"""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_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						"""data2vec-audio"""

				def __init__(				self		,    lowerCAmelCase=32		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    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=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)		,    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=2_56		,    lowerCAmelCase=(5_12, 5_12, 5_12, 5_12, 15_00)		,    lowerCAmelCase=(5, 3, 3, 1, 1)		,    lowerCAmelCase=(1, 2, 3, 1, 1)		,    lowerCAmelCase=5_12		,    lowerCAmelCase=0		,    lowerCAmelCase=1		,    lowerCAmelCase=2		,    lowerCAmelCase=False		,    lowerCAmelCase=3		,    lowerCAmelCase=2		,    lowerCAmelCase=3		,    lowerCAmelCase=None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase		,    pad_token_id=lowerCAmelCase		,    bos_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase    )
										snake_case								=			hidden_size
										snake_case								=			feat_extract_activation
										snake_case								=			list(lowerCAmelCase    )
										snake_case								=			list(lowerCAmelCase    )
										snake_case								=			list(lowerCAmelCase    )
										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(lowerCAmelCase    )
										snake_case								=			list(lowerCAmelCase    )
										snake_case								=			list(lowerCAmelCase    )
										snake_case								=			xvector_output_dim

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return math.prod(self.conv_stride    )



"""simple docstring"""
from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, torch.Tensor]:





						"""simple docstring"""






						snake_case								=			[]
						snake_case								=			[]
						snake_case								=			[]

						for rt in rc.restypes:
												snake_case								=			rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
												restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
												snake_case								=			{name: i for i, name in enumerate(_UpperCamelCase					)}
												restype_atomaa_to_atomaa_list.append(
												    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

												restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

						# Add dummy mapping for restype 'UNK'
						restype_atomaa_to_atomaa_list.append([0] * 1_4					)
						restype_atomaa_to_atomaa_list.append([0] * 3_7					)
						restype_atomaa_mask_list.append([0.0] * 1_4					)

						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.floataa ,			device=protein['aatype'].device ,			)
						snake_case								=			protein['aatype'].to(torch.long					)

						# create the mapping for (residx, atom14) --> atom37, i.e. an array
						# with shape (num_res, 14) containing the atom37 indices for this protein
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			restype_atomaa_mask[protein_aatype]

						snake_case								=			residx_atomaa_mask
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the gather indices for mapping back
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the corresponding mask
						snake_case								=			torch.zeros([2_1, 3_7] ,			dtype=torch.floataa ,			device=protein['aatype'].device					)
						for restype, restype_letter in enumerate(rc.restypes					):
												snake_case								=			rc.restype_atoa[restype_letter]
												snake_case								=			rc.residue_atoms[restype_name]
												for atom_name in atom_names:
																		snake_case								=			rc.atom_order[atom_name]
																		snake_case								=			1

						snake_case								=			restype_atomaa_mask[protein_aatype]
						snake_case								=			residx_atomaa_mask

						return protein





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, np.ndarray]:





						"""simple docstring"""






						snake_case								=			tree_map(lambda _UpperCamelCase					: torch.tensor(_UpperCamelCase ,			device=batch['aatype'].device					) ,			_UpperCamelCase ,			np.ndarray					)
						snake_case								=			tensor_tree_map(lambda _UpperCamelCase					: np.array(_UpperCamelCase					) ,			make_atomaa_masks(_UpperCamelCase					)					)
						return out


