




"""simple docstring"""







import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402


__A   :  Dict					=      {
    'return_dict': False,
    'output_hidden_states': True,
    'output_attentions': True,
    'torchscript': True,
    'torch_dtype': 'float16',
    'use_bfloat16': True,
    'tf_legacy_loss': True,
    'pruned_heads': {'a': 1},
    'tie_word_embeddings': False,
    'is_decoder': True,
    'cross_attention_hidden_size': 1_2_8,
    'add_cross_attention': True,
    'tie_encoder_decoder': True,
    'max_length': 5_0,
    'min_length': 3,
    'do_sample': True,
    'early_stopping': True,
    'num_beams': 3,
    'num_beam_groups': 3,
    'diversity_penalty': 0.5,
    'temperature': 2.0,
    'top_k': 1_0,
    'top_p': 0.7,
    'typical_p': 0.2,
    'repetition_penalty': 0.8,
    'length_penalty': 0.8,
    'no_repeat_ngram_size': 5,
    'encoder_no_repeat_ngram_size': 5,
    'bad_words_ids': [1, 2, 3],
    'num_return_sequences': 3,
    'chunk_size_feed_forward': 5,
    'output_scores': True,
    'return_dict_in_generate': True,
    'forced_bos_token_id': 2,
    'forced_eos_token_id': 3,
    'remove_invalid_values': True,
    'architectures': ['BertModel'],
    'finetuning_task': 'translation',
    'id2label': {0: 'label'},
    'label2id': {'label': '0'},
    'tokenizer_class': 'BertTokenizerFast',
    'prefix': 'prefix',
    'bos_token_id': 6,
    'pad_token_id': 7,
    'eos_token_id': 8,
    'sep_token_id': 9,
    'decoder_start_token_id': 1_0,
    'exponential_decay_length_penalty': (5, 1.01),
    'suppress_tokens': [0, 1],
    'begin_suppress_tokens': 2,
    'task_specific_params': {'translation': 'some_params'},
    'problem_type': 'regression',
}




@is_staging_test
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				@classmethod
				def       snake_case    (     cls					:	Tuple						):
						__lowercase				:		Tuple												=				TOKEN
						HfFolder.save_token(lowercase__						)



				@classmethod
				def       snake_case    (     cls					:	Any						):
						try:
								delete_repo(token=cls._token    ,   repo_id="test-config"						)
						except HTTPError:
								pass

						try:
								delete_repo(token=cls._token    ,   repo_id="valid_org/test-config-org"						)
						except HTTPError:
								pass

						try:
								delete_repo(token=cls._token    ,   repo_id="test-dynamic-config"						)
						except HTTPError:
								pass



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		List[Any]												=				BertConfig(
						    vocab_size=9_9    ,   hidden_size=3_2    ,   num_hidden_layers=5    ,   num_attention_heads=4    ,   intermediate_size=3_7						)
						config.push_to_hub("test-config"    ,   use_auth_token=self._token						)

						__lowercase				:		Optional[int]												=				BertConfig.from_pretrained(f'{USER}/test-config'						)
						for k, v in config.to_dict().items():
								if k != "transformers_version":
										self.assertEqual(lowercase__    ,   getattr(lowercase__    ,   lowercase__						)						)

        # Reset repo
						delete_repo(token=self._token    ,   repo_id="test-config"						)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
								config.save_pretrained(lowercase__    ,   repo_id="test-config"    ,   push_to_hub=lowercase__    ,   use_auth_token=self._token						)

						__lowercase				:		Tuple												=				BertConfig.from_pretrained(f'{USER}/test-config'						)
						for k, v in config.to_dict().items():
								if k != "transformers_version":
										self.assertEqual(lowercase__    ,   getattr(lowercase__    ,   lowercase__						)						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		Optional[int]												=				BertConfig(
						    vocab_size=9_9    ,   hidden_size=3_2    ,   num_hidden_layers=5    ,   num_attention_heads=4    ,   intermediate_size=3_7						)
						config.push_to_hub("valid_org/test-config-org"    ,   use_auth_token=self._token						)

						__lowercase				:		Any												=				BertConfig.from_pretrained("valid_org/test-config-org"						)
						for k, v in config.to_dict().items():
								if k != "transformers_version":
										self.assertEqual(lowercase__    ,   getattr(lowercase__    ,   lowercase__						)						)

        # Reset repo
						delete_repo(token=self._token    ,   repo_id="valid_org/test-config-org"						)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
								config.save_pretrained(
								    lowercase__    ,   repo_id="valid_org/test-config-org"    ,   push_to_hub=lowercase__    ,   use_auth_token=self._token						)

						__lowercase				:		int												=				BertConfig.from_pretrained("valid_org/test-config-org"						)
						for k, v in config.to_dict().items():
								if k != "transformers_version":
										self.assertEqual(lowercase__    ,   getattr(lowercase__    ,   lowercase__						)						)



				def       snake_case    (     self					:	Any						):
						CustomConfig.register_for_auto_class()
						__lowercase				:		Union[str, Any]												=				CustomConfig(attribute=4_2						)

						config.push_to_hub("test-dynamic-config"    ,   use_auth_token=self._token						)

						# This has added the proper auto_map field to the config
						self.assertDictEqual(config.auto_map    ,   {"AutoConfig": "custom_configuration.CustomConfig"}						)

						__lowercase				:		List[str]												=				AutoConfig.from_pretrained(f'{USER}/test-dynamic-config'    ,   trust_remote_code=lowercase__						)
						# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
						self.assertEqual(new_config.__class__.__name__    ,   "CustomConfig"						)
						self.assertEqual(new_config.attribute    ,   4_2						)





class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		str												=				GPTaConfig()

