



'''simple docstring'''






from typing import TYPE_CHECKING

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


A_							: Optional[Any]						 =	{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

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

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: Dict						 =	["""LlamaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: Optional[int]						 =	[
       """LlamaForCausalLM""",
       """LlamaModel""",
       """LlamaPreTrainedModel""",
       """LlamaForSequenceClassification""",
   ]


if TYPE_CHECKING:
   from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama import LlamaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama_fast import LlamaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
   import sys

   A_							: Any						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = False		)->		list[float]:





       '''simple docstring'''


       if radian_mode:
              return [magnitude * cos(lowerCAmelCase_		), magnitude * sin(lowerCAmelCase_		)]
       return [magnitude * cos(radians(lowerCAmelCase_		)		), magnitude * sin(radians(lowerCAmelCase_		)		)]




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = 10**-1		)->		bool:





       '''simple docstring'''


       _UpperCAmelCase  :						NDArray[floataa]			     = cross(lowerCAmelCase_       ,   lowerCAmelCase_		)
       _UpperCAmelCase  :						float			     = sum(lowerCAmelCase_		)
       return abs(lowerCAmelCase_		) < eps


if __name__ == "__main__":
   # Test to check if it works
   A_							: str						 =	array(
       [
           polar_force(718.4, 1_8_0 - 3_0),
           polar_force(879.54, 4_5),
           polar_force(1_0_0, -9_0),
       ]
   )

   A_							: NDArray[floataa]						 =	array([[0, 0], [0, 0], [0, 0]])

   assert in_static_equilibrium(forces, location)

   # Problem 1 in image_data/2D_problems.jpg
   A_							: List[str]						 =	array(
       [
           polar_force(3_0 * 9.81, 1_5),
           polar_force(2_1_5, 1_8_0 - 4_5),
           polar_force(2_6_4, 9_0 - 3_0),
       ]
   )

   A_							: Tuple						 =	array([[0, 0], [0, 0], [0, 0]])

   assert in_static_equilibrium(forces, location)

   # Problem in image_data/2D_problems_1.jpg
   A_							: Dict						 =	array([[0, -2_0_0_0], [0, -1_2_0_0], [0, 1_5_6_0_0], [0, -1_2_4_0_0]])

   A_							: Union[str, Any]						 =	array([[0, 0], [6, 0], [1_0, 0], [1_2, 0]])

   assert in_static_equilibrium(forces, location)

   import doctest

   doctest.testmod()



