

from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
A_		:			str										=   20

# Time between time steps in seconds
A_		:			Dict										=   INTERVAL / 1000
class 					A_   :


    '''simple docstring'''






    def __init__(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__ = 1.0     ,      lowercase__ = 1.0     ,      lowercase__ = "blue"     ,      )     ->      Union[str, Any]:
        __UpperCAmelCase									=			position_x
        __UpperCAmelCase									=			position_y
        __UpperCAmelCase									=			velocity_x
        __UpperCAmelCase									=			velocity_y
        __UpperCAmelCase									=			mass
        __UpperCAmelCase									=			size
        __UpperCAmelCase									=			color


    @property
    def      lowerCAmelCase_						(self  )     ->      Dict:
        return self.position_x, self.position_y


    @property
    def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
        return self.velocity_x, self.velocity_y


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
        self.velocity_x += force_x * delta_time
        self.velocity_y += force_y * delta_time






    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      Tuple:
        self.position_x += self.velocity_x * delta_time
        self.position_y += self.velocity_y * delta_time






class 					A_   :


    '''simple docstring'''






    def __init__(self     ,      lowercase__     ,      lowercase__ = 1.0     ,      lowercase__ = 1.0     ,      lowercase__ = 0.0     ,      )     ->      Dict:
        __UpperCAmelCase									=			bodies
        __UpperCAmelCase									=			gravitation_constant
        __UpperCAmelCase									=			time_factor
        __UpperCAmelCase									=			softening_factor


    def __len__(self  )     ->      Tuple:
        return len(self.bodies  )






    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      Any:







        for bodya in self.bodies:
            __UpperCAmelCase									=			0.0
            __UpperCAmelCase									=			0.0
            for bodya in self.bodies:
                if bodya != bodya:
                    __UpperCAmelCase									=			bodya.position_x - bodya.position_x
                    __UpperCAmelCase									=			bodya.position_y - bodya.position_y

                    # Calculation of the distance using Pythagoras's theorem
                    # Extra factor due to the softening technique
                    __UpperCAmelCase									=			(dif_x**2 + dif_y**2 + self.softening_factor) ** (
                        1 / 2
                    )

                    # Newton's law of universal gravitation.
                    force_x += (
                        self.gravitation_constant * bodya.mass * dif_x / distance**3
                    )
                    force_y += (
                        self.gravitation_constant * bodya.mass * dif_y / distance**3
                    )

            # Update the body's velocity once all the force components have been added
            bodya.update_velocity(lowercase__     ,      lowercase__     ,      delta_time * self.time_factor  )

        # Update the positions only after all the velocities have been updated
        for body in self.bodies:
            body.update_position(delta_time * self.time_factor  )






def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     str:






    '''simple docstring'''
    body_system.update_system(lowercase__      )

    # Update the positions of the patches
    for patch, body in zip(lowercase__       ,   body_system.bodies      ):
        __UpperCAmelCase									=			(body.position_x, body.position_y)


def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE = -1       ,   SCREAMING_SNAKE_CASE = 1       ,   SCREAMING_SNAKE_CASE = -1       ,   SCREAMING_SNAKE_CASE = 1       ,   )      ->     Tuple:






    '''simple docstring'''
    __UpperCAmelCase									=			plt.figure()
    fig.canvas.set_window_title(lowercase__      )
    __UpperCAmelCase									=			plt.axes(
        xlim=(x_start, x_end)       ,   ylim=(y_start, y_end)      )  # Set section to be plotted
    plt.gca().set_aspect('''equal'''      )  # Fix aspect ratio

    # Each body is drawn as a patch by the plt-function
    __UpperCAmelCase									=			[
        plt.Circle((body.position_x, body.position_y)       ,   body.size       ,   fc=body.color      )
        for body in body_system.bodies
    ]

    for patch in patches:
        ax.add_patch(lowercase__      )

    # Function called at each step of the animation
    def update(SCREAMING_SNAKE_CASE      ) -> list[plt.Circle]:
        update_step(lowercase__       ,   lowercase__       ,   lowercase__      )
        return patches

    __UpperCAmelCase									=			animation.FuncAnimation(  # noqa: F841
        lowercase__       ,   lowercase__       ,   interval=lowercase__       ,   blit=lowercase__      )

    plt.show()


def       __a       (   )      ->     Any:






    '''simple docstring'''
    __UpperCAmelCase									=			0.9700436
    __UpperCAmelCase									=			-0.24308753
    __UpperCAmelCase									=			0.466203685
    __UpperCAmelCase									=			0.43236573

    __UpperCAmelCase									=			[
        Body(lowercase__       ,   lowercase__       ,   lowercase__       ,   lowercase__       ,   size=0.2       ,   color='''red'''      ),
        Body(-position_x       ,   -position_y       ,   lowercase__       ,   lowercase__       ,   size=0.2       ,   color='''green'''      ),
        Body(0       ,   0       ,   -2 * velocity_x       ,   -2 * velocity_y       ,   size=0.2       ,   color='''blue'''      ),
    ]
    return BodySystem(lowercase__       ,   time_factor=3      )


def       __a       (   )      ->     Optional[int]:






    '''simple docstring'''
    __UpperCAmelCase									=			7.3476e22
    __UpperCAmelCase									=			5.972e24
    __UpperCAmelCase									=			1_0_2_2
    __UpperCAmelCase									=			3_8_4_3_9_9_0_0_0
    __UpperCAmelCase									=			6.674e-11

    # Calculation of the respective velocities so that total impulse is zero,
    # i.e. the two bodies together don't move
    __UpperCAmelCase									=			earth_mass * velocity_dif / (earth_mass + moon_mass)
    __UpperCAmelCase									=			moon_velocity - velocity_dif

    __UpperCAmelCase									=			Body(-earth_moon_distance       ,   0       ,   0       ,   lowercase__       ,   lowercase__       ,   1_0_0_0_0_0_0_0       ,   '''grey'''      )
    __UpperCAmelCase									=			Body(0       ,   0       ,   0       ,   lowercase__       ,   lowercase__       ,   5_0_0_0_0_0_0_0       ,   '''blue'''      )
    return BodySystem([earth, moon]       ,   lowercase__       ,   time_factor=1_0_0_0_0_0_0      )


def       __a       (   )      ->     Optional[int]:






    '''simple docstring'''
    __UpperCAmelCase									=			[]
    for _ in range(1_0      ):
        __UpperCAmelCase									=			random.uniform(-0.5       ,   0.5      )
        __UpperCAmelCase									=			random.uniform(-0.5       ,   0.5      )

        # Bodies are created pairwise with opposite velocities so that the
        # total impulse remains zero
        bodies.append(
            Body(
                random.uniform(-0.5       ,   0.5      )       ,   random.uniform(-0.5       ,   0.5      )       ,   lowercase__       ,   lowercase__       ,   size=0.05       ,   )      )
        bodies.append(
            Body(
                random.uniform(-0.5       ,   0.5      )       ,   random.uniform(-0.5       ,   0.5      )       ,   -velocity_x       ,   -velocity_y       ,   size=0.05       ,   )      )
    return BodySystem(lowercase__       ,   0.01       ,   1_0       ,   0.1      )


if __name__ == "__main__":
       plot('Figure-8 solution to the 3-body-problem', example_a(), -2, 2, -2, 2)
       plot(
           'Moon\'s orbit around the earth',
           example_a(),
           -430000000,
           430000000,
           -430000000,
           430000000,
       )
       plot('Random system with many bodies', example_a(), -1.5, 1.5, -1.5, 1.5)








from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.mpnet.modeling_tf_mpnet import (
           TFMPNetForMaskedLM,
           TFMPNetForMultipleChoice,
           TFMPNetForQuestionAnswering,
           TFMPNetForSequenceClassification,
           TFMPNetForTokenClassification,
           TFMPNetModel,
       )
class 					A_   :


    '''simple docstring'''






    def __init__(self     ,      lowercase__     ,      lowercase__=13     ,      lowercase__=7     ,      lowercase__=True     ,      lowercase__=True     ,      lowercase__=False     ,      lowercase__=True     ,      lowercase__=99     ,      lowercase__=64     ,      lowercase__=2     ,      lowercase__=4     ,      lowercase__=64     ,      lowercase__="gelu"     ,      lowercase__=0.1     ,      lowercase__=0.1     ,      lowercase__=512     ,      lowercase__=16     ,      lowercase__=2     ,      lowercase__=0.02     ,      lowercase__=3     ,      lowercase__=4     ,      lowercase__=None     ,      )     ->      Tuple:
        __UpperCAmelCase									=			parent
        __UpperCAmelCase									=			batch_size
        __UpperCAmelCase									=			seq_length
        __UpperCAmelCase									=			is_training
        __UpperCAmelCase									=			use_input_mask
        __UpperCAmelCase									=			use_token_type_ids
        __UpperCAmelCase									=			use_labels
        __UpperCAmelCase									=			vocab_size
        __UpperCAmelCase									=			hidden_size
        __UpperCAmelCase									=			num_hidden_layers
        __UpperCAmelCase									=			num_attention_heads
        __UpperCAmelCase									=			intermediate_size
        __UpperCAmelCase									=			hidden_act
        __UpperCAmelCase									=			hidden_dropout_prob
        __UpperCAmelCase									=			attention_probs_dropout_prob
        __UpperCAmelCase									=			max_position_embeddings
        __UpperCAmelCase									=			type_vocab_size
        __UpperCAmelCase									=			type_sequence_label_size
        __UpperCAmelCase									=			initializer_range
        __UpperCAmelCase									=			num_labels
        __UpperCAmelCase									=			num_choices
        __UpperCAmelCase									=			scope


    def      lowerCAmelCase_						(self  )     ->      str:
        __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size  )

