


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			:			Union[str, Any]												=					get_logger(__name__)


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


def 		lowercase_	(  _UpperCamelCase = None	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase = None							,      _UpperCamelCase = None	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


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

			__lowercase			       = args.hub_token if hasattr(_UpperCamelCase							,      '''hub_token'''	) else None
			__lowercase			       = get_full_repo_name(_UpperCamelCase							,      token=_UpperCamelCase	)

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

			__lowercase			       = os.path.join(args.output_dir							,      '''README.md'''	)
			model_card.save(_UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase = None	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase = None							,      _UpperCamelCase = None	):


			'''simple docstring'''







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

			__lowercase			       = Path(_UpperCamelCase	).expanduser()
			__lowercase			       = Path(_UpperCamelCase	).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			       = new_cache_dir / old_blob_path.relative_to(_UpperCamelCase	)
									new_blob_path.parent.mkdir(parents=_UpperCamelCase							,      exist_ok=_UpperCamelCase	)
									os.replace(_UpperCamelCase							,      _UpperCamelCase	)
									try:
												os.symlink(_UpperCamelCase							,      _UpperCamelCase	)
									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			:			str												=					os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
	a			:			Union[str, Any]												=					0
else:
	with open(cache_version_file) as f:
		try:
			a			:			Dict												=					int(f.read())
		except ValueError:
			a			:			int												=					0

if cache_version < 1:
	a			:			List[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			:			List[str]												=					'''\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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase = None	):


			'''simple docstring'''







			if variant is not None:
						__lowercase			       = weights_name.split('''.'''	)
						__lowercase			       = splits[:-1] + [variant] + splits[-1:]
						__lowercase			       = '''.'''.join(_UpperCamelCase	)

			return weights_name





def 		lowercase_	(  _UpperCamelCase							,      *,
    _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None							,      ):


			'''simple docstring'''







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






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

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__					)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					field(default="automatic-speech-recognition"  ,  metadata={"include_in_asdict_even_if_is_default": True}					)
					__UpperCAmelCase							  =					Features({"audio": Audio()}					)
					__UpperCAmelCase							  =					Features({"transcription": Value("string"					)}					)
					__UpperCAmelCase							  =					"audio"
					__UpperCAmelCase							  =					"transcription"






					def A   (					self			,							snake_case_    ) ->      Any:







								'''simple docstring'''

								if self.audio_column not in features:
											raise ValueError(F'Column {self.audio_column} is not present in features.'    )
								if not isinstance(features[self.audio_column]			,							snake_case_    ):
											raise ValueError(F'Column {self.audio_column} is not an Audio type.'    )
								__lowercase			       = copy.deepcopy(self    )
								__lowercase			       = self.input_schema.copy()
								__lowercase			       = features[self.audio_column]
								__lowercase			       = input_schema
								return task_template






					@property
					def A   (					self    ) ->      Dict[str, str]:







								'''simple docstring'''

								return {self.audio_column: "audio", self.transcription_column: "transcription"}


