






"""simple docstring"""





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCamelCase_	:				int										= 299792458

# Symbols
UpperCamelCase_	:				str										= symbols("""ct x y z""")



def     __lowercase					(    a :				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     __lowercase					(    a :				float )					->					float:
      return 1 / sqrt(1 - beta(a ) ** 2 )



def     __lowercase					(    a :				float )					->					np.ndarray:
      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     __lowercase					(    a :				float       ,		a :				np.ndarray | None = None )					->					np.ndarray:
      # Ensure event is not empty
      if event is None:
            __snake_case      :    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:
    UpperCamelCase_	:				List[str]										= transform(29979245)
    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
    UpperCamelCase_	:				Any										= {ct: c, x: 1, y: 1, z: 1}
    UpperCamelCase_	:				List[str]										= [four_vector[i].subs(sub_dict) for i in range(4)]

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










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_	:				Any										= {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= [
        """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ReformerAttention""",
        """ReformerForMaskedLM""",
        """ReformerForQuestionAnswering""",
        """ReformerForSequenceClassification""",
        """ReformerLayer""",
        """ReformerModel""",
        """ReformerModelWithLMHead""",
        """ReformerPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer import ReformerTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer_fast import ReformerTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_reformer import (
            REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            ReformerAttention,
            ReformerForMaskedLM,
            ReformerForQuestionAnswering,
            ReformerForSequenceClassification,
            ReformerLayer,
            ReformerModel,
            ReformerModelWithLMHead,
            ReformerPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_	:				Tuple										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


