'''simple docstring'''




from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
_lowercase		:					Dict													=		20

# Time between time steps in seconds
_lowercase		:					Optional[int]													=		INTERVAL / 1000





class        __magic_name__		:







			def __init__(      self							:    Tuple							,     lowercase_							:    float							,     lowercase_							:    float							,     lowercase_							:    float							,     lowercase_							:    float							,     lowercase_							:    float = 1.0							,     lowercase_							:    float = 1.0							,     lowercase_							:    str = "blue"							,     ):
										lowercase_   :     Any									=			position_x
										lowercase_   :     Optional[int]									=			position_y
										lowercase_   :     Dict									=			velocity_x
										lowercase_   :     Dict									=			velocity_y
										lowercase_   :     Dict									=			mass
										lowercase_   :     List[str]									=			size
										lowercase_   :     Any									=			color







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										return self.position_x, self.position_y







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										return self.velocity_x, self.velocity_y







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    float							,     lowercase_							:    float							,     lowercase_							:    float       ):
										self.velocity_x += force_x * delta_time
										self.velocity_y += force_y * delta_time







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    float       ):
										self.position_x += self.velocity_x * delta_time
										self.position_y += self.velocity_y * delta_time





class        __magic_name__		:







			def __init__(      self							:    List[str]							,     lowercase_							:    list[Body]							,     lowercase_							:    float = 1.0							,     lowercase_							:    float = 1.0							,     lowercase_							:    float = 0.0							,     ):
										lowercase_   :     int									=			bodies
										lowercase_   :     Optional[Any]									=			gravitation_constant
										lowercase_   :     Tuple									=			time_factor
										lowercase_   :     Union[str, Any]									=			softening_factor







			def __len__(      self							:    Union[str, Any]       ):
										return len(self.bodies       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]							,     lowercase_							:    float       ):
										for bodya in self.bodies:
																	lowercase_   :     Optional[Any]									=			0.0
																	lowercase_   :     Optional[Any]									=			0.0
																	for bodya in self.bodies:
																								if bodya != bodya:
																															lowercase_   :     Optional[Any]									=			bodya.position_x - bodya.position_x
																															lowercase_   :     Any									=			bodya.position_y - bodya.position_y

																															# Calculation of the distance using Pythagoras's theorem
																															# Extra factor due to the softening technique
																															lowercase_   :     List[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 				lowerCamelCase      ( UpperCAmelCase__			:   BodySystem , UpperCAmelCase__			:   float , UpperCAmelCase__			:   list[plt.Circle]					)     -> None:
							body_system.update_system(UpperCAmelCase__					)

							# Update the positions of the patches
							for patch, body in zip(UpperCAmelCase__ , body_system.bodies					):
														lowercase_   :     Optional[int]									=			(body.position_x, body.position_y)




def 				lowerCamelCase      ( UpperCAmelCase__			:   str , UpperCAmelCase__			:   BodySystem , UpperCAmelCase__			:   float = -1 , UpperCAmelCase__			:   float = 1 , UpperCAmelCase__			:   float = -1 , UpperCAmelCase__			:   float = 1 , )     -> None:
							lowercase_   :     Dict									=			plt.figure()
							fig.canvas.set_window_title(UpperCAmelCase__					)
							lowercase_   :     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
							lowercase_   :     Optional[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(UpperCAmelCase__					)

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

							lowercase_   :     Dict									=			animation.FuncAnimation(  # noqa: F841
							    UpperCAmelCase__ , UpperCAmelCase__ , interval=UpperCAmelCase__ , blit=UpperCAmelCase__					)

							plt.show()




def 				lowerCamelCase      ( )     -> BodySystem:
							lowercase_   :     List[Any]									=			0.970_0436
							lowercase_   :     Any									=			-0.2430_8753
							lowercase_   :     Tuple									=			0.4_6620_3685
							lowercase_   :     Optional[int]									=			0.4323_6573

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




def 				lowerCamelCase      ( )     -> BodySystem:
							lowercase_   :     Dict									=			7.3476e22
							lowercase_   :     str									=			5.972e24
							lowercase_   :     str									=			1022
							lowercase_   :     Tuple									=			384399000
							lowercase_   :     Optional[Any]									=			6.674e-11