						# attempt to modify each of int/float/bool/str config records and verify they were updated
						__lowercase				:		Union[str, Any]												=				c.n_embd + 1  # int
						__lowercase				:		List[Any]												=				c.resid_pdrop + 1.0  # float
						__lowercase				:		int												=				not c.scale_attn_weights  # bool
						__lowercase				:		Tuple												=				c.summary_type + "foo"  # str
						c.update_from_string(
						    f'n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'						)
						self.assertEqual(lowercase__    ,   c.n_embd    ,   "mismatch for key: n_embd"						)
						self.assertEqual(lowercase__    ,   c.resid_pdrop    ,   "mismatch for key: resid_pdrop"						)
						self.assertEqual(lowercase__    ,   c.scale_attn_weights    ,   "mismatch for key: scale_attn_weights"						)
						self.assertEqual(lowercase__    ,   c.summary_type    ,   "mismatch for key: summary_type"						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		Optional[int]												=				PretrainedConfig()
						__lowercase				:		Tuple												=				[key for key in base_config.__dict__ if key not in config_common_kwargs]
						# If this part of the test fails, you have arguments to addin config_common_kwargs above.
						self.assertListEqual(
						    lowercase__    ,   ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]						)
						__lowercase				:		Any												=				[key for key, value in config_common_kwargs.items() if value == getattr(lowercase__    ,   lowercase__						)]
						if len(lowercase__						) > 0:
								raise ValueError(
								    "The following keys are set with the default values in"
								    " `test_configuration_common.config_common_kwargs` pick another value for them:"
								    f' {", ".join(lowercase__						)}.'						)



				def       snake_case    (     self					:	Optional[int]						):
						with self.assertRaises(lowercase__						):
								# config is in subfolder, the following should not work without specifying the subfolder
								__lowercase				:		Tuple												=				BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"						)

						__lowercase				:		int												=				BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"    ,   subfolder="bert"						)

						self.assertIsNotNone(lowercase__						)



				def       snake_case    (     self					:	List[str]						):
						# A mock response for an HTTP head request to emulate server down
						__lowercase				:		Optional[Any]												=				mock.Mock()
						__lowercase				:		int												=				5_0_0
						__lowercase				:		Dict												=				{}
						__lowercase				:		Optional[int]												=				HTTPError
						__lowercase				:		str												=				{}

						# Download this model to make sure it's in the cache.
						__lowercase				:		Union[str, Any]												=				BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"						)

						# Under the mock environment we get a 500 error when trying to reach the model.
						with mock.patch("requests.Session.request"    ,   return_value=lowercase__						) as mock_head:
								__lowercase				:		Union[str, Any]												=				BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"						)
								# This check we did call the fake head request
								mock_head.assert_called()



				def       snake_case    (     self					:	Any						):
						# This test is for deprecated behavior and can be removed in v5
						__lowercase				:		List[str]												=				BertConfig.from_pretrained(
						    "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"						)



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Union[str, Any]												=				AutoConfig.from_pretrained("bert-base-cased"						)
						__lowercase				:		Optional[int]												=				["config.4.0.0.json"]

						with tempfile.TemporaryDirectory() as tmp_dir:
								configuration.save_pretrained(lowercase__						)
								__lowercase				:		Optional[int]												=				2
								json.dump(configuration.to_dict()    ,   open(os.path.join(lowercase__    ,   "config.4.0.0.json"						)    ,   "w"						)						)

								# This should pick the new configuration file as the version of Transformers is > 4.0.0
								__lowercase				:		Optional[int]												=				AutoConfig.from_pretrained(lowercase__						)
								self.assertEqual(new_configuration.hidden_size    ,   2						)

								# Will need to be adjusted if we reach v42 and this test is still here.
								# Should pick the old configuration file as the version of Transformers is < 4.42.0
								__lowercase				:		Any												=				["config.42.0.0.json"]
								__lowercase				:		List[str]												=				7_6_8
								configuration.save_pretrained(lowercase__						)
								shutil.move(os.path.join(lowercase__    ,   "config.4.0.0.json"						)    ,   os.path.join(lowercase__    ,   "config.42.0.0.json"						)						)
								__lowercase				:		List[Any]												=				AutoConfig.from_pretrained(lowercase__						)
								self.assertEqual(new_configuration.hidden_size    ,   7_6_8						)



				def       snake_case    (     self					:	Optional[Any]						):
						# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
						__lowercase				:		int												=				"hf-internal-testing/test-two-configs"

						import transformers as new_transformers

						__lowercase				:		List[str]												=				"v4.0.0"
						__lowercase						,__lowercase				:		Optional[int]												=				new_transformers.models.auto.AutoConfig.from_pretrained(
						    lowercase__    ,   return_unused_kwargs=lowercase__						)
						self.assertEqual(new_configuration.hidden_size    ,   2						)
						# This checks `_configuration_file` ia not kept in the kwargs by mistake.
						self.assertDictEqual(lowercase__    ,   {}						)

						# Testing an older version by monkey-patching the version in the module it's used.
						import transformers as old_transformers

						__lowercase				:		List[str]												=				"v3.0.0"
						__lowercase				:		List[str]												=				old_transformers.models.auto.AutoConfig.from_pretrained(lowercase__						)
						self.assertEqual(old_configuration.hidden_size    ,   7_6_8						)






"""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}'				)
