



"""simple docstring"""


from __future__ import annotations

from collections.abc import Iterable







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Union[str, Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Optional[int]   )							->     Dict:
												UpperCAmelCase ,       UpperCAmelCase			 =					float(lowercase_   ), float(lowercase_   )






						def __eq__(   self					:List[str]  ,    lowercase_					:Optional[int]   )							->     str:
												return self.x == other.x and self.y == other.y






						def __ne__(   self					:Optional[int]  ,    lowercase_					:Tuple   )							->     Any:
												return not self == other






						def __gt__(   self					:Optional[int]  ,    lowercase_					:Optional[int]   )							->     Tuple:
												if self.x > other.x:
																		return True
												elif self.x == other.x:
																		return self.y > other.y
												return False






						def __lt__(   self					:List[Any]  ,    lowercase_					:Dict   )							->     int:
												return not self > other






						def __ge__(   self					:List[Any]  ,    lowercase_					:Union[str, Any]   )							->     List[Any]:
												if self.x > other.x:
																		return True
												elif self.x == other.x:
																		return self.y >= other.y
												return False






						def __le__(   self					:str  ,    lowercase_					:Union[str, Any]   )							->     List[Any]:
												if self.x < other.x:
																		return True
												elif self.x == other.x:
																		return self.y <= other.y
												return False






						def __repr__(   self					:int   )							->     int:
												return f"""({self.x}, {self.y})"""






						def __hash__(   self					:Union[str, Any]   )							->     List[str]:
												return hash(self.x   )


def 					_lowerCAmelCase (  lowercase_						,      ):
						UpperCAmelCase			 =					[]
						if list_of_tuples:
												for p in list_of_tuples:
																		if isinstance(lowercase_						,      lowercase_		):
																								points.append(lowercase_		)
																		else:
																								try:
																														points.append(Point(p[0]						,      p[1]		)		)
																								except (IndexError, TypeError):
																														print(
																														    F"""Ignoring deformed point {p}. All points"""
																														    ' must have at least 2 coordinates.'		)
						return points

def 					_lowerCAmelCase (  lowercase_		):

						if not hasattr(lowercase_						,      '__iter__'		):
												UpperCAmelCase			 =					F"""Expecting an iterable object but got an non-iterable type {points}"""
												raise ValueError(lowercase_		)

						if not points:
												UpperCAmelCase			 =					F"""Expecting a list of points but got {points}"""
												raise ValueError(lowercase_		)

						return _construct_points(lowercase_		)

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
						return det

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					sorted(_validate_input(lowercase_		)		)
						UpperCAmelCase			 =					len(lowercase_		)
						UpperCAmelCase			 =					set()

						for i in range(n - 1		):
												for j in range(i + 1						,      lowercase_		):
																		UpperCAmelCase			 =					UpperCAmelCase			 =					False
																		UpperCAmelCase			 =					True
																		for k in range(lowercase_		):
																								if k != i and k != j:
																														UpperCAmelCase			 =					_det(points[i]						,      points[j]						,      points[k]		)

																														if det_k > 0:
																																				UpperCAmelCase			 =					True
																														elif det_k < 0:
																																				UpperCAmelCase			 =					True
																														else:
																																				# point[i], point[j], point[k] all lie on a straight line
																																				# if point[k] is to the left of point[i] or it's to the
																																				# right of point[j], then point[i], point[j] cannot be
																																				# part of the convex hull of A
																																				if points[k] < points[i] or points[k] > points[j]:
																																										UpperCAmelCase			 =					False
																																										break

																								if points_left_of_ij and points_right_of_ij:
																														UpperCAmelCase			 =					False
																														break

																		if ij_part_of_convex_hull:
																								convex_set.update([points[i], points[j]]		)

						return sorted(lowercase_		)

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					sorted(_validate_input(lowercase_		)		)
						UpperCAmelCase			 =					len(lowercase_		)

						# divide all the points into an upper hull and a lower hull
						# the left most point and the right most point are definitely
						# members of the convex hull by definition.
						# use these two anchors to divide all the points into two hulls,
						# an upper hull and a lower hull.

