



"""simple docstring"""




import functools
import operator

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


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    "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  snake_case     (							_UpperCAmelCase   ):
							UpperCAmelCase__						     =      '''wavlm'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=32       ,       SCREAMING_SNAKE_CASE_=7_68       ,       SCREAMING_SNAKE_CASE_=12       ,       SCREAMING_SNAKE_CASE_=12       ,       SCREAMING_SNAKE_CASE_=30_72       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=1e-5       ,       SCREAMING_SNAKE_CASE_="group"       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)       ,       SCREAMING_SNAKE_CASE_=(5, 2, 2, 2, 2, 2, 2)       ,       SCREAMING_SNAKE_CASE_=(10, 3, 3, 3, 3, 2, 2)       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=1_28       ,       SCREAMING_SNAKE_CASE_=16       ,       SCREAMING_SNAKE_CASE_=3_20       ,       SCREAMING_SNAKE_CASE_=8_00       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=0.05       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=3_20       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1_00       ,       SCREAMING_SNAKE_CASE_=2_56       ,       SCREAMING_SNAKE_CASE_=2_56       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_="mean"       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=2_56       ,       SCREAMING_SNAKE_CASE_=(5_12, 5_12, 5_12, 5_12, 15_00)       ,       SCREAMING_SNAKE_CASE_=(5, 3, 3, 1, 1)       ,       SCREAMING_SNAKE_CASE_=(1, 2, 3, 1, 1)       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_=80       ,       SCREAMING_SNAKE_CASE_=0       ,       SCREAMING_SNAKE_CASE_=1       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=None       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**A_       ,       pad_token_id=A_       ,       bos_token_id=A_       ,       eos_token_id=A_    )
									SCREAMING_SNAKE_CASE_						 = hidden_size
									SCREAMING_SNAKE_CASE_						 = feat_extract_norm
									SCREAMING_SNAKE_CASE_						 = feat_extract_activation
									SCREAMING_SNAKE_CASE_						 = list(A_    )
									SCREAMING_SNAKE_CASE_						 = list(A_    )
									SCREAMING_SNAKE_CASE_						 = list(A_    )
									SCREAMING_SNAKE_CASE_						 = conv_bias
									SCREAMING_SNAKE_CASE_						 = num_buckets
									SCREAMING_SNAKE_CASE_						 = max_bucket_distance
									SCREAMING_SNAKE_CASE_						 = num_conv_pos_embeddings
									SCREAMING_SNAKE_CASE_						 = num_conv_pos_embedding_groups
									SCREAMING_SNAKE_CASE_						 = len(self.conv_dim    )
									SCREAMING_SNAKE_CASE_						 = num_hidden_layers
									SCREAMING_SNAKE_CASE_						 = intermediate_size
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_dropout
									SCREAMING_SNAKE_CASE_						 = attention_dropout
									SCREAMING_SNAKE_CASE_						 = activation_dropout
									SCREAMING_SNAKE_CASE_						 = feat_proj_dropout
									SCREAMING_SNAKE_CASE_						 = final_dropout
									SCREAMING_SNAKE_CASE_						 = layerdrop
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = num_ctc_classes
									SCREAMING_SNAKE_CASE_						 = vocab_size
									SCREAMING_SNAKE_CASE_						 = do_stable_layer_norm
									SCREAMING_SNAKE_CASE_						 = use_weighted_layer_sum
									SCREAMING_SNAKE_CASE_						 = 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_						 = apply_spec_augment
									SCREAMING_SNAKE_CASE_						 = mask_time_prob
									SCREAMING_SNAKE_CASE_						 = mask_time_length
									SCREAMING_SNAKE_CASE_						 = mask_time_min_masks
									SCREAMING_SNAKE_CASE_						 = mask_feature_prob
									SCREAMING_SNAKE_CASE_						 = mask_feature_length

									# parameters for pretraining with codevector quantized representations
									SCREAMING_SNAKE_CASE_						 = num_codevectors_per_group
									SCREAMING_SNAKE_CASE_						 = num_codevector_groups
									SCREAMING_SNAKE_CASE_						 = contrastive_logits_temperature
									SCREAMING_SNAKE_CASE_						 = num_negatives
									SCREAMING_SNAKE_CASE_						 = codevector_dim
									SCREAMING_SNAKE_CASE_						 = proj_codevector_dim
									SCREAMING_SNAKE_CASE_						 = diversity_loss_weight

									# ctc loss
									SCREAMING_SNAKE_CASE_						 = ctc_loss_reduction
									SCREAMING_SNAKE_CASE_						 = ctc_zero_infinity

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

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

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







							@property
							def _lowercase					(self    ):

									"""simple docstring"""







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

"""simple docstring"""




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


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index