






'''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


UpperCamelCase     :   List[Any]	      =		get_logger(__name__)


UpperCamelCase     :   List[str]	      =		Path(__file__).parent / 'model_card_template.md'
UpperCamelCase     :   List[str]	      =		uuida().hex
UpperCamelCase     :   Any	      =		os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase     :   Any	      =		os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase     :   Optional[Any]	      =		HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'
def     A__      (					__lowerCAmelCase		:      Union[Dict, str, None] = None    ):
						lowerCamelCase__								=	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     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      Optional[str] = None ,     __lowerCAmelCase		:      Optional[str] = None    ):





						if token is None:
												lowerCamelCase__								=	HfFolder.get_token()
						if organization is None:
												lowerCamelCase__								=	whoami(SCREAMING_SNAKE_CASE_    )["""name"""]
												return F'''{username}/{model_id}'''
						else:
												return F'''{organization}/{model_id}'''
def     A__      (					__lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      Dict    ):





						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

						lowerCamelCase__								=	args.hub_token if hasattr(SCREAMING_SNAKE_CASE_ ,     """hub_token"""    ) else None
						lowerCamelCase__								=	get_full_repo_name(SCREAMING_SNAKE_CASE_ ,     token=SCREAMING_SNAKE_CASE_    )

						lowerCamelCase__								=	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 ,     )

						lowerCamelCase__								=	os.path.join(args.output_dir ,     """README.md"""    )
						model_card.save(SCREAMING_SNAKE_CASE_    )
def     A__      (					__lowerCAmelCase		:      Optional[str] ,     __lowerCAmelCase		:      Optional[str] = None    ):





						if resolved_file is None or commit_hash is not None:
												return commit_hash
						lowerCamelCase__								=	str(Path(SCREAMING_SNAKE_CASE_    ).as_posix()    )
						lowerCamelCase__								=	re.search(R"""snapshots/([^/]+)/""" ,     SCREAMING_SNAKE_CASE_    )
						if search is None:
												return None
						lowerCamelCase__								=	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.
UpperCamelCase     :   Tuple	      =		os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
UpperCamelCase     :   Optional[Any]	      =		os.path.join(hf_cache_home, 'diffusers')
def     A__      (					__lowerCAmelCase		:      Optional[str] = None ,     __lowerCAmelCase		:      Optional[str] = None    ):





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

						lowerCamelCase__								=	Path(SCREAMING_SNAKE_CASE_    ).expanduser()
						lowerCamelCase__								=	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():
																		lowerCamelCase__								=	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).


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

if cache_version < 1:
							UpperCamelCase     :   Dict	      =		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:
																					UpperCamelCase     :   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     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      Optional[str] = None    ):





						if variant is not None:
												lowerCamelCase__								=	weights_name.split("""."""    )
												lowerCamelCase__								=	splits[:-1] + [variant] + splits[-1:]
												lowerCamelCase__								=	""".""".join(SCREAMING_SNAKE_CASE_    )

						return weights_name





def     A__      (					__lowerCAmelCase		:      List[Any] ,     *,
    __lowerCAmelCase		:      Dict ,     __lowerCAmelCase		:      str ,     __lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      int ,     __lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      str ,     __lowerCAmelCase		:      Optional[int] ,     __lowerCAmelCase		:      int ,     __lowerCAmelCase		:      str ,     __lowerCAmelCase		:      List[str]=None ,     ):
						lowerCamelCase__								=	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
																		lowerCamelCase__								=	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_    )    ):
																		lowerCamelCase__								=	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:
																								lowerCamelCase__								=	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
																		lowerCamelCase__								=	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'''


import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[Any]								=       "data2vec-audio"




 def __init__(						self      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)      ,  _lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)      ,  _lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=19      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=0.05      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase="sum"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=256      ,  _lowerCAmelCase=(512, 512, 512, 512, 1500)      ,  _lowerCAmelCase=(5, 3, 3, 1, 1)      ,  _lowerCAmelCase=(1, 2, 3, 1, 1)      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase      ,  pad_token_id=_lowerCAmelCase      ,  bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase		)
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   feat_extract_activation
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   conv_bias
     _lowerCAmelCase						      =   num_conv_pos_embeddings
     _lowerCAmelCase						      =   num_conv_pos_embedding_groups
     _lowerCAmelCase						      =   conv_pos_kernel_size
     _lowerCAmelCase						      =   len(self.conv_dim		)
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   hidden_dropout
     _lowerCAmelCase						      =   attention_dropout
     _lowerCAmelCase						      =   activation_dropout
     _lowerCAmelCase						      =   feat_proj_dropout
     _lowerCAmelCase						      =   final_dropout
     _lowerCAmelCase						      =   layerdrop
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   use_weighted_layer_sum

     if (
         (len(self.conv_stride		) != self.num_feat_extract_layers)
         or (len(self.conv_kernel		) != self.num_feat_extract_layers)
         or (len(self.conv_dim		) != self.num_feat_extract_layers)
     ):
         raise ValueError(
             "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
             " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
             f''' {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,'''
             f''' `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     _lowerCAmelCase						      =   mask_time_prob
     _lowerCAmelCase						      =   mask_time_length
     _lowerCAmelCase						      =   mask_time_min_masks
     _lowerCAmelCase						      =   mask_feature_prob
     _lowerCAmelCase						      =   mask_feature_length
     _lowerCAmelCase						      =   mask_feature_min_masks

     # ctc loss
     _lowerCAmelCase						      =   ctc_loss_reduction
     _lowerCAmelCase						      =   ctc_zero_infinity

     # adapter
     _lowerCAmelCase						      =   add_adapter
     _lowerCAmelCase						      =   adapter_kernel_size
     _lowerCAmelCase						      =   adapter_stride
     _lowerCAmelCase						      =   num_adapter_layers
     _lowerCAmelCase						      =   output_hidden_size or hidden_size

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     _lowerCAmelCase						      =   classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   xvector_output_dim





 @property
 def 				_snake_case (						self		)     ->				str:
     return math.prod(self.conv_stride		)


