tamazight': 'tzm_Tfng',
    'Uyghur': 'uig_Arab',
    'Ukrainian': 'ukr_Cyrl',
    'Umbundu': 'umb_Latn',
    'Urdu': 'urd_Arab',
    'Northern Uzbek': 'uzn_Latn',
    'Venetian': 'vec_Latn',
    'Vietnamese': 'vie_Latn',
    'Waray': 'war_Latn',
    'Wolof': 'wol_Latn',
    'Xhosa': 'xho_Latn',
    'Eastern Yiddish': 'ydd_Hebr',
    'Yoruba': 'yor_Latn',
    'Yue Chinese': 'yue_Hant',
    'Chinese Simplified': 'zho_Hans',
    'Chinese Traditional': 'zho_Hant',
    'Standard Malay': 'zsm_Latn',
    'Zulu': 'zul_Latn',
}




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     str       =      "facebook/nllb-200-distilled-600M"
				__UpperCAmelCase :     Tuple       =      (
				    "This is a tool that translates text from a language to another. It takes three inputs: `text`, which should "
				    "be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, "
				    "which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in "
				    "plain English, such as 'Romanian', or 'Albanian'. It returns the text translated in `tgt_lang`."
				)
				__UpperCAmelCase :     str       =      "translator"
				__UpperCAmelCase :     Optional[int]       =      AutoTokenizer
				__UpperCAmelCase :     Dict       =      AutoModelForSeqaSeqLM
				__UpperCAmelCase :     Tuple       =      LANGUAGE_CODES

				__UpperCAmelCase :     int       =      ["text", "text", "text"]
				__UpperCAmelCase :     int       =      ["text"]



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Any						):
						if src_lang not in self.lang_to_code:
								raise ValueError(f'{src_lang} is not a supported language.'						)
						if tgt_lang not in self.lang_to_code:
								raise ValueError(f'{tgt_lang} is not a supported language.'						)
						__lowercase				:		List[str]												=				self.lang_to_code[src_lang]
						__lowercase				:		List[str]												=				self.lang_to_code[tgt_lang]
						return self.pre_processor._build_translation_inputs(
						    lowercase__    ,   return_tensors="pt"    ,   src_lang=lowercase__    ,   tgt_lang=lowercase__						)



				def       snake_case    (     self					:	str    ,   lowercase__					:	Tuple						):
						return self.model.generate(**lowercase__						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	List[Any]						):
						return self.post_processor.decode(outputs[0].tolist()    ,   skip_special_tokens=lowercase__						)






"""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   :  List[Any]					=      get_logger(__name__)


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

def 						snake_case__       (					_lowerCamelCase = None				)			->str:






		"""simple docstring"""


		__lowercase				:		Optional[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(_lowerCamelCase,  _lowerCamelCase				):
				ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()				)
		elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
				ua += "; " + user_agent
		return ua

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None,  _lowerCamelCase = None				)			->List[Any]:






		"""simple docstring"""


		if token is None:
				__lowercase				:		Any												=				HfFolder.get_token()
		if organization is None:
				__lowercase				:		int												=				whoami(_lowerCamelCase				)["name"]
				return F'{username}/{model_id}'
		else:
				return F'{organization}/{model_id}'

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""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(_lowerCamelCase,  "local_rank"				) and args.local_rank not in [-1, 0]:
				return

		__lowercase				:		Tuple												=				args.hub_token if hasattr(_lowerCamelCase,  "hub_token"				) else None
		__lowercase				:		int												=				get_full_repo_name(_lowerCamelCase,  token=_lowerCamelCase				)

		__lowercase				:		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=_lowerCamelCase,  model_name=_lowerCamelCase,  repo_name=_lowerCamelCase,  dataset_name=args.dataset_name if hasattr(_lowerCamelCase,  "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(_lowerCamelCase,  "gradient_accumulation_steps"				) else None
		    ),  adam_betaa=args.adam_betaa if hasattr(_lowerCamelCase,  "adam_beta1"				) else None,  adam_betaa=args.adam_betaa if hasattr(_lowerCamelCase,  "adam_beta2"				) else None,  adam_weight_decay=args.adam_weight_decay if hasattr(_lowerCamelCase,  "adam_weight_decay"				) else None,  adam_epsilon=args.adam_epsilon if hasattr(_lowerCamelCase,  "adam_epsilon"				) else None,  lr_scheduler=args.lr_scheduler if hasattr(_lowerCamelCase,  "lr_scheduler"				) else None,  lr_warmup_steps=args.lr_warmup_steps if hasattr(_lowerCamelCase,  "lr_warmup_steps"				) else None,  ema_inv_gamma=args.ema_inv_gamma if hasattr(_lowerCamelCase,  "ema_inv_gamma"				) else None,  ema_power=args.ema_power if hasattr(_lowerCamelCase,  "ema_power"				) else None,  ema_max_decay=args.ema_max_decay if hasattr(_lowerCamelCase,  "ema_max_decay"				) else None,  mixed_precision=args.mixed_precision,  )

		__lowercase				:		Dict												=				os.path.join(args.output_dir,  "README.md"				)
		model_card.save(_lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->Tuple:






		"""simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase = None,  _lowerCamelCase = None				)			->None:






		"""simple docstring"""


		if new_cache_dir is None:
				__lowercase				:		int												=				DIFFUSERS_CACHE
		if old_cache_dir is None:
				__lowercase				:		Tuple												=				old_diffusers_cache

		__lowercase				:		str												=				Path(_lowerCamelCase				).expanduser()
		__lowercase				:		Optional[int]												=				Path(_lowerCamelCase				).expanduser()
		for old_blob_path in old_cache_dir.glob("**/blobs/*"				):
				if old_blob_path.is_file() and not old_blob_path.is_symlink():
						__lowercase				:		str												=				new_cache_dir / old_blob_path.relative_to(_lowerCamelCase				)
						new_blob_path.parent.mkdir(parents=_lowerCamelCase,  exist_ok=_lowerCamelCase				)
						os.replace(_lowerCamelCase,  _lowerCamelCase				)
						try:
								os.symlink(_lowerCamelCase,  _lowerCamelCase				)
						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   :  int					=      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   :  Optional[int]					=      int(f.read())
												except ValueError:
																		__A   :  Optional[Any]					=      0

if cache_version < 1:
						__A   :  Optional[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   :  Union[str, Any]					=      '\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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->str:






		"""simple docstring"""


		if variant is not None:
				__lowercase				:		str												=				weights_name.split("."				)
				__lowercase				:		str												=				splits[:-1] + [variant] + splits[-1:]
				__lowercase				:		Dict												=				".".join(_lowerCamelCase				)

		return weights_name





def 						snake_case__       (					_lowerCamelCase,  *,
    _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase=None,  )			->str:






		"""simple docstring"""


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