



'''simple docstring'''

import functools
import operator

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class 	a__     (				_UpperCAmelCase	):
	__magic_name__		:    List[Any]	    =     '''wavlm'''


	def __init__(self : Any,					__UpperCAmelCase : str=32,					__UpperCAmelCase : Union[str, Any]=768,					__UpperCAmelCase : str=12,					__UpperCAmelCase : Dict=12,					__UpperCAmelCase : List[str]=3072,					__UpperCAmelCase : Dict="gelu",					__UpperCAmelCase : int=0.1,					__UpperCAmelCase : List[str]=0.1,					__UpperCAmelCase : Union[str, Any]=0.1,					__UpperCAmelCase : Dict=0.0,					__UpperCAmelCase : Optional[Any]=0.1,					__UpperCAmelCase : Tuple=0.1,					__UpperCAmelCase : List[str]=0.02,					__UpperCAmelCase : List[Any]=1e-5,					__UpperCAmelCase : Tuple="group",					__UpperCAmelCase : Optional[int]="gelu",					__UpperCAmelCase : List[Any]=(512, 512, 512, 512, 512, 512, 512),					__UpperCAmelCase : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2),					__UpperCAmelCase : str=(10, 3, 3, 3, 3, 2, 2),					__UpperCAmelCase : Optional[int]=False,					__UpperCAmelCase : int=128,					__UpperCAmelCase : int=16,					__UpperCAmelCase : List[Any]=320,					__UpperCAmelCase : Union[str, Any]=800,					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : Tuple=True,					__UpperCAmelCase : List[str]=0.05,					__UpperCAmelCase : Any=10,					__UpperCAmelCase : List[Any]=2,					__UpperCAmelCase : str=0.0,					__UpperCAmelCase : Tuple=10,					__UpperCAmelCase : int=320,					__UpperCAmelCase : Union[str, Any]=2,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : List[Any]=100,					__UpperCAmelCase : Union[str, Any]=256,					__UpperCAmelCase : Dict=256,					__UpperCAmelCase : Tuple=0.1,					__UpperCAmelCase : List[Any]="mean",					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : Any=False,					__UpperCAmelCase : Union[str, Any]=256,					__UpperCAmelCase : str=(512, 512, 512, 512, 1500),					__UpperCAmelCase : Any=(5, 3, 3, 1, 1),					__UpperCAmelCase : Dict=(1, 2, 3, 1, 1),					__UpperCAmelCase : Union[str, Any]=512,					__UpperCAmelCase : str=80,					__UpperCAmelCase : int=0,					__UpperCAmelCase : Optional[Any]=1,					__UpperCAmelCase : Dict=2,					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : List[str]=3,					__UpperCAmelCase : Tuple=2,					__UpperCAmelCase : int=3,					__UpperCAmelCase : List[str]=None,					**__UpperCAmelCase : Dict,					)     ->			int:

					"""simple docstring"""

					super().__init__(**A_,					pad_token_id=A_,					bos_token_id=A_,					eos_token_id=A_      )
					SCREAMING_SNAKE_CASE  :						str      					= hidden_size
					SCREAMING_SNAKE_CASE  :						int      					= feat_extract_norm
					SCREAMING_SNAKE_CASE  :						Dict      					= feat_extract_activation
					SCREAMING_SNAKE_CASE  :						List[Any]      					= list(A_      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= list(A_      )
					SCREAMING_SNAKE_CASE  :						int      					= list(A_      )
					SCREAMING_SNAKE_CASE  :						Any      					= conv_bias
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_buckets
					SCREAMING_SNAKE_CASE  :						Dict      					= max_bucket_distance
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_conv_pos_embeddings
					SCREAMING_SNAKE_CASE  :						int      					= num_conv_pos_embedding_groups
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= len(self.conv_dim      )
					SCREAMING_SNAKE_CASE  :						Dict      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						Dict      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= hidden_act
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						str      					= hidden_dropout
					SCREAMING_SNAKE_CASE  :						int      					= attention_dropout
					SCREAMING_SNAKE_CASE  :						List[Any]      					= activation_dropout
					SCREAMING_SNAKE_CASE  :						Tuple      					= feat_proj_dropout
					SCREAMING_SNAKE_CASE  :						Any      					= final_dropout
					SCREAMING_SNAKE_CASE  :						List[Any]      					= layerdrop
					SCREAMING_SNAKE_CASE  :						str      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						List[Any]      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Dict      					= num_ctc_classes
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= do_stable_layer_norm
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= use_weighted_layer_sum
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= classifier_proj_size

					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
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= apply_spec_augment
					SCREAMING_SNAKE_CASE  :						int      					= mask_time_prob
					SCREAMING_SNAKE_CASE  :						List[str]      					= mask_time_length
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= mask_time_min_masks
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= mask_feature_prob
					SCREAMING_SNAKE_CASE  :						List[Any]      					= mask_feature_length

					# parameters for pretraining with codevector quantized representations
					SCREAMING_SNAKE_CASE  :						str      					= num_codevectors_per_group
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= num_codevector_groups
					SCREAMING_SNAKE_CASE  :						int      					= contrastive_logits_temperature
					SCREAMING_SNAKE_CASE  :						str      					= num_negatives
					SCREAMING_SNAKE_CASE  :						Tuple      					= codevector_dim
					SCREAMING_SNAKE_CASE  :						List[str]      					= proj_codevector_dim
					SCREAMING_SNAKE_CASE  :						int      					= diversity_loss_weight

					# ctc loss
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= ctc_loss_reduction
					SCREAMING_SNAKE_CASE  :						Any      					= ctc_zero_infinity

					# adapter
					SCREAMING_SNAKE_CASE  :						List[str]      					= add_adapter
					SCREAMING_SNAKE_CASE  :						Tuple      					= adapter_kernel_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= adapter_stride
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_adapter_layers
					SCREAMING_SNAKE_CASE  :						str      					= output_hidden_size or hidden_size

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						int      					= classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						Tuple      					= list(A_      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= list(A_      )
					SCREAMING_SNAKE_CASE  :						str      					= list(A_      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= xvector_output_dim






	@property
	def 				lowercase__  (self : List[str]      )     ->			int:

					"""simple docstring"""

					return functools.reduce(operator.mul,					self.conv_stride,					1      )









'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


snake_case_			=     [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						List[Any]      					= Github(os.environ['''GITHUB_TOKEN''']       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= g.get_repo('''huggingface/diffusers'''       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= repo.get_issues(state='''open'''       )

				for issue in open_issues:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= sorted(issue.get_comments()    ,	key=lambda _SCREAMING_SNAKE_CASE       : i.created_at    ,	reverse=_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= comments[0] if len(_SCREAMING_SNAKE_CASE       ) > 0 else None
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and (dt.utcnow() - issue.updated_at).days > 7
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
								):
												# Closes the issue after 7 days of inactivity since the Stalebot notification.
												issue.edit(state='''closed'''       )
								elif (
								    "stale" in issue.get_labels()
								    and last_comment is not None
								    and last_comment.user.login != "github-actions[bot]"
								):
												# Opens the issue if someone other than Stalebot commented.
												issue.edit(state='''open'''       )
												issue.remove_from_labels('''stale'''       )
								elif (
								    (dt.utcnow() - issue.updated_at).days > 23
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
								):
												# Post a Stalebot notification after 23 days of inactivity.
												issue.create_comment(
												    '''This issue has been automatically marked as stale because it has not had '''
												    '''recent activity. If you think this still needs to be addressed '''
												    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
												    '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
												    '''are likely to be ignored.'''       )
												issue.add_to_labels('''stale'''       )


if __name__ == "__main__":
	main()