						# all points to the left (above) the line joining the extreme points belong to the
						# upper hull
						# all points to the right (below) the line joining the extreme points below to the
						# lower hull
						# ignore all points on the line joining the extreme points since they cannot be
						# part of the convex hull

						UpperCAmelCase			 =					points[0]
						UpperCAmelCase			 =					points[n - 1]

						UpperCAmelCase			 =					{left_most_point, right_most_point}
						UpperCAmelCase			 =					[]
						UpperCAmelCase			 =					[]

						for i in range(1						,      n - 1		):
												UpperCAmelCase			 =					_det(lowercase_						,      lowercase_						,      points[i]		)

												if det > 0:
																		upper_hull.append(points[i]		)
												elif det < 0:
																		lower_hull.append(points[i]		)

						_construct_hull(lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		)
						_construct_hull(lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		)

						return sorted(lowercase_		)

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):
						if points:
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					float('-inf'		)
												UpperCAmelCase			 =					[]

												for p in points:
																		UpperCAmelCase			 =					_det(lowercase_						,      lowercase_						,      lowercase_		)

																		if det > 0:
																								candidate_points.append(lowercase_		)

																								if det > extreme_point_distance:
																														UpperCAmelCase			 =					det
																														UpperCAmelCase			 =					p

												if extreme_point:
																		_construct_hull(lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		)
																		convex_set.add(lowercase_		)
																		_construct_hull(lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		)

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					sorted(_validate_input(lowercase_		)		)
						UpperCAmelCase			 =					len(lowercase_		)

						UpperCAmelCase			 =					points[:2]
						for i in range(2						,      lowercase_		):
												UpperCAmelCase			 =					_det(convex_hull[1]						,      convex_hull[0]						,      points[i]		)
												if det > 0:
																		convex_hull.insert(0						,      points[i]		)
																		break
												elif det < 0:
																		convex_hull.append(points[i]		)
																		break
												else:
																		UpperCAmelCase			 =					points[i]
						i += 1

						for j in range(lowercase_						,      lowercase_		):
												if (
												    _det(convex_hull[0]						,      convex_hull[-1]						,      points[j]		) > 0
												    and _det(convex_hull[-1]						,      convex_hull[0]						,      points[1]		) < 0
												):
																		# The point lies within the convex hull
																		continue

												convex_hull.insert(0						,      points[j]		)
												convex_hull.append(points[j]		)
												while _det(convex_hull[0]						,      convex_hull[1]						,      convex_hull[2]		) >= 0:
																		del convex_hull[1]
												while _det(convex_hull[-1]						,      convex_hull[-2]						,      convex_hull[-3]		) <= 0:
																		del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
						return sorted(convex_hull[1:] if len(lowercase_		) > 3 else convex_hull		)

def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					[
						    (0, 3),
						    (2, 2),
						    (1, 1),
						    (2, 1),
						    (3, 0),
						    (0, 0),
						    (3, 3),
						    (2, -1),
						    (2, -4),
						    (1, -3),
						]
						# the convex set of points is
						# [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
						UpperCAmelCase			 =					convex_hull_bf(lowercase_		)

						UpperCAmelCase			 =					convex_hull_recursive(lowercase_		)
						assert results_bf == results_recursive

						UpperCAmelCase			 =					convex_hull_melkman(lowercase_		)
						assert results_bf == results_melkman

						print(lowercase_		)


if __name__ == "__main__":
				main()












"""simple docstring"""




import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       CpmAntConfig,
       CpmAntForCausalLM,
       CpmAntModel,
       CpmAntTokenizer,
   )

@require_torch
class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=2   , snake_case_=8   , snake_case_=True   , snake_case_=False   , snake_case_=False   , snake_case_=False   , snake_case_=False   , snake_case_=9_9   , snake_case_=3_2   , snake_case_=3   , snake_case_=4   , snake_case_=3_7   , snake_case_=3_2   , snake_case_=1_2_8   , snake_case_=8   , snake_case_=8   , snake_case_=8   , snake_case_=1.0   , snake_case_=True   , ):

        """simple docstring"""

        A_    :			List[Any]									=    parent
        A_    :			Union[str, Any]									=    batch_size
        A_    :			Dict									=    seq_length
        A_    :			Tuple									=    is_training
        A_    :			Union[str, Any]									=    use_token_type_ids
        A_    :			Optional[Any]									=    use_input_mask
        A_    :			str									=    use_labels
        A_    :			Optional[int]									=    use_mc_token_ids
        A_    :			str									=    vocab_size
        A_    :			Any									=    hidden_size
        A_    :			List[Any]									=    num_hidden_layers
        A_    :			str									=    num_attention_heads
        A_    :			Optional[Any]									=    intermediate_size
        A_    :			List[str]									=    num_buckets
        A_    :			Tuple									=    max_distance
        A_    :			Optional[int]									=    prompt_length
        A_    :			List[str]									=    prompt_types
        A_    :			Dict									=    segment_types
        A_    :			List[str]									=    init_std
        A_    :			Union[str, Any]									=    return_dict

