


'''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


__snake_case											=					get_logger(__name__)


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


def       a				(      __a = None       )  ->     str:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			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       a				(      __a			, __a = None			, __a = None       )  ->     List[Any]:



					'''simple docstring'''






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


def       a				(      __a			, __a       )  ->     int:



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

					UpperCamelCase__       :Optional[Any]							 =			args.hub_token if hasattr(__a			, '''hub_token'''       ) else None
					UpperCamelCase__       :Tuple							 =			get_full_repo_name(__a			, token=__a       )

					UpperCamelCase__       :List[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			, )

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


def       a				(      __a			, __a = None       )  ->     int:



					'''simple docstring'''






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


def       a				(      __a = None			, __a = None       )  ->     None:



					'''simple docstring'''






					if new_cache_dir is None:
										UpperCamelCase__       :Dict							 =			DIFFUSERS_CACHE
					if old_cache_dir is None:
										UpperCamelCase__       :int							 =			old_diffusers_cache

					UpperCamelCase__       :Optional[int]							 =			Path(__a       ).expanduser()
					UpperCamelCase__       :List[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():
															UpperCamelCase__       :List[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).


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

if cache_version < 1:
		__snake_case											=					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:
						__snake_case											=					'''\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       a				(      __a			, __a = None       )  ->     str:



					'''simple docstring'''






					if variant is not None:
										UpperCamelCase__       :Union[str, Any]							 =			weights_name.split('''.'''       )
										UpperCamelCase__       :Optional[int]							 =			splits[:-1] + [variant] + splits[-1:]
										UpperCamelCase__       :List[str]							 =			'''.'''.join(__a       )

					return weights_name







def       a				(      __a			, *,
    __a			, __a			, __a			, __a			, __a			, __a			, __a			, __a			, __a			, __a			, __a=None			, )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			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
															UpperCamelCase__       :Any							 =			os.path.join(__a			, __a       )
															return model_file
										elif subfolder is not None and os.path.isfile(
										    os.path.join(__a			, __a			, __a       )       ):
															UpperCamelCase__       :Union[str, 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:
																				UpperCamelCase__       :str							 =			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
															UpperCamelCase__       :Tuple							 =			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 os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase__       :List[Any]							 =			logging.get_verbosity()

							logging.set_verbosity_error()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							logging.set_verbosity_warning()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							logging.set_verbosity_info()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							logging.set_verbosity_debug()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							# restore to the original level
							logging.set_verbosity(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			logging.get_verbosity()

							UpperCamelCase__       :Union[str, Any]							 =			logging.get_logger('''transformers.models.bart.tokenization_bart'''							)
							UpperCamelCase__       :Optional[Any]							 =			'''Testing 1, 2, 3'''

							# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
							if level_origin <= logging.WARNING:
												with CaptureLogger(UpperCamelCase_							) as cl:
																	logger.warning(UpperCamelCase_							)
												self.assertEqual(cl.out   ,       msg + '''\n'''							)

							# this is setting the level for all of `transformers.*` loggers
							logging.set_verbosity_error()

							# should not be able to log warnings
							with CaptureLogger(UpperCamelCase_							) as cl:
												logger.warning(UpperCamelCase_							)
							self.assertEqual(cl.out   ,       ''''''							)

							# should be able to log warnings again
							logging.set_verbosity_warning()
							with CaptureLogger(UpperCamelCase_							) as cl:
												logger.warning(UpperCamelCase_							)
							self.assertEqual(cl.out   ,       msg + '''\n'''							)

							# restore to the original level
							logging.set_verbosity(UpperCamelCase_							)




		@mockenv(TRANSFORMERS_VERBOSITY='''error'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase__       :Tuple							 =			logging.get_logger('''transformers.models.bart.tokenization_bart'''							)

							UpperCamelCase__       :Any							 =			os.getenv('''TRANSFORMERS_VERBOSITY'''   ,       UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			logging.log_levels[env_level_str]

							UpperCamelCase__       :int							 =			logging.get_verbosity()
							self.assertEqual(
							    UpperCamelCase_   ,       UpperCamelCase_   ,       F'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''   ,       )

							# restore to the original level
							UpperCamelCase__       :Union[str, Any]							 =			''''''
							transformers.utils.logging._reset_library_root_logger()




		@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase__       :Dict							 =			logging.logging.getLogger()
							with CaptureLogger(UpperCamelCase_							) as cl:
												# this action activates the env var
												logging.get_logger('''transformers.models.bart.tokenization_bart'''							)
							self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''   ,       cl.out							)

							# no need to restore as nothing was changed




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase__       :Optional[int]							 =			logging.get_logger('''transformers.models.bart.tokenization_bart'''							)
							UpperCamelCase__       :int							 =			'''Testing 1, 2, 3'''

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''							):
												# nothing should be logged as env var disables this method
												with CaptureLogger(UpperCamelCase_							) as cl:
																	logger.warning_advice(UpperCamelCase_							)
												self.assertEqual(cl.out   ,       ''''''							)

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''							):
												# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
												with CaptureLogger(UpperCamelCase_							) as cl:
																	logger.warning_advice(UpperCamelCase_							)
												self.assertEqual(cl.out   ,       msg + '''\n'''							)



def       a				(      )  ->     str:



					'''simple docstring'''






					disable_progress_bar()
					assert are_progress_bars_disabled()

					enable_progress_bar()
					assert not are_progress_bars_disabled()