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

							lowercase_   :     Optional[Any]									=			Body(-earth_moon_distance , 0 , 0 , UpperCAmelCase__ , UpperCAmelCase__ , 10000000 , """grey"""					)
							lowercase_   :     Optional[int]									=			Body(0 , 0 , 0 , UpperCAmelCase__ , UpperCAmelCase__ , 50000000 , """blue"""					)
							return BodySystem([earth, moon] , UpperCAmelCase__ , time_factor=1000000					)




def 				lowerCamelCase      ( )     -> BodySystem:
							lowercase_   :     List[str]									=			[]
							for _ in range(10					):
														lowercase_   :     int									=			random.uniform(-0.5 , 0.5					)
														lowercase_   :     str									=			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					) , UpperCAmelCase__ , UpperCAmelCase__ , 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(UpperCAmelCase__ , 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(),
						    -430000000,
						    430000000,
						    -430000000,
						    430000000,
						)
						plot("Random system with many bodies", example_a(), -1.5, 1.5, -1.5, 1.5)






'''simple docstring'''




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        __magic_name__		:
			UpperCamelCase__							=			MarianConfig
			UpperCamelCase__							=			{}
			UpperCamelCase__							=			'''gelu'''







			def __init__(      self							:    Optional[Any]							,     lowercase_							:    Tuple							,     lowercase_							:    Optional[int]=13							,     lowercase_							:    Dict=7							,     lowercase_							:    str=True							,     lowercase_							:    str=False							,     lowercase_							:    Tuple=99							,     lowercase_							:    int=32							,     lowercase_							:    int=2							,     lowercase_							:    Optional[int]=4							,     lowercase_							:    Optional[int]=37							,     lowercase_							:    Any=0.1							,     lowercase_							:    List[Any]=0.1							,     lowercase_							:    Union[str, Any]=20							,     lowercase_							:    List[str]=2							,     lowercase_							:    int=1							,     lowercase_							:    Union[str, Any]=0							,     ):
										lowercase_   :     str									=			parent
										lowercase_   :     Any									=			batch_size
										lowercase_   :     List[Any]									=			seq_length
										lowercase_   :     str									=			is_training
										lowercase_   :     str									=			use_labels
										lowercase_   :     str									=			vocab_size
										lowercase_   :     Optional[Any]									=			hidden_size
										lowercase_   :     Optional[Any]									=			num_hidden_layers
										lowercase_   :     List[Any]									=			num_attention_heads
										lowercase_   :     List[Any]									=			intermediate_size

										lowercase_   :     List[str]									=			hidden_dropout_prob
										lowercase_   :     Union[str, Any]									=			attention_probs_dropout_prob
										lowercase_   :     str									=			max_position_embeddings
										lowercase_   :     Optional[int]									=			eos_token_id
										lowercase_   :     Dict									=			pad_token_id
										lowercase_   :     Any									=			bos_token_id







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     Optional[int]									=			ids_tensor([self.batch_size, self.seq_length - 1]							,     self.vocab_size       )
										lowercase_   :     Tuple									=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )							,     1       )
										lowercase_   :     Dict									=			tf.concat([input_ids, eos_tensor]							,     axis=1       )

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

										lowercase_   :     List[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							,     )
										lowercase_   :     List[Any]									=			prepare_marian_inputs_dict(lowercase_							,     lowercase_							,     lowercase_       )
										return config, inputs_dict







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    List[Any]							,     lowercase_							:    Optional[int]       ):
										lowercase_   :     Any									=			TFMarianModel(config=lowercase_       ).get_decoder()
										lowercase_   :     Optional[Any]									=			inputs_dict["""input_ids"""]

										lowercase_   :     Tuple									=			input_ids[:1, :]
										lowercase_   :     Optional[Any]									=			inputs_dict["""attention_mask"""][:1, :]
										lowercase_   :     List[str]									=			inputs_dict["""head_mask"""]
										lowercase_   :     Dict									=			1

										# first forward pass
										lowercase_   :     List[Any]									=			model(lowercase_							,     attention_mask=lowercase_							,     head_mask=lowercase_							,     use_cache=lowercase_       )

										lowercase_		,		lowercase_   :     Any									=			outputs.to_tuple()

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

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

										lowercase_   :     Optional[int]									=			model(lowercase_							,     attention_mask=lowercase_       )[0]
										lowercase_   :     Union[str, 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
										lowercase_   :     Union[str, Any]									=			int(ids_tensor((1,)							,     output_from_past.shape[-1]       )       )
										lowercase_   :     List[str]									=			output_from_no_past[:, -3:, random_slice_idx]
										lowercase_   :     Any									=			output_from_past[:, :, random_slice_idx]

