





'''simple docstring'''




lowercase__       : str         =       tuple[float, float, float]
lowercase__       : Dict         =       tuple[float, float, float]






def 			_lowerCAmelCase     (    __snake_case    :					Pointad					,				__snake_case    :					Pointad     )	->	Vectorad:
     __A		:					List[Any]						=			end_pointa[0] - end_pointa[0]
     __A		:					Union[str, Any]						=			end_pointa[1] - end_pointa[1]
     __A		:					Union[str, Any]						=			end_pointa[2] - end_pointa[2]
     return (x, y, z)






def 			_lowerCAmelCase     (    __snake_case    :					Vectorad					,				__snake_case    :					Vectorad     )	->	Vectorad:
     __A		:					Tuple						=			ab[1] * ac[2] - ab[2] * ac[1]  # *i
     __A		:					Union[str, Any]						=			(ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
     __A		:					int						=			ab[0] * ac[1] - ab[1] * ac[0]  # *k
     return (x, y, z)






def 			_lowerCAmelCase     (    __snake_case    :					Vectorad					,				__snake_case    :					int     )	->	bool:
     return tuple(round(__snake_case					,				__snake_case     ) for x in vector     ) == (0, 0, 0)







def 			_lowerCAmelCase     (    __snake_case    :					Pointad					,				__snake_case    :					Pointad					,				__snake_case    :					Pointad					,				__snake_case    :					int = 10     )	->	bool:
     __A		:					int						=			create_vector(__snake_case					,				__snake_case     )
     __A		:					Union[str, Any]						=			create_vector(__snake_case					,				__snake_case     )
     return is_zero_vector(get_ad_vectors_cross(__snake_case					,				__snake_case     )					,				__snake_case     )





"""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       ,				)

