




from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
UpperCamelCase__  =		2_0

# Time between time steps in seconds
UpperCamelCase__  =		INTERVAL / 1_0_0_0


class        __SCREAMING_SNAKE_CASE							:

					def __init__(       self				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase = 1.0				, __lowercase = 1.0				, __lowercase = "blue"				, ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							position_x
						lowerCamelCase_							:	Union[str, Any]							     =							position_y
						lowerCamelCase_							:	Optional[Any]							     =							velocity_x
						lowerCamelCase_							:	int							     =							velocity_y
						lowerCamelCase_							:	Optional[int]							     =							mass
						lowerCamelCase_							:	int							     =							size
						lowerCamelCase_							:	List[Any]							     =							color

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return self.position_x, self.position_y

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return self.velocity_x, self.velocity_y

					def 			lowercase_							(       self				, __lowercase				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						self.velocity_x += force_x * delta_time
						self.velocity_y += force_y * delta_time





					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						self.position_x += self.velocity_x * delta_time
						self.position_y += self.velocity_y * delta_time


class        __SCREAMING_SNAKE_CASE							:

					def __init__(       self				, __lowercase				, __lowercase = 1.0				, __lowercase = 1.0				, __lowercase = 0.0				, ):







						'''simple docstring'''




						lowerCamelCase_							:	str							     =							bodies
						lowerCamelCase_							:	Optional[Any]							     =							gravitation_constant
						lowerCamelCase_							:	Optional[int]							     =							time_factor
						lowerCamelCase_							:	Any							     =							softening_factor

					def __len__(       self      ):







						'''simple docstring'''




						return len(self.bodies      )





					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						for bodya in self.bodies:
							lowerCamelCase_							:	Any							     =							0.0
							lowerCamelCase_							:	List[Any]							     =							0.0
							for bodya in self.bodies:
								if bodya != bodya:
									lowerCamelCase_							:	str							     =							bodya.position_x - bodya.position_x
									lowerCamelCase_							:	Union[str, Any]							     =							bodya.position_y - bodya.position_y

									# Calculation of the distance using Pythagoras's theorem
									# Extra factor due to the softening technique
									lowerCamelCase_							:	Optional[Any]							     =							(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       snake_case      (							A		,     A		,     A    )							->							None:




	'''simple docstring'''


	body_system.update_system(A    )

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






def       snake_case      (							A		,     A		,     A = -1		,     A = 1		,     A = -1		,     A = 1		,     )							->							None:




	'''simple docstring'''


	lowerCamelCase_							:	Any							     =							plt.figure()
	fig.canvas.set_window_title(A    )
	lowerCamelCase_							:	Dict							     =							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
	lowerCamelCase_							:	Any							     =							[
	    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(A    )

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

	lowerCamelCase_							:	Dict							     =							animation.FuncAnimation(  # noqa: F841
	    A		,     A		,     interval=A		,     blit=A    )

	plt.show()






def       snake_case      (							)							->							BodySystem:




	'''simple docstring'''


	lowerCamelCase_							:	Optional[int]							     =							0.970_0436
	lowerCamelCase_							:	Optional[Any]							     =							-0.2430_8753
	lowerCamelCase_							:	List[Any]							     =							0.4_6620_3685
	lowerCamelCase_							:	str							     =							0.4323_6573

	lowerCamelCase_							:	Union[str, Any]							     =							[
	    Body(A		,     A		,     A		,     A		,     size=0.2		,     color='red'    ),
	    Body(-position_x		,     -position_y		,     A		,     A		,     size=0.2		,     color='green'    ),
	    Body(0		,     0		,     -2 * velocity_x		,     -2 * velocity_y		,     size=0.2		,     color='blue'    ),
	]
	return BodySystem(A		,     time_factor=3    )






def       snake_case      (							)							->							BodySystem:




	'''simple docstring'''


	lowerCamelCase_							:	Any							     =							7.3476e22
	lowerCamelCase_							:	List[str]							     =							5.972e24
	lowerCamelCase_							:	str							     =							1022
	lowerCamelCase_							:	List[Any]							     =							384399000
	lowerCamelCase_							:	Dict							     =							6.674e-11

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

	lowerCamelCase_							:	List[str]							     =							Body(-earth_moon_distance		,     0		,     0		,     A		,     A		,     10000000		,     'grey'    )
	lowerCamelCase_							:	Any							     =							Body(0		,     0		,     0		,     A		,     A		,     50000000		,     'blue'    )
	return BodySystem([earth, moon]		,     A		,     time_factor=1000000    )






def       snake_case      (							)							->							BodySystem:




	'''simple docstring'''


	lowerCamelCase_							:	int							     =							[]
	for _ in range(10    ):
		lowerCamelCase_							:	Tuple							     =							random.uniform(-0.5		,     0.5    )
		lowerCamelCase_							:	Tuple							     =							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    )		,     A		,     A		,     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(A		,     0.01		,     10		,     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(),
	    -4_3_0_0_0_0_0_0_0,
	    4_3_0_0_0_0_0_0_0,
	    -4_3_0_0_0_0_0_0_0,
	    4_3_0_0_0_0_0_0_0,
	)
	plot('Random system with many bodies', example_a(), -1.5, 1.5, -1.5, 1.5)






from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel


@require_tf
class        __SCREAMING_SNAKE_CASE							:
					_lowerCamelCase							=				MarianConfig
					_lowerCamelCase							=				{}
					_lowerCamelCase							=				"""gelu"""

					def __init__(       self				, __lowercase				, __lowercase=13				, __lowercase=7				, __lowercase=True				, __lowercase=False				, __lowercase=99				, __lowercase=32				, __lowercase=2				, __lowercase=4				, __lowercase=37				, __lowercase=0.1				, __lowercase=0.1				, __lowercase=20				, __lowercase=2				, __lowercase=1				, __lowercase=0				, ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							parent
						lowerCamelCase_							:	List[str]							     =							batch_size
						lowerCamelCase_							:	List[str]							     =							seq_length
						lowerCamelCase_							:	Optional[int]							     =							is_training
						lowerCamelCase_							:	List[str]							     =							use_labels
						lowerCamelCase_							:	Tuple							     =							vocab_size
						lowerCamelCase_							:	Dict							     =							hidden_size
						lowerCamelCase_							:	List[str]							     =							num_hidden_layers
						lowerCamelCase_							:	List[str]							     =							num_attention_heads
						lowerCamelCase_							:	Tuple							     =							intermediate_size

						lowerCamelCase_							:	Tuple							     =							hidden_dropout_prob
						lowerCamelCase_							:	Dict							     =							attention_probs_dropout_prob
						lowerCamelCase_							:	List[str]							     =							max_position_embeddings
						lowerCamelCase_							:	Optional[int]							     =							eos_token_id
						lowerCamelCase_							:	List[Any]							     =							pad_token_id
						lowerCamelCase_							:	Optional[int]							     =							bos_token_id

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							ids_tensor([self.batch_size, self.seq_length - 1]				, self.vocab_size      )
						lowerCamelCase_							:	Tuple							     =							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )				, 1      )
						lowerCamelCase_							:	Optional[int]							     =							tf.concat([input_ids, eos_tensor]				, axis=1      )