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




def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str] , UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   Tuple=None , UpperCAmelCase__			:   Union[str, Any]=None , UpperCAmelCase__			:   str=None , UpperCAmelCase__			:   Any=None , UpperCAmelCase__			:   Any=None , )     -> List[Any]:
							if attention_mask is None:
														lowercase_   :     Tuple									=			tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id					) , tf.inta					)
							if decoder_attention_mask is None:
														lowercase_   :     Optional[int]									=			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:
														lowercase_   :     Union[str, Any]									=			tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
							if decoder_head_mask is None:
														lowercase_   :     Optional[int]									=			tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
							if cross_attn_head_mask is None:
														lowercase_   :     Any									=			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        __magic_name__		(       _UpperCAmelCase,	_UpperCAmelCase,	unittest.TestCase):
			UpperCamelCase__							=			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
			UpperCamelCase__							=			(TFMarianMTModel,) if is_tf_available() else ()
			UpperCamelCase__							=			(
			    {
			        '''conversational''': TFMarianMTModel,
			        '''feature-extraction''': TFMarianModel,
			        '''summarization''': TFMarianMTModel,
			        '''text2text-generation''': TFMarianMTModel,
			        '''translation''': TFMarianMTModel,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCamelCase__							=			True
			UpperCamelCase__							=			False
			UpperCamelCase__							=			False







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_   :     Dict									=			TFMarianModelTester(self       )
										lowercase_   :     Tuple									=			ConfigTester(self							,     config_class=lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										self.config_tester.run_common_tests()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     Dict									=			self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowercase_       )





@require_tf
class        __magic_name__		(       unittest.TestCase):
			UpperCamelCase__							=			1000  # show more chars for failing integration tests







			@classmethod
			def       SCREAMING_SNAKE_CASE_	(      cls							:    Optional[int]       ):
										lowercase_   :     Optional[Any]									=			f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
										return cls







			@cached_property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										return AutoTokenizer.from_pretrained(self.model_name       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										return self.tokenizer.eos_token_id







			@cached_property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										warnings.simplefilter("""error"""       )
										lowercase_   :     TFMarianMTModel									=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
										assert isinstance(lowercase_							,     lowercase_       )
										lowercase_   :     List[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       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     **lowercase_							:    str       ):
										lowercase_   :     List[Any]									=			self.translate_src_text(**lowercase_       )
										self.assertListEqual(self.expected_text							,     lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     **lowercase_							:    Dict       ):
										lowercase_   :     Dict									=			self.tokenizer(self.src_text							,     **lowercase_							,     padding=lowercase_							,     return_tensors="""tf"""       )
										lowercase_   :     Tuple									=			self.model.generate(
										    model_inputs.input_ids							,     attention_mask=model_inputs.attention_mask							,     num_beams=2							,     max_length=128       )
										lowercase_   :     Any									=			self.tokenizer.batch_decode(generated_ids.numpy()							,     skip_special_tokens=lowercase_       )
										return generated_words





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







			@unittest.skip("""Skipping until #12647 is resolved."""       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			'''en'''
			UpperCamelCase__							=			'''zh'''
			UpperCamelCase__							=			['''My name is Wolfgang and I live in Berlin''']
			UpperCamelCase__							=			['''我叫沃尔夫冈 我住在柏林''']







			@unittest.skip("""Skipping until #12647 is resolved."""       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			'''en'''
			UpperCamelCase__							=			'''ROMANCE'''
			UpperCamelCase__							=			[
			    '''>>fr<< Don\'t spend so much time watching TV.''',
			    '''>>pt<< Your message has been sent.''',
			    '''>>es<< He\'s two years older than me.''',
			]
			UpperCamelCase__							=			[
			    '''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       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										self._assert_generated_batch_equal_expected()







			@unittest.skip("""Skipping until #12647 is resolved."""       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										lowercase_   :     List[str]									=			TranslationPipeline(self.model							,     self.tokenizer							,     framework="""tf"""       )
										lowercase_   :     Optional[int]									=			pipeline(self.src_text       )
										self.assertEqual(self.expected_text							,     [x["""translation_text"""] for x in output]       )





