'''simple docstring'''


from typing import TYPE_CHECKING

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


snake_case__					:      Optional[int]				      =  {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

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

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__					:      Union[str, Any]				      =  ['''ReformerTokenizerFast''']

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

 snake_case__					:      Optional[Any]				      =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''


snake_case__					:      Optional[Any]				      =  tuple[float, float, float]
snake_case__					:      Tuple				      =  tuple[float, float, float]


def  _lowerCamelCase   (				lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad       ):



     """simple docstring"""





     UpperCAmelCase_							:       Any       =				end_pointa[0] - end_pointa[0]
     UpperCAmelCase_							:       Optional[Any]       =				end_pointa[1] - end_pointa[1]
     UpperCAmelCase_							:       Any       =				end_pointa[2] - end_pointa[2]
     return (x, y, z)


def  _lowerCamelCase   (				lowerCamelCase_ :							Vectorad     ,					lowerCamelCase_ :							Vectorad       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[Any]       =				ab[1] * ac[2] - ab[2] * ac[1]  # *i
     UpperCAmelCase_							:       Optional[Any]       =				(ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
     UpperCAmelCase_							:       Dict       =				ab[0] * ac[1] - ab[1] * ac[0]  # *k
     return (x, y, z)


def  _lowerCamelCase   (				lowerCamelCase_ :							Vectorad     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     return tuple(round(lowerCamelCase_     ,					lowerCamelCase_       ) for x in vector       ) == (0, 0, 0)





def  _lowerCamelCase   (				lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							int = 10       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				create_vector(lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       List[Any]       =				create_vector(lowerCamelCase_     ,					lowerCamelCase_       )
     return is_zero_vector(get_ad_vectors_cross(lowerCamelCase_     ,					lowerCamelCase_       )     ,					lowerCamelCase_       )






