
import functools
import operator

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


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {
    "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   (				__magic_name__    ):
				_snake_case		    = '''wavlm'''




				def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[Any]=32,    SCREAMING_SNAKE_CASE_	:				List[Any]=7_68,    SCREAMING_SNAKE_CASE_	:				Tuple=12,    SCREAMING_SNAKE_CASE_	:				List[str]=12,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=30_72,    SCREAMING_SNAKE_CASE_	:				List[str]="gelu",    SCREAMING_SNAKE_CASE_	:				Optional[int]=0.1,    SCREAMING_SNAKE_CASE_	:				List[str]=0.1,    SCREAMING_SNAKE_CASE_	:				List[str]=0.1,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=0.0,    SCREAMING_SNAKE_CASE_	:				List[Any]=0.1,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=0.1,    SCREAMING_SNAKE_CASE_	:				Dict=0.02,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=1e-5,    SCREAMING_SNAKE_CASE_	:				Dict="group",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="gelu",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12),    SCREAMING_SNAKE_CASE_	:				int=(5, 2, 2, 2, 2, 2, 2),    SCREAMING_SNAKE_CASE_	:				Dict=(10, 3, 3, 3, 3, 2, 2),    SCREAMING_SNAKE_CASE_	:				Any=False,    SCREAMING_SNAKE_CASE_	:				List[Any]=1_28,    SCREAMING_SNAKE_CASE_	:				str=16,    SCREAMING_SNAKE_CASE_	:				int=3_20,    SCREAMING_SNAKE_CASE_	:				List[str]=8_00,    SCREAMING_SNAKE_CASE_	:				List[str]=False,    SCREAMING_SNAKE_CASE_	:				Optional[int]=True,    SCREAMING_SNAKE_CASE_	:				Tuple=0.05,    SCREAMING_SNAKE_CASE_	:				Any=10,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=2,    SCREAMING_SNAKE_CASE_	:				List[Any]=0.0,    SCREAMING_SNAKE_CASE_	:				Dict=10,    SCREAMING_SNAKE_CASE_	:				List[Any]=3_20,    SCREAMING_SNAKE_CASE_	:				Any=2,    SCREAMING_SNAKE_CASE_	:				Optional[int]=0.1,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=1_00,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=2_56,    SCREAMING_SNAKE_CASE_	:				int=2_56,    SCREAMING_SNAKE_CASE_	:				str=0.1,    SCREAMING_SNAKE_CASE_	:				List[str]="mean",    SCREAMING_SNAKE_CASE_	:				List[Any]=False,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=False,    SCREAMING_SNAKE_CASE_	:				List[str]=2_56,    SCREAMING_SNAKE_CASE_	:				Tuple=(5_12, 5_12, 5_12, 5_12, 15_00),    SCREAMING_SNAKE_CASE_	:				str=(5, 3, 3, 1, 1),    SCREAMING_SNAKE_CASE_	:				List[str]=(1, 2, 3, 1, 1),    SCREAMING_SNAKE_CASE_	:				Optional[int]=5_12,    SCREAMING_SNAKE_CASE_	:				List[str]=80,    SCREAMING_SNAKE_CASE_	:				Any=0,    SCREAMING_SNAKE_CASE_	:				Optional[int]=1,    SCREAMING_SNAKE_CASE_	:				Dict=2,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=False,    SCREAMING_SNAKE_CASE_	:				Dict=3,    SCREAMING_SNAKE_CASE_	:				List[Any]=2,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=3,    SCREAMING_SNAKE_CASE_	:				List[Any]=None,    **SCREAMING_SNAKE_CASE_	:				Optional[Any],    ):
										super().__init__(**SCREAMING_SNAKE_CASE_,    pad_token_id=SCREAMING_SNAKE_CASE_,    bos_token_id=SCREAMING_SNAKE_CASE_,    eos_token_id=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[Any] =    hidden_size
										snake_case       :     Dict =    feat_extract_norm
										snake_case       :     Union[str, Any] =    feat_extract_activation
										snake_case       :     int =    list(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    list(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Union[str, Any] =    list(SCREAMING_SNAKE_CASE_      )
										snake_case       :     int =    conv_bias
										snake_case       :     Dict =    num_buckets
										snake_case       :     List[str] =    max_bucket_distance
										snake_case       :     List[Any] =    num_conv_pos_embeddings
										snake_case       :     Optional[int] =    num_conv_pos_embedding_groups
										snake_case       :     Union[str, Any] =    len(self.conv_dim      )
										snake_case       :     Any =    num_hidden_layers
										snake_case       :     List[str] =    intermediate_size
										snake_case       :     Optional[Any] =    hidden_act
										snake_case       :     int =    num_attention_heads
										snake_case       :     List[Any] =    hidden_dropout
										snake_case       :     Tuple =    attention_dropout
										snake_case       :     List[Any] =    activation_dropout
										snake_case       :     Dict =    feat_proj_dropout
										snake_case       :     str =    final_dropout
										snake_case       :     List[str] =    layerdrop
										snake_case       :     Tuple =    layer_norm_eps
										snake_case       :     Optional[Any] =    initializer_range
										snake_case       :     Tuple =    num_ctc_classes
										snake_case       :     int =    vocab_size
										snake_case       :     Optional[int] =    do_stable_layer_norm
										snake_case       :     Union[str, Any] =    use_weighted_layer_sum
										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
										snake_case       :     int =    apply_spec_augment
										snake_case       :     List[str] =    mask_time_prob
										snake_case       :     Optional[int] =    mask_time_length
										snake_case       :     Optional[int] =    mask_time_min_masks
										snake_case       :     List[Any] =    mask_feature_prob
										snake_case       :     Any =    mask_feature_length

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

										# ctc loss
										snake_case       :     Dict =    ctc_loss_reduction
										snake_case       :     Optional[Any] =    ctc_zero_infinity

										# adapter
										snake_case       :     int =    add_adapter
										snake_case       :     Dict =    adapter_kernel_size
										snake_case       :     Optional[int] =    adapter_stride
										snake_case       :     Optional[Any] =    num_adapter_layers
										snake_case       :     Any =    output_hidden_size or hidden_size

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

										# XVector-specific parameters. Feel free to ignore for other classes.
										snake_case       :     int =    list(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[Any] =    list(SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    list(SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[Any] =    xvector_output_dim




				@property
				def 	__snake_case							(							self	:				List[Any]      ):
										return functools.reduce(operator.mul,    self.conv_stride,    1      )



'''simple docstring'''





import pytest

from datasets import inspect_metric, list_metrics, load_metric

@pytest.fixture
def      A (       A_							:					str       ):
						monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings'''    ,							set()       )

@pytest.fixture
def      A (       A_							:					Optional[Any]       ):

						class 						a   :




										def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     List[str] =    metric_id




						class 						a   :
										_snake_case		    = [MetricMock(__magic_name__    ) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]




										def 	__snake_case							(							self	:				str      ):
																return self._metrics




						monkeypatch.setattr('''datasets.inspect.huggingface_hub'''    ,							HfhMock()       )





@pytest.mark.parametrize(
    '''func, args'''    ,							[(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))]       )
def      A (       A_							:					str    ,							A_							:					Dict    ,							A_							:					int    ,							A_							:					int    ,							A_							:					str       ):
						if "tmp_path" in args:
												snake_case       :     List[str] =    tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args       )
						with pytest.warns(A_    ,							match='''https://huggingface.co/docs/evaluate'''       ):
												func(*A_       )

