


from __future__ import annotations

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





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							List[str]         = 1
	UpperCAmelCase   :							List[str]         = 2
	UpperCAmelCase   :							Optional[int]         = 3





	def __repr__(  self							)		->			List[str]:
								"""simple docstring"""






								return f"""{self.__class__.__name__}.{self.name}"""





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






							"""simple docstring"""



							a_		,				a_       :   Optional[Any]			=	point
							return degrees(atana(y - miny	,	x - minx      )      )





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






							"""simple docstring"""



							a_		,				a_       :   int			=	starting
							a_		,				a_       :   List[str]			=	via
							a_		,				a_       :   Dict			=	target
							a_       :   List[Any]			=	degrees(atana(ya - ya	,	xa - xa      )      )
							via_angle %= 3_60
							a_       :   int			=	degrees(atana(ya - ya	,	xa - xa      )      )
							target_angle %= 3_60
							# 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		(							SCREAMING_SNAKE_CASE_				: list[tuple[int, int]]      ):






							"""simple docstring"""



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

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

							a_       :   Tuple			=	sorted(SCREAMING_SNAKE_CASE_	,	key=lambda SCREAMING_SNAKE_CASE_      : angle_comparer(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )      )
							# 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)      )

							a_       :   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.
							a_       :   Optional[Any]			=	Direction.left

							for i in range(3	,	len(SCREAMING_SNAKE_CASE_      )      ):
														while True:
																					a_       :   List[str]			=	stack[-2]
																					a_       :   str			=	stack[-1]
																					a_       :   Optional[int]			=	sorted_points[i]
																					a_       :   str			=	check_direction(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )

																					if next_direction == Direction.left:
																												a_       :   int			=	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(SCREAMING_SNAKE_CASE_      )






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE : Optional[Any]          =    {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE : Optional[int]          =    ["CamembertTokenizer"]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE : Any          =    ["CamembertTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE : Union[str, Any]          =    [
	    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "CamembertForCausalLM",
	    "CamembertForMaskedLM",
	    "CamembertForMultipleChoice",
	    "CamembertForQuestionAnswering",
	    "CamembertForSequenceClassification",
	    "CamembertForTokenClassification",
	    "CamembertModel",
	    "CamembertPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE : List[Any]          =    [
	    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFCamembertForCausalLM",
	    "TFCamembertForMaskedLM",
	    "TFCamembertForMultipleChoice",
	    "TFCamembertForQuestionAnswering",
	    "TFCamembertForSequenceClassification",
	    "TFCamembertForTokenClassification",
	    "TFCamembertModel",
	    "TFCamembertPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_camembert import CamembertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_camembert_fast import CamembertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_camembert import (
		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CamembertForCausalLM,
		    CamembertForMaskedLM,
		    CamembertForMultipleChoice,
		    CamembertForQuestionAnswering,
		    CamembertForSequenceClassification,
		    CamembertForTokenClassification,
		    CamembertModel,
		    CamembertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_camembert import (
		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCamembertForCausalLM,
		    TFCamembertForMaskedLM,
		    TFCamembertForMultipleChoice,
		    TFCamembertForQuestionAnswering,
		    TFCamembertForSequenceClassification,
		    TFCamembertForTokenClassification,
		    TFCamembertModel,
		    TFCamembertPreTrainedModel,
		)

else:
	import sys

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