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

						lowerCamelCase_							:	Optional[Any]							     =							self.config_cls(
						    vocab_size=self.vocab_size				, d_model=self.hidden_size				, encoder_layers=self.num_hidden_layers				, decoder_layers=self.num_hidden_layers				, encoder_attention_heads=self.num_attention_heads				, decoder_attention_heads=self.num_attention_heads				, encoder_ffn_dim=self.intermediate_size				, decoder_ffn_dim=self.intermediate_size				, dropout=self.hidden_dropout_prob				, attention_dropout=self.attention_probs_dropout_prob				, max_position_embeddings=self.max_position_embeddings				, eos_token_ids=[2]				, bos_token_id=self.bos_token_id				, pad_token_id=self.pad_token_id				, decoder_start_token_id=self.pad_token_id				, **self.config_updates				, )
						lowerCamelCase_							:	int							     =							prepare_marian_inputs_dict(__lowercase				, __lowercase				, __lowercase      )
						return config, inputs_dict





					def 			lowercase_							(       self				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							TFMarianModel(config=__lowercase      ).get_decoder()
						lowerCamelCase_							:	Tuple							     =							inputs_dict['input_ids']

						lowerCamelCase_							:	Union[str, Any]							     =							input_ids[:1, :]
						lowerCamelCase_							:	List[str]							     =							inputs_dict['attention_mask'][:1, :]
						lowerCamelCase_							:	List[str]							     =							inputs_dict['head_mask']
						lowerCamelCase_							:	List[str]							     =							1

						# first forward pass
						lowerCamelCase_							:	List[str]							     =							model(__lowercase				, attention_mask=__lowercase				, head_mask=__lowercase				, use_cache=__lowercase      )

						lowerCamelCase_     ,    lowerCamelCase_							:	Any							     =							outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						lowerCamelCase_							:	Any							     =							ids_tensor((self.batch_size, 3)				, config.vocab_size      )
						lowerCamelCase_							:	Optional[Any]							     =							tf.cast(ids_tensor((self.batch_size, 3)				, 2      )				, tf.inta      )

						# append to next input_ids and
						lowerCamelCase_							:	str							     =							tf.concat([input_ids, next_tokens]				, axis=-1      )
						lowerCamelCase_							:	List[str]							     =							tf.concat([attention_mask, next_attn_mask]				, axis=-1      )

						lowerCamelCase_							:	str							     =							model(__lowercase				, attention_mask=__lowercase      )[0]
						lowerCamelCase_							:	Optional[Any]							     =							model(__lowercase				, attention_mask=__lowercase				, past_key_values=__lowercase      )[0]

						self.parent.assertEqual(next_tokens.shape[1]				, output_from_past.shape[1]      )

						# select random slice
						lowerCamelCase_							:	Any							     =							int(ids_tensor((1,)				, output_from_past.shape[-1]      )      )
						lowerCamelCase_							:	Union[str, Any]							     =							output_from_no_past[:, -3:, random_slice_idx]
						lowerCamelCase_							:	int							     =							output_from_past[:, :, random_slice_idx]

						# test that outputs are equal for slice
						tf.debugging.assert_near(__lowercase				, __lowercase				, rtol=1E-3      )






def       snake_case      (							A		,     A		,     A		,     A=None		,     A=None		,     A=None		,     A=None		,     A=None		,     )							->							Dict:




	'''simple docstring'''


	if attention_mask is None:
		lowerCamelCase_							:	Tuple							     =							tf.cast(tf.math.not_equal(A		,     config.pad_token_id    )		,     tf.inta    )
	if decoder_attention_mask is None:
		lowerCamelCase_							:	Union[str, Any]							     =							tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape		,     dtype=tf.inta    ),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,     config.pad_token_id    )		,     tf.inta    ),
		    ]		,     axis=-1		,     )
	if head_mask is None:
		lowerCamelCase_							:	Optional[Any]							     =							tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
	if decoder_head_mask is None:
		lowerCamelCase_							:	Tuple							     =							tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
	if cross_attn_head_mask is None:
		lowerCamelCase_							:	Dict							     =							tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}