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    {}
        A_    :			Any									=    ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size    ).type(torch.intaa    )
        A_    :			Union[str, Any]									=    False

        A_    :			Optional[int]									=    self.get_config()

        return (config, input_ids)

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return CpmAntConfig(
            vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , num_hidden_layers=self.num_hidden_layers   , num_attention_heads=self.num_attention_heads   , dim_ff=self.intermediate_size   , position_bias_num_buckets=self.num_buckets   , position_bias_max_distance=self.max_distance   , prompt_types=self.prompt_types   , prompt_length=self.prompt_length   , segment_types=self.segment_types   , use_cache=snake_case_   , init_std=self.init_std   , return_dict=self.return_dict   , )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , *snake_case_    ):

        """simple docstring"""

        A_    :			Tuple									=    CpmAntModel(config=snake_case_    )
        model.to(snake_case_    )
        model.eval()

        A_    :			Union[str, Any]									=    model(**snake_case_    ).last_hidden_state

        self.parent.assertEqual(hidden_states.shape   , (self.batch_size, self.seq_length, config.hidden_size)    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , *snake_case_    ):

        """simple docstring"""

        A_    :			Dict									=    CpmAntForCausalLM(snake_case_    )
        model.to(snake_case_    )
        A_    :			str									=    input_ids['input_ids'].to(snake_case_    )
        model.eval()

        A_    :			int									=    model(**snake_case_    )
        self.parent.assertEqual(
            model_output.logits.shape   , (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			Optional[Any]									=    self.prepare_config_and_inputs()
        return config, inputs_dict


@require_torch
class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					Any             =  (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
      lowercase_ :					int             =  (
          {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
      )

      lowercase_ :					Any             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Dict             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Union[str, Any]             =  False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    CpmAntModelTester(self    )
        A_    :			Optional[int]									=    ConfigTester(self   , config_class=snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.config_tester.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        unittest.skip('CPMAnt doesn\'t support input_embeds.'    )(self.test_inputs_embeds    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        unittest.skip(
            'CPMAnt doesn\'t support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model.'    )(self.test_retain_grad_hidden_states_attentions    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			Optional[Any]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_cpmant_model(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			List[Any]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(snake_case_   , snake_case_    )


@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    '今天天气真好！'
        A_    :			Tuple									=    'openbmb/cpm-ant-10b'
        A_    :			Tuple									=    CpmAntModel.from_pretrained(snake_case_    )
        A_    :			Dict									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			str									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Union[str, Any]									=    model(**snake_case_    ).last_hidden_state

        A_    :			Optional[int]									=    torch.tensor(
            [[[6.17_08, 5.92_44, 1.08_35], [6.52_07, 6.28_93, -11.33_24], [-1.01_07, -0.05_76, -5.95_77]]]   , )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]   , snake_case_   , atol=1E-2    )    )


@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    '今天天气真好！'
        A_    :			Union[str, Any]									=    'openbmb/cpm-ant-10b'
        A_    :			List[str]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Union[str, Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			int									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Tuple									=    model(**snake_case_    ).logits

        A_    :			Union[str, Any]									=    torch.tensor(
            [[[-6.42_67, -6.40_83, -6.39_58], [-5.88_02, -5.94_47, -5.78_11], [-5.38_96, -5.48_20, -5.42_95]]]   , )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]   , snake_case_   , atol=1E-2    )    )

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    'openbmb/cpm-ant-10b'
        A_    :			Optional[int]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Optional[Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			List[str]									=    '今天天气不错，'
        A_    :			Dict									=    '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的'
        A_    :			str									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Dict									=    model.generate(**snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer.batch_decode(snake_case_    )
        self.assertEqual(snake_case_   , snake_case_    )

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    'openbmb/cpm-ant-10b'
        A_    :			List[Any]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Optional[Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			int									=    ['今天天气不错，', '新年快乐，万事如意！']
        A_    :			List[Any]									=    [
            '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的',
            '新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的',
        ]
        A_    :			Tuple									=    tokenizer(snake_case_   , return_tensors='pt'   , padding=snake_case_    )
        A_    :			int									=    model.generate(**snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer.batch_decode(snake_case_    )
        self.assertEqual(snake_case_   , snake_case_    )