

from typing import TYPE_CHECKING

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


a_      =     {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['ReformerTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['ReformerTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    '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

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_lowerCamelCase      :						str							=  299792458

# Symbols
_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase      :						int							=  symbols("""ct x y z""")







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			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 SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			float:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			np.ndarray:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_ = None		)  ->			np.ndarray:



       """simple docstring"""

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

       return transformation_matrix(lowercase_		) @ event


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Example of symbolic vector:
  _lowerCamelCase      :						Tuple							=  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
  _lowerCamelCase      :						int							=  {ct: c, x: 1, y: 1, z: 1}
  _lowerCamelCase      :						Any							=  [four_vector[i].subs(sub_dict) for i in range(4)]

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


