




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


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    """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 			a__    (					snake_case__      ):
						_a							:    Optional[int]					=    """speech_to_text"""
						_a							:    str					=    ["""past_key_values"""]
						_a							:    Union[str, Any]					=    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






						def __init__(   self		,			_A=1_0_0_0_0		,			_A=1_2		,			_A=2_0_4_8		,			_A=4		,			_A=6		,			_A=2_0_4_8		,			_A=4		,			_A=0.0		,			_A=0.0		,			_A=True		,			_A=True		,			_A="relu"		,			_A=2_5_6		,			_A=0.1		,			_A=0.0		,			_A=0.0		,			_A=0.02		,			_A=2		,			_A=True		,			_A=1		,			_A=0		,			_A=2		,			_A=6_0_0_0		,			_A=1_0_2_4		,			_A=2		,			_A=(5, 5)		,			_A=1_0_2_4		,			_A=8_0		,			_A=1		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       vocab_size
											__lowerCAmelCase				   =       d_model
											__lowerCAmelCase				   =       encoder_ffn_dim
											__lowerCAmelCase				   =       encoder_layers
											__lowerCAmelCase				   =       encoder_attention_heads
											__lowerCAmelCase				   =       decoder_ffn_dim
											__lowerCAmelCase				   =       decoder_layers
											__lowerCAmelCase				   =       decoder_attention_heads
											__lowerCAmelCase				   =       dropout
											__lowerCAmelCase				   =       attention_dropout
											__lowerCAmelCase				   =       activation_dropout
											__lowerCAmelCase				   =       activation_function
											__lowerCAmelCase				   =       init_std
											__lowerCAmelCase				   =       encoder_layerdrop
											__lowerCAmelCase				   =       decoder_layerdrop
											__lowerCAmelCase				   =       use_cache
											__lowerCAmelCase				   =       encoder_layers
											__lowerCAmelCase				   =       scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCAmelCase				   =       max_source_positions
											__lowerCAmelCase				   =       max_target_positions
											__lowerCAmelCase				   =       num_conv_layers
											__lowerCAmelCase				   =       list(_A			)
											__lowerCAmelCase				   =       conv_channels
											__lowerCAmelCase				   =       input_feat_per_channel
											__lowerCAmelCase				   =       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=_A		,			bos_token_id=_A		,			eos_token_id=_A		,			is_encoder_decoder=_A		,			decoder_start_token_id=_A		,			**_A		,			)






from __future__ import annotations

from typing import Any




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  lowercase	:    int			,  lowercase	:    int			,  lowercase	:    float = 0	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			row, column
				_snake_case		=			[[default_value for c in range(lowercase	)] for r in range(lowercase	)]



	def __str__(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

				# Make string identifier
				_snake_case		=			0
				for row_vector in self.array:
							for obj in row_vector:
										_snake_case		=			max(lowercase			,  len(str(lowercase	)	)	)
				_snake_case		=			f'''%{max_element_length}s'''

				# Make string and return
				def single_line(lowercase	:    list[float]	) -> str:
							nonlocal string_format_identifier
							_snake_case		=			'['
							line += ", ".join(string_format_identifier % (obj,) for obj in row_vector	)
							line += "]"
							return line

				s += "\n".join(single_line(lowercase	) for row_vector in self.array	)
				return s



	def __repr__(  self	:    Dict	):


				'''simple docstring'''


				return str(self	)



	def  A							(  self	:    str			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				if not (isinstance(lowercase			,  (list, tuple)	) and len(lowercase	) == 2):
							return False
				elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
							return False
				else:
							return True



	def __getitem__(  self	:    Dict			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				return self.array[loc[0]][loc[1]]



	def __setitem__(  self	:    str			,  lowercase	:    tuple[int, int]			,  lowercase	:    float	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				_snake_case		=			value



	def __add__(  self	:    str			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	)
				assert self.row == another.row and self.column == another.column

				# Add
				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c] + another[r, c]
				return result



	def __neg__(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			-self[r, c]
				return result



	def __sub__(  self	:    List[str]			,  lowercase	:    Matrix	):


				'''simple docstring'''


				return self + (-another)



	def __mul__(  self	:    Dict			,  lowercase	:    int | float | Matrix	):


				'''simple docstring'''


				if isinstance(lowercase			,  (int, float)	):  # Scalar multiplication
							_snake_case		=			Matrix(self.row			,  self.column	)
							for r in range(self.row	):
										for c in range(self.column	):
													_snake_case		=			self[r, c] * another
							return result
				elif isinstance(lowercase			,  lowercase	):  # Matrix multiplication
							assert self.column == another.row
							_snake_case		=			Matrix(self.row			,  another.column	)
							for r in range(self.row	):
										for c in range(another.column	):
													for i in range(self.column	):
																result[r, c] += self[r, i] * another[i, c]
							return result
				else:
							_snake_case		=			f'''Unsupported type given for another ({type(lowercase	)})'''
							raise TypeError(lowercase	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.column			,  self.row	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c]
				return result





	def  A							(  self	:    List[Any]			,  lowercase	:    Matrix			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	) and isinstance(lowercase			,  lowercase	)
				assert self.row == self.column == u.row == v.row  # u, v should be column vector
				assert u.column == v.column == 1  # u, v should be column vector

				# Calculate
				_snake_case		=			v.transpose()
				_snake_case		=			(v_t * self * u)[0, 0] + 1
				if numerator_factor == 0:
							return None  # It's not invertable
				return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






			def        a_		(       ) ->  None:
						# a^(-1)
						_snake_case		=			Matrix(3       ,					3       ,					0 )
						for i in range(3 ):
									_snake_case		=			1
						print(f'''a^(-1) is {ainv}''' )
						# u, v
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			1, 2, -3
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			4, -2, 5
						print(f'''u is {u}''' )
						print(f'''v is {v}''' )
						print(f'''uv^T is {u * v.transpose()}''' )
						# Sherman Morrison
						print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowercase       ,					__lowercase )}''' )






			def        a_		(       ) ->  None:
						import doctest

						doctest.testmod()

			testa()