


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


SCREAMING_SNAKE_CASE       :		Optional[Any]         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				{
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}






class      UpperCamelCase					(		snake_case_     ):


   '''simple docstring'''







   lowercase					:	Optional[Any]									="""transfo-xl"""
   lowercase					:	List[str]									=["""mems"""]
   lowercase					:	Optional[Any]									={
       """n_token""": """vocab_size""",
       """hidden_size""": """d_model""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }
   def __init__(     self   ,							UpperCamelCase_=26_7735   ,							UpperCamelCase_=[2_0000, 4_0000, 20_0000]   ,							UpperCamelCase_=1024   ,							UpperCamelCase_=1024   ,							UpperCamelCase_=16   ,							UpperCamelCase_=64   ,							UpperCamelCase_=4096   ,							UpperCamelCase_=4   ,							UpperCamelCase_=False   ,							UpperCamelCase_=18   ,							UpperCamelCase_=1600   ,							UpperCamelCase_=1000   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=0   ,							UpperCamelCase_=-1   ,							UpperCamelCase_=True   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=0.0   ,							UpperCamelCase_=True   ,							UpperCamelCase_="normal"   ,							UpperCamelCase_=0.01   ,							UpperCamelCase_=0.01   ,							UpperCamelCase_=0.02   ,							UpperCamelCase_=1E-5   ,							UpperCamelCase_=0   ,							**UpperCamelCase_   ,							):
        lowercase_       :Union[str, Any]     =       vocab_size
        lowercase_       :Dict     =       []
        self.cutoffs.extend(UpperCamelCase_		)
        if proj_share_all_but_first:
             lowercase_       :List[str]     =       [False] + [True] * len(self.cutoffs		)
        else:
             lowercase_       :Optional[Any]     =       [False] + [False] * len(self.cutoffs		)
        lowercase_       :Any     =       d_model
        lowercase_       :Tuple     =       d_embed
        lowercase_       :List[Any]     =       d_head
        lowercase_       :Optional[Any]     =       d_inner
        lowercase_       :Optional[int]     =       div_val
        lowercase_       :str     =       pre_lnorm
        lowercase_       :Optional[Any]     =       n_layer
        lowercase_       :Tuple     =       n_head
        lowercase_       :Optional[Any]     =       mem_len
        lowercase_       :str     =       same_length
        lowercase_       :int     =       attn_type
        lowercase_       :Optional[Any]     =       clamp_len
        lowercase_       :List[str]     =       sample_softmax
        lowercase_       :str     =       adaptive
        lowercase_       :List[Any]     =       dropout
        lowercase_       :Optional[Any]     =       dropatt
        lowercase_       :List[str]     =       untie_r
        lowercase_       :int     =       init
        lowercase_       :Optional[Any]     =       init_range
        lowercase_       :Dict     =       proj_init_std
        lowercase_       :Tuple     =       init_std
        lowercase_       :Union[str, Any]     =       layer_norm_epsilon
        super().__init__(eos_token_id=UpperCamelCase_   ,							**UpperCamelCase_		)
   @property
   def 		UpperCamelCase  (     self		):
        # Message copied from Transformer-XL documentation
        logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."		)
        return -1




   @max_position_embeddings.setter
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        # Message copied from Transformer-XL documentation
        raise NotImplementedError(
            f"The model {self.model_type} is one of the few models that has no sequence length limit."		)





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE       :		int         =				299_792_458

# Symbols
SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		Optional[Any]         =				symbols("ct x y z")




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     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     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return 1 / sqrt(1 - beta(_a		) ** 2		)




def     UpperCamelCase    (      _a		)					->							np.ndarray:
     '''simple docstring'''





     return np.array(
         [
             [gamma(_a		), -gamma(_a		) * beta(_a		), 0, 0],
             [-gamma(_a		) * beta(_a		), gamma(_a		), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]		)




def     UpperCamelCase    (      _a       ,		_a = None		)					->							np.ndarray:
     '''simple docstring'''





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

     return transformation_matrix(_a		) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   SCREAMING_SNAKE_CASE       :		List[str]         =				transform(29_979_245)
   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
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				{ct: c, x: 1, y: 1, z: 1}
   SCREAMING_SNAKE_CASE       :		str         =				[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f"\n{numerical_vector}")