        __UpperCAmelCase									=			None
        if self.use_input_mask:
            __UpperCAmelCase									=			random_attention_mask([self.batch_size, self.seq_length]  )

        __UpperCAmelCase									=			None
        __UpperCAmelCase									=			None
        __UpperCAmelCase									=			None
        if self.use_labels:
            __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      self.type_sequence_label_size  )
            __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels  )
            __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      self.num_choices  )

        __UpperCAmelCase									=			MPNetConfig(
            vocab_size=self.vocab_size     ,      hidden_size=self.hidden_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     ,      initializer_range=self.initializer_range     ,      )
        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[Any]:
        __UpperCAmelCase									=			TFMPNetModel(config=lowercase__  )
        __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        __UpperCAmelCase									=			model(lowercase__  )
        __UpperCAmelCase									=			[input_ids, input_mask]
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      Dict:
        __UpperCAmelCase									=			TFMPNetForMaskedLM(config=lowercase__  )
        __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[Any]:
        __UpperCAmelCase									=			TFMPNetForQuestionAnswering(config=lowercase__  )
        __UpperCAmelCase									=			{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
        }
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.start_logits.shape     ,      (self.batch_size, self.seq_length)  )
        self.parent.assertEqual(result.end_logits.shape     ,      (self.batch_size, self.seq_length)  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      str:
        __UpperCAmelCase									=			self.num_labels
        __UpperCAmelCase									=			TFMPNetForSequenceClassification(lowercase__  )
        __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      str:
        __UpperCAmelCase									=			self.num_choices
        __UpperCAmelCase									=			TFMPNetForMultipleChoice(lowercase__  )
        __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
        __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
        __UpperCAmelCase									=			{
            '''input_ids''': multiple_choice_inputs_ids,
            '''attention_mask''': multiple_choice_input_mask,
        }
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)  )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[str]:
        __UpperCAmelCase									=			self.num_labels
        __UpperCAmelCase									=			TFMPNetForTokenClassification(lowercase__  )
        __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        __UpperCAmelCase									=			model(lowercase__  )
        self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)  )






    def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
        __UpperCAmelCase									=			self.prepare_config_and_inputs()
        ((__UpperCAmelCase)					,     (__UpperCAmelCase)					,     (__UpperCAmelCase)					,     (__UpperCAmelCase)					,     (__UpperCAmelCase)					,     (__UpperCAmelCase))									=			config_and_inputs
        __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict






@require_tf
class 					A_   (      _a ,     _a ,     unittest.TestCase						):


    '''simple docstring'''






    a__     							=       (
        (
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetModel,
        )
        if is_tf_available()
        else ()
    )
    a__     							=       (
        {
            "feature-extraction": TFMPNetModel,
            "fill-mask": TFMPNetForMaskedLM,
            "question-answering": TFMPNetForQuestionAnswering,
            "text-classification": TFMPNetForSequenceClassification,
            "token-classification": TFMPNetForTokenClassification,
            "zero-shot": TFMPNetForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    a__     							=       False
    a__     							=       False


    def      lowerCAmelCase_						(self  )     ->      int:
        __UpperCAmelCase									=			TFMPNetModelTester(self  )
        __UpperCAmelCase									=			ConfigTester(self     ,      config_class=lowercase__     ,      hidden_size=37  )


    def      lowerCAmelCase_						(self  )     ->      Optional[int]:
        self.config_tester.run_common_tests()


    def      lowerCAmelCase_						(self  )     ->      Dict:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_model(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      Any:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_masked_lm(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      int:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      Optional[int]:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      str:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      str:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase__  )






    @slow
    def      lowerCAmelCase_						(self  )     ->      List[str]:
        for model_name in ["microsoft/mpnet-base"]:
            __UpperCAmelCase									=			TFMPNetModel.from_pretrained(lowercase__  )
            self.assertIsNotNone(lowercase__  )






@require_tf
class 					A_   (      unittest.TestCase						):


    '''simple docstring'''






    @slow
    def      lowerCAmelCase_						(self  )     ->      Any:
        __UpperCAmelCase									=			TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''  )
        __UpperCAmelCase									=			tf.constant([[0, 1, 2, 3, 4, 5]]  )
        __UpperCAmelCase									=			model(lowercase__  )[0]

        __UpperCAmelCase									=			[1, 6, 768]
        self.assertEqual(output.shape     ,      lowercase__  )

        __UpperCAmelCase									=			tf.constant(
            [
                [
                    [-0.1067172, 0.08216473, 0.0024543],
                    [-0.03465879, 0.8354118, -0.03252288],
                    [-0.06569476, -0.12424111, -0.0494436],
                ]
            ]  )
        tf.debugging.assert_near(output[:, :3, :3]     ,      lowercase__     ,      atol=1E-4  )




