'''simple docstring'''


from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Tuple		       =						1
  lowerCamelCase_						:List[Any]		       =						2
  lowerCamelCase_						:Any		       =						3


  def __repr__(					self		):




       '''simple docstring'''

       return F'''{self.__class__.__name__}.{self.name}'''







def    _lowerCamelCase       ( lowerCamelCase_   :				tuple[int, int]      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     UpperCAmelCase_  ,     UpperCAmelCase_				:  Tuple  = point
     return degrees(atana(y - miny      ,	x - minx  )  )


def    _lowerCamelCase       ( lowerCamelCase_   :				tuple[int, int]      ,	lowerCamelCase_   :				tuple[int, int]      ,	lowerCamelCase_   :				tuple[int, int]  ):



     """simple docstring"""





     UpperCAmelCase_  ,     UpperCAmelCase_				:  Union[str, Any]  = starting
     UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[Any]  = via
     UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = target
     UpperCAmelCase_				:  Any  = degrees(atana(ya - ya      ,	xa - xa  )  )
     via_angle %= 360
     UpperCAmelCase_				:  Union[str, Any]  = degrees(atana(ya - ya      ,	xa - xa  )  )
     target_angle %= 360
     # t-
     #  \ \
     #   \ v
     #    \|
     #     s
     # via_angle is always lower than target_angle, if direction is left.
     # If they are same, it means they are on a same line of convex hull.
     if target_angle > via_angle:
          return Direction.left
     elif target_angle == via_angle:
          return Direction.straight
     else:
          return Direction.right





def    _lowerCamelCase       ( lowerCamelCase_   :				list[tuple[int, int]]  ):



     """simple docstring"""





     if len(lowerCamelCase_  ) <= 2:
          # There is no convex hull
          raise ValueError('graham_scan: argument must contain more than 3 points.'  )
     if len(lowerCamelCase_  ) == 3:
          return points
     # find the lowest and the most left point
     UpperCAmelCase_				:  Union[str, Any]  = 0
     UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = maxsize, maxsize
     for i, point in enumerate(lowerCamelCase_  ):
          UpperCAmelCase_				:  Optional[Any]  = point[0]
          UpperCAmelCase_				:  Dict  = point[1]
          if y < miny:
               UpperCAmelCase_				:  List[Any]  = y
               UpperCAmelCase_				:  Union[str, Any]  = x
               UpperCAmelCase_				:  Union[str, Any]  = i
          if y == miny and x < minx:
               UpperCAmelCase_				:  Dict  = x
               UpperCAmelCase_				:  Optional[Any]  = i

    # remove the lowest and the most left point from points for preparing for sort
     points.pop(lowerCamelCase_  )

     UpperCAmelCase_				:  List[str]  = sorted(lowerCamelCase_      ,	key=lambda lowerCamelCase_  : angle_comparer(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )  )
     # This insert actually costs complexity,
     # and you should instead add (minx, miny) into stack later.
     # I'm using insert just for easy understanding.
     sorted_points.insert(0      ,	(minx, miny)  )

     UpperCAmelCase_				:  deque[tuple[int, int]]  = deque()
     stack.append(sorted_points[0]  )
     stack.append(sorted_points[1]  )
     stack.append(sorted_points[2]  )
     # In any ways, the first 3 points line are towards left.
     # Because we sort them the angle from minx, miny.
     UpperCAmelCase_				:  List[Any]  = Direction.left

     for i in range(3      ,	len(lowerCamelCase_  )  ):
          while True:
               UpperCAmelCase_				:  Union[str, Any]  = stack[-2]
               UpperCAmelCase_				:  Any  = stack[-1]
               UpperCAmelCase_				:  Optional[Any]  = sorted_points[i]
               UpperCAmelCase_				:  List[str]  = check_direction(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )

               if next_direction == Direction.left:
                    UpperCAmelCase_				:  List[str]  = Direction.left
                    break
               if next_direction == Direction.straight:
                    if current_direction == Direction.left:
                         # We keep current_direction as left.
                         # Because if the straight line keeps as straight,
                         # we want to know if this straight line is towards left.
                         break
                    elif current_direction == Direction.right:
                         # If the straight line is towards right,
                         # every previous points on those straigh line is not convex hull.
                         stack.pop()
               if next_direction == Direction.right:
                    stack.pop()
          stack.append(sorted_points[i]  )
     return list(lowerCamelCase_  )








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase	=    {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GPTJForCausalLM''',
							    '''GPTJForQuestionAnswering''',
							    '''GPTJForSequenceClassification''',
							    '''GPTJModel''',
							    '''GPTJPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''TFGPTJForCausalLM''',
							    '''TFGPTJForQuestionAnswering''',
							    '''TFGPTJForSequenceClassification''',
							    '''TFGPTJModel''',
							    '''TFGPTJPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''FlaxGPTJForCausalLM''',
							    '''FlaxGPTJModel''',
							    '''FlaxGPTJPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

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






