


"""simple docstring"""



from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize
class     __magic_name__						(	__UpperCAmelCase   ):
       __A	:					Any					     =      1
       __A	:					Optional[Any]					     =      2
       __A	:					Optional[Any]					     =      3






       def __repr__(      self     :			Union[str, Any] ):

              '''simple docstring'''






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







def 					lowerCamelCase    (a_  :Optional[int] , a_  :Dict , a_  :Tuple)   ->							float:
       lowercase						:Any											= point
       return degrees(atana(y - miny , x - minx))






def 					lowerCamelCase    (a_  :Optional[int] , a_  :Union[str, Any] , a_  :str)   ->							Direction:
       lowercase						:Any											= starting
       lowercase						:List[str]											= via
       lowercase						:Tuple											= target
       lowercase						:List[Any]											= degrees(atana(ya - ya , xa - xa))
       via_angle %= 360
       lowercase						:int											= 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    (a_  :Union[str, Any])   ->							list[tuple[int, int]]:


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

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

       lowercase						:List[Any]											= sorted(a_ , key=lambda a_: angle_comparer(a_ , a_ , a_))
       # 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))

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

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

                     if next_direction == Direction.left:
                            lowercase						: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(a_)







import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.electra.modeling_flax_electra import (
							    FlaxElectraForCausalLM,
							    FlaxElectraForMaskedLM,
							    FlaxElectraForMultipleChoice,
							    FlaxElectraForPreTraining,
							    FlaxElectraForQuestionAnswering,
							    FlaxElectraForSequenceClassification,
							    FlaxElectraForTokenClassification,
							    FlaxElectraModel,
							)


class 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase=13 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=99 ,       __lowercase=24 ,       __lowercase=32 ,       __lowercase=5 ,       __lowercase=4 ,       __lowercase=37 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=512 ,       __lowercase=16 ,       __lowercase=2 ,       __lowercase=0.02 ,       __lowercase=4 ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							parent
						lowerCamelCase_							:		Any							   =							batch_size
						lowerCamelCase_							:		Union[str, Any]							   =							seq_length
						lowerCamelCase_							:		Dict							   =							is_training
						lowerCamelCase_							:		Any							   =							use_attention_mask
						lowerCamelCase_							:		Any							   =							use_token_type_ids
						lowerCamelCase_							:		Any							   =							use_labels
						lowerCamelCase_							:		Any							   =							vocab_size
						lowerCamelCase_							:		Any							   =							hidden_size
						lowerCamelCase_							:		Optional[Any]							   =							embedding_size
						lowerCamelCase_							:		Dict							   =							num_hidden_layers
						lowerCamelCase_							:		Optional[Any]							   =							num_attention_heads
						lowerCamelCase_							:		Optional[Any]							   =							intermediate_size
						lowerCamelCase_							:		List[str]							   =							hidden_act
						lowerCamelCase_							:		Optional[Any]							   =							hidden_dropout_prob
						lowerCamelCase_							:		int							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[int]							   =							max_position_embeddings
						lowerCamelCase_							:		List[str]							   =							type_vocab_size
						lowerCamelCase_							:		int							   =							type_sequence_label_size
						lowerCamelCase_							:		Tuple							   =							initializer_range
						lowerCamelCase_							:		int							   =							num_choices

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size					)

						lowerCamelCase_							:		List[Any]							   =							None
						if self.use_attention_mask:
							lowerCamelCase_							:		Tuple							   =							random_attention_mask([self.batch_size, self.seq_length]					)

						lowerCamelCase_							:		int							   =							None
						if self.use_token_type_ids:
							lowerCamelCase_							:		List[Any]							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size					)

						lowerCamelCase_							:		List[str]							   =							ElectraConfig(
						    vocab_size=self.vocab_size ,       hidden_size=self.hidden_size ,       embedding_size=self.embedding_size ,       num_hidden_layers=self.num_hidden_layers ,       num_attention_heads=self.num_attention_heads ,       intermediate_size=self.intermediate_size ,       hidden_act=self.hidden_act ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       max_position_embeddings=self.max_position_embeddings ,       type_vocab_size=self.type_vocab_size ,       initializer_range=self.initializer_range ,       )

						return config, input_ids, token_type_ids, attention_mask





					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							config_and_inputs
						lowerCamelCase_							:		Union[str, Any]							   =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict


@require_flax
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = True

					_lowerCamelCase		  = (
					    (
					        FlaxElectraModel,
					        FlaxElectraForCausalLM,
					        FlaxElectraForMaskedLM,
					        FlaxElectraForPreTraining,
					        FlaxElectraForTokenClassification,
					        FlaxElectraForQuestionAnswering,
					        FlaxElectraForMultipleChoice,
					        FlaxElectraForSequenceClassification,
					    )
					    if is_flax_available()
					    else ()
					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							FlaxElectraModelTester(self					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							if model_class_name == FlaxElectraForMaskedLM:
								lowerCamelCase_							:		Union[str, Any]							   =							model_class_name.from_pretrained('google/electra-small-generator'					)
							else:
								lowerCamelCase_							:		List[Any]							   =							model_class_name.from_pretrained('google/electra-small-discriminator'					)
							lowerCamelCase_							:		Dict							   =							model(np.ones((1, 1)					)					)
							self.assertIsNotNone(__lowercase					)