@require_tf
class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase     ,  lowerCamelCase     ,  unittest.TestCase						):
					_lowerCamelCase							=				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
					_lowerCamelCase							=				(TFMarianMTModel,) if is_tf_available() else ()
					_lowerCamelCase							=				(
					    {
					        """conversational""": TFMarianMTModel,
					        """feature-extraction""": TFMarianModel,
					        """summarization""": TFMarianMTModel,
					        """text2text-generation""": TFMarianMTModel,
					        """translation""": TFMarianMTModel,
					    }
					    if is_tf_available()
					    else {}
					)
					_lowerCamelCase							=				True
					_lowerCamelCase							=				False
					_lowerCamelCase							=				False

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							TFMarianModelTester(self      )
						lowerCamelCase_							:	str							     =							ConfigTester(self				, config_class=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self.config_tester.run_common_tests()





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*__lowercase      )


@require_tf
class        __SCREAMING_SNAKE_CASE							(	unittest.TestCase						):
					_lowerCamelCase							=				10_00  # show more chars for failing integration tests

					@classmethod
					def 			lowercase_							(       cls      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
						return cls

					@cached_property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return AutoTokenizer.from_pretrained(self.model_name      )

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return self.tokenizer.eos_token_id

					@cached_property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						warnings.simplefilter('error'      )
						lowerCamelCase_							:	TFMarianMTModel							     =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
						assert isinstance(__lowercase				, __lowercase      )
						lowerCamelCase_							:	Optional[Any]							     =							model.config
						self.assertListEqual(c.bad_words_ids				, [[c.pad_token_id]]      )
						self.assertEqual(c.max_length				, 512      )
						self.assertEqual(c.decoder_start_token_id				, c.pad_token_id      )
						return model

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							self.translate_src_text(**__lowercase      )
						self.assertListEqual(self.expected_text				, __lowercase      )





					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	str							     =							self.tokenizer(self.src_text				, **__lowercase				, padding=__lowercase				, return_tensors='tf'      )
						lowerCamelCase_							:	Optional[int]							     =							self.model.generate(
						    model_inputs.input_ids				, attention_mask=model_inputs.attention_mask				, num_beams=2				, max_length=128      )
						lowerCamelCase_							:	Optional[int]							     =							self.tokenizer.batch_decode(generated_ids.numpy()				, skip_special_tokens=__lowercase      )
						return generated_words


@require_sentencepiece
@require_tokenizers
@require_tf
class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				"""mt"""
					_lowerCamelCase							=				"""en"""
					_lowerCamelCase							=				["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
					_lowerCamelCase							=				["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]

					@unittest.skip('Skipping until #12647 is resolved.'      )
					@slow
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self._assert_generated_batch_equal_expected()


@require_sentencepiece
@require_tokenizers
@require_tf
class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				"""en"""
					_lowerCamelCase							=				"""zh"""
					_lowerCamelCase							=				["""My name is Wolfgang and I live in Berlin"""]
					_lowerCamelCase							=				["""我叫沃尔夫冈 我住在柏林"""]

					@unittest.skip('Skipping until #12647 is resolved.'      )
					@slow
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self._assert_generated_batch_equal_expected()


@require_sentencepiece
@require_tokenizers
@require_tf
class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				"""en"""
					_lowerCamelCase							=				"""ROMANCE"""
					_lowerCamelCase							=				[
					    """>>fr<< Don't spend so much time watching TV.""",
					    """>>pt<< Your message has been sent.""",
					    """>>es<< He's two years older than me.""",
					]
					_lowerCamelCase							=				[
					    """Ne passez pas autant de temps à regarder la télé.""",
					    """A sua mensagem foi enviada.""",
					    """Es dos años más viejo que yo.""",
					]

					@unittest.skip('Skipping until #12647 is resolved.'      )
					@slow
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self._assert_generated_batch_equal_expected()





					@unittest.skip('Skipping until #12647 is resolved.'      )
					@slow
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[int]							     =							TranslationPipeline(self.model				, self.tokenizer				, framework='tf'      )
						lowerCamelCase_							:	Optional[int]							     =							pipeline(self.src_text      )
						self.assertEqual(self.expected_text				, [x['translation_text'] for x in output]      )
