




from __future__ import annotations

from typing import Generic, TypeVar

_snake_case										=       TypeVar("T")







class 						lowercase							(   Generic[T]		):

     def __init__(  self		,     _a				)						->							None:
           _A							:     List[str]			=						data
           _A							:     int			=						self
           _A							:     Any			=						0







class 						lowercase							(   Generic[T]		):

     def __init__(  self				)						->							None:
           # map from node name to the node object
           _A							:     dict[T, DisjointSetTreeNode[T]]			=						{}

     def 					a__	(  self		,     _a				)						->							None:
           # create a new set with x as its member
           _A							:     Union[str, Any]			=						DisjointSetTreeNode(_a				)

     def 					a__	(  self		,     _a				)						->							DisjointSetTreeNode[T]:
           # find the set x belongs to (with path-compression)
           _A							:     Any			=						self.map[data]
           if elem_ref != elem_ref.parent:
                 _A							:     Optional[int]			=						self.find_set(elem_ref.parent.data				)
           return elem_ref.parent

     def 					a__	(  self		,     _a		,     _a				)						->							None:
           # helper function for union operation
           if nodea.rank > nodea.rank:
                 _A							:     List[str]			=						nodea
           else:
                 _A							:     str			=						nodea
                 if nodea.rank == nodea.rank:
                       nodea.rank += 1



     def 					a__	(  self		,     _a		,     _a				)						->							None:
           # merge 2 disjoint sets
           self.link(self.find_set(_a				)		,     self.find_set(_a				)				)







class 						lowercase							(   Generic[T]		):

     def __init__(  self				)						->							None:
           # connections: map from the node to the neighbouring nodes (with weights)
           _A							:     dict[T, dict[T, int]]			=						{}

     def 					a__	(  self		,     _a				)						->							None:
           # add a node ONLY if its not present in the graph
           if node not in self.connections:
                 _A							:     Dict			=						{}

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							None:
           # add an edge with the given weight
           self.add_node(_a				)
           self.add_node(_a				)
           _A							:     Optional[int]			=						weight
           _A							:     Tuple			=						weight



     def 					a__	(  self				)						->							GraphUndirectedWeighted[T]:
           _A							:     Dict			=						[]
           _A							:     Dict			=						set()
           for start in self.connections:
                 for end in self.connections[start]:
                       if (start, end) not in seen:
                             seen.add((end, start)				)
                             edges.append((start, end, self.connections[start][end])				)
           edges.sort(key=lambda _a				: x[2]				)

           # creating the disjoint set
           _A							:     Any			=						DisjointSetTree[T]()
           for node in self.connections:
                 disjoint_set.make_set(_a				)

           # MST generation
           _A							:     Optional[int]			=						0
           _A							:     List[Any]			=						0
           _A							:     Dict			=						GraphUndirectedWeighted[T]()
           while num_edges < len(self.connections				) - 1:
                 _A			,   _A			,   _A							:     Tuple			=						edges[index]
                 index += 1
                 _A							:     Optional[Any]			=						disjoint_set.find_set(_a				)
                 _A							:     List[str]			=						disjoint_set.find_set(_a				)
                 if parent_u != parent_v:
                       num_edges += 1
                       graph.add_edge(_a		,     _a		,     _a				)
                       disjoint_set.union(_a		,     _a				)
           return graph





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   __a			(    __UpperCamelCase      ):
 __lowercase :							Optional[int]        = 'data2vec-audio'
 def __init__(   self				, lowerCAmelCase__=32				, lowerCAmelCase__=768				, lowerCAmelCase__=12				, lowerCAmelCase__=12				, lowerCAmelCase__=3_072				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0_2				, lowerCAmelCase__=1E-5				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=(512, 512, 512, 512, 512, 512, 512)				, 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__=0.0_5				, lowerCAmelCase__=10				, lowerCAmelCase__=2				, lowerCAmelCase__=0.0				, lowerCAmelCase__=10				, lowerCAmelCase__=0				, lowerCAmelCase__="sum"				, lowerCAmelCase__=False				, lowerCAmelCase__=False				, lowerCAmelCase__=256				, lowerCAmelCase__=(512, 512, 512, 512, 1_500)				, lowerCAmelCase__=(5, 3, 3, 1, 1)				, lowerCAmelCase__=(1, 2, 3, 1, 1)				, lowerCAmelCase__=512				, lowerCAmelCase__=0				, lowerCAmelCase__=1				, lowerCAmelCase__=2				, lowerCAmelCase__=False				, lowerCAmelCase__=3				, lowerCAmelCase__=2				, lowerCAmelCase__=3				, lowerCAmelCase__=None				, **lowerCAmelCase__				, )					->				Optional[int]:




        '''simple docstring'''
        super().__init__(**lowerCAmelCase__				, pad_token_id=lowerCAmelCase__				, bos_token_id=lowerCAmelCase__				, eos_token_id=lowerCAmelCase__ )
        lowercase__:   int  			=   hidden_size
        lowercase__:   str  			=   feat_extract_activation
        lowercase__:   List[Any]  			=   list(lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   list(lowerCAmelCase__ )
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   conv_bias
        lowercase__:   int  			=   num_conv_pos_embeddings
        lowercase__:   List[str]  			=   num_conv_pos_embedding_groups
        lowercase__:   List[Any]  			=   conv_pos_kernel_size
        lowercase__:   Optional[Any]  			=   len(self.conv_dim )
        lowercase__:   List[str]  			=   num_hidden_layers
        lowercase__:   List[str]  			=   intermediate_size
        lowercase__:   Tuple  			=   hidden_act
        lowercase__:   Any  			=   num_attention_heads
        lowercase__:   Optional[int]  			=   hidden_dropout
        lowercase__:   List[str]  			=   attention_dropout
        lowercase__:   int  			=   activation_dropout
        lowercase__:   Dict  			=   feat_proj_dropout
        lowercase__:   str  			=   final_dropout
        lowercase__:   List[str]  			=   layerdrop
        lowercase__:   str  			=   layer_norm_eps
        lowercase__:   Union[str, Any]  			=   initializer_range
        lowercase__:   Union[str, Any]  			=   vocab_size
        lowercase__:   Any  			=   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
        lowercase__:   List[str]  			=   mask_time_prob
        lowercase__:   Tuple  			=   mask_time_length
        lowercase__:   List[Any]  			=   mask_time_min_masks
        lowercase__:   Optional[int]  			=   mask_feature_prob
        lowercase__:   Union[str, Any]  			=   mask_feature_length
        lowercase__:   List[str]  			=   mask_feature_min_masks

        # ctc loss
        lowercase__:   Union[str, Any]  			=   ctc_loss_reduction
        lowercase__:   str  			=   ctc_zero_infinity

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

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        lowercase__:   List[Any]  			=   classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   Dict  			=   list(lowerCAmelCase__ )
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   str  			=   xvector_output_dim
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        return math.prod(self.conv_stride )


