
import math

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


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{
    '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   (   __a				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"""data2vec-audio"""

							def __init__(self  ,  UpperCAmelCase=32  ,  UpperCAmelCase=768  ,  UpperCAmelCase=12  ,  UpperCAmelCase=12  ,  UpperCAmelCase=3072  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-5  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)  ,  UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)  ,  UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)  ,  UpperCAmelCase=False  ,  UpperCAmelCase=16  ,  UpperCAmelCase=19  ,  UpperCAmelCase=5  ,  UpperCAmelCase=0.05  ,  UpperCAmelCase=10  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=10  ,  UpperCAmelCase=0  ,  UpperCAmelCase="sum"  ,  UpperCAmelCase=False  ,  UpperCAmelCase=False  ,  UpperCAmelCase=256  ,  UpperCAmelCase=(512, 512, 512, 512, 1500)  ,  UpperCAmelCase=(5, 3, 3, 1, 1)  ,  UpperCAmelCase=(1, 2, 3, 1, 1)  ,  UpperCAmelCase=512  ,  UpperCAmelCase=0  ,  UpperCAmelCase=1  ,  UpperCAmelCase=2  ,  UpperCAmelCase=False  ,  UpperCAmelCase=3  ,  UpperCAmelCase=2  ,  UpperCAmelCase=3  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       List[str]:
														super().__init__(**a__  ,  pad_token_id=a__  ,  bos_token_id=a__  ,  eos_token_id=a__  )
														_snake_case       =  hidden_size
														_snake_case       =  feat_extract_activation
														_snake_case       =  list(a__  )
														_snake_case       =  list(a__  )
														_snake_case       =  list(a__  )
														_snake_case       =  conv_bias
														_snake_case       =  num_conv_pos_embeddings
														_snake_case       =  num_conv_pos_embedding_groups
														_snake_case       =  conv_pos_kernel_size
														_snake_case       =  len(self.conv_dim  )
														_snake_case       =  num_hidden_layers
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_act
														_snake_case       =  num_attention_heads
														_snake_case       =  hidden_dropout
														_snake_case       =  attention_dropout
														_snake_case       =  activation_dropout
														_snake_case       =  feat_proj_dropout
														_snake_case       =  final_dropout
														_snake_case       =  layerdrop
														_snake_case       =  layer_norm_eps
														_snake_case       =  initializer_range
														_snake_case       =  vocab_size
														_snake_case       =  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
														_snake_case       =  mask_time_prob
														_snake_case       =  mask_time_length
														_snake_case       =  mask_time_min_masks
														_snake_case       =  mask_feature_prob
														_snake_case       =  mask_feature_length
														_snake_case       =  mask_feature_min_masks

														# ctc loss
														_snake_case       =  ctc_loss_reduction
														_snake_case       =  ctc_zero_infinity

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

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

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





							@property
							def 	lowercase							(self  )					->       Tuple:
														return math.prod(self.conv_stride  )

'''simple docstring'''
from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url






@pytest.mark.parametrize("""repo_id"""							, ["""canonical_dataset_name""", """org-name/dataset-name"""]				)
@pytest.mark.parametrize("""path"""							, ["""filename.csv""", """filename with blanks.csv"""]				)
@pytest.mark.parametrize("""revision"""							, [None, """v2"""]				)
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  hf_hub_url(repo_id=_SCREAMING_SNAKE_CASE							, path=_SCREAMING_SNAKE_CASE							, revision=_SCREAMING_SNAKE_CASE				)
							assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(_SCREAMING_SNAKE_CASE				)}"""