




"""simple docstring"""




from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
a_            =   2_0

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

class        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_ = "blue"							,		)		->   None:
					__lowercase	:       Optional[int]				=   position_x
					__lowercase	:       List[Any]				=   position_y
					__lowercase	:       Optional[int]				=   velocity_x
					__lowercase	:       List[str]				=   velocity_y
					__lowercase	:       List[Any]				=   mass
					__lowercase	:       Tuple				=   size
					__lowercase	:       Tuple				=   color
		@property
		def 					_lowerCamelCase    (		self   )		->   tuple[float, float]:
					return self.position_x, self.position_y
		@property
		def 					_lowerCamelCase    (		self   )		->   tuple[float, float]:
					return self.velocity_x, self.velocity_y
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   None:
					self.velocity_x += force_x * delta_time
					self.velocity_y += force_y * delta_time
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   None:
					self.position_x += self.velocity_x * delta_time
					self.position_y += self.velocity_y * delta_time


class        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_ = 0.0							,		)		->   None:
					__lowercase	:       int				=   bodies
					__lowercase	:       Dict				=   gravitation_constant
					__lowercase	:       List[Any]				=   time_factor
					__lowercase	:       List[str]				=   softening_factor
		def __len__(		self   )		->   int:
					return len(self.bodies   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   None:
					for bodya in self.bodies:
								__lowercase	:       Optional[Any]				=   0.0
								__lowercase	:       Any				=   0.0
								for bodya in self.bodies:
											if bodya != bodya:
														__lowercase	:       Union[str, Any]				=   bodya.position_x - bodya.position_x
														__lowercase	:       str				=   bodya.position_y - bodya.position_y

														# Calculation of the distance using Pythagoras's theorem
														# Extra factor due to the softening technique
														__lowercase	:       str				=   (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(UpperCamelCase_							,		UpperCamelCase_							,		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       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					):
			body_system.update_system(__UpperCamelCase					)

			# Update the positions of the patches
			for patch, body in zip(__UpperCamelCase					,							body_system.bodies					):
						__lowercase	:       List[str]				=   (body.position_x, body.position_y)







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase = -1					,							__UpperCamelCase = 1					,							__UpperCamelCase = -1					,							__UpperCamelCase = 1					,							):
			__lowercase	:       List[str]				=   plt.figure()
			fig.canvas.set_window_title(__UpperCamelCