






"""simple docstring"""





# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCamelCase							=  TypeVar('T')



class       __A		(   Generic[T]			):

					def __init__(			self  :      str     ,		__snake_case  :      bool = True					)			->							None:
											__magic_name__:	dict[T, list[T]]							=       {}  # dictionary of lists
											__magic_name__:	Any							=       directed

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      T     ,		__snake_case  :      T					)			->							GraphAdjacencyList[T]:

											if not self.directed:  # For undirected graphs
																	# if both source vertex and destination vertex are both present in the
																	# adjacency list, add destination vertex to source vertex list of adjacent
																	# vertices and add source vertex to destination vertex list of adjacent
																	# vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																							self.adj_list[source_vertex].append(__snake_case					)
																							self.adj_list[destination_vertex].append(__snake_case					)
																	# if only source vertex is present in adjacency list, add destination vertex
																	# to source vertex list of adjacent vertices, then create a new vertex with
																	# destination vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif source_vertex in self.adj_list:
																							self.adj_list[source_vertex].append(__snake_case					)
																							__magic_name__:	Optional[int]							=       [source_vertex]
																	# if only destination vertex is present in adjacency list, add source vertex
																	# to destination vertex list of adjacent vertices, then create a new vertex
																	# with source vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif destination_vertex in self.adj_list:
																							self.adj_list[destination_vertex].append(__snake_case					)
																							__magic_name__:	Dict							=       [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and assign a list
																	# containing the destination vertex as it's first adjacent vertex also
																	# create a new vertex with destination vertex as key and assign a list
																	# containing the source vertex as it's first adjacent vertex.
																	else:
																							__magic_name__:	List[Any]							=       [destination_vertex]
																							__magic_name__:	int							=       [source_vertex]
											else:  # For directed graphs
																	# if both source vertex and destination vertex are present in adjacency
																	# list, add destination vertex to source vertex list of adjacent vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																							self.adj_list[source_vertex].append(__snake_case					)
																	# if only source vertex is present in adjacency list, add destination
																	# vertex to source vertex list of adjacent vertices and create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	elif source_vertex in self.adj_list:
																							self.adj_list[source_vertex].append(__snake_case					)
																							__magic_name__:	Union[str, Any]							=       []
																	# if only destination vertex is present in adjacency list, create a new
																	# vertex with source vertex as key and assign a list containing destination
																	# vertex as first adjacent vertex
																	elif destination_vertex in self.adj_list:
																							__magic_name__:	Optional[Any]							=       [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and a list containing
																	# destination vertex as it's first adjacent vertex. Then create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	else:
																							__magic_name__:	List[Any]							=       [destination_vertex]
																							__magic_name__:	List[Any]							=       []

											return self






					def __repr__(			self  :      Tuple					)			->							str:
											return pformat(self.adj_list					)









"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''speech_to_text'''
 UpperCAmelCase       :	List[Any]     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=10_000       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			int=2_048       ,				_UpperCAmelCase			:			Optional[Any]=4       ,				_UpperCAmelCase			:			List[str]=6       ,				_UpperCAmelCase			:			Tuple=2_048       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]="relu"       ,				_UpperCAmelCase			:			List[Any]=256       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			List[str]=6_000       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			Optional[Any]=2       ,				_UpperCAmelCase			:			Any=(5, 5)       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			str=80       ,				_UpperCAmelCase			:			Any=1       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  vocab_size
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  max_source_positions
       _A     =  max_target_positions
       _A     =  num_conv_layers
       _A     =  list(_UpperCAmelCase						)
       _A     =  conv_channels
       _A     =  input_feat_per_channel
       _A     =  input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, '''
                 F'''`config.num_conv_layers = {self.num_conv_layers}`.'''						)

       super().__init__(
           pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				is_encoder_decoder=_UpperCAmelCase       ,				decoder_start_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

