





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
snake_case								=							2_9_9_7_9_2_4_5_8

# Symbols
snake_case								=							symbols("""ct x y z""")

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:float  )   ->    float:







  if velocity > c:
    raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!'  )
  elif velocity < 1:
    # Usually the speed should be much higher than 1 (c order of magnitude)
    raise ValueError('Speed must be greater than or equal to 1!'  )

  return velocity / c

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:float  )   ->    float:
  return 1 / sqrt(1 - beta(UpperCamelCase__  ) ** 2  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:float  )   ->    np.ndarray:
  return np.array(
      [
          [gamma(UpperCamelCase__  ), -gamma(UpperCamelCase__  ) * beta(UpperCamelCase__  ), 0, 0],
          [-gamma(UpperCamelCase__  ) * beta(UpperCamelCase__  ), gamma(UpperCamelCase__  ), 0, 0],
          [0, 0, 1, 0],
          [0, 0, 0, 1],
      ]  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:float      ,				snake_case__	:np.ndarray | None = None  )   ->    np.ndarray:







  if event is None:
    _lowercase						    =			np.array([ct, x, y, z]  )  # Symbolic four vector
  else:
    event[0] *= c  # x0 is ct (speed of light * time)

  return transformation_matrix(UpperCamelCase__  ) @ event


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Example of symbolic vector:
      snake_case								=							transform(2_9_9_7_9_2_4_5)
      print("""Example of four vector: """)
      print(F"""ct\' = {four_vector[0]}""")
      print(F"""x\' = {four_vector[1]}""")
      print(F"""y\' = {four_vector[2]}""")
      print(F"""z\' = {four_vector[3]}""")

      # Substitute symbols with numerical values
      snake_case								=							{ct: c, x: 1, y: 1, z: 1}
      snake_case								=							[four_vector[i].subs(sub_dict) for i in range(4)]

      print(F"""\n{numerical_vector}""")



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


_lowercase							:   List[Any]						=						logging.get_logger(__name__)

_lowercase							:   List[str]						=						{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'mctct'

				def __init__(    self					, a__=8065					, a__=1536					, a__=36					, a__=6144					, a__=4					, a__=384					, a__=920					, a__=1e-5					, a__=0.3					, a__="relu"					, a__=0.02					, a__=0.3					, a__=0.3					, a__=1					, a__=0					, a__=2					, a__=1					, a__=0.3					, a__=1					, a__=(7,)					, a__=(3,)					, a__=80					, a__=1					, a__=None					, a__="sum"					, a__=False					, **a__					, )						->					Optional[Any]:
					super().__init__(**a__					, pad_token_id=a__					, bos_token_id=a__					, eos_token_id=a__ )
					A             =					vocab_size
					A             =					hidden_size
					A             =					num_hidden_layers
					A             =					intermediate_size
					A             =					num_attention_heads
					A             =					attention_head_dim
					A             =					max_position_embeddings
					A             =					layer_norm_eps
					A             =					layerdrop
					A             =					hidden_act
					A             =					initializer_range
					A             =					hidden_dropout_prob
					A             =					attention_probs_dropout_prob
					A             =					pad_token_id
					A             =					bos_token_id
					A             =					eos_token_id
					A             =					conv_glu_dim
					A             =					conv_dropout
					A             =					num_conv_layers
					A             =					input_feat_per_channel
					A             =					input_channels
					A             =					conv_channels
					A             =					ctc_loss_reduction
					A             =					ctc_zero_infinity

					# prevents config testing fail with exporting to json
					A             =					list(a__ )
					A             =					list(a__ )

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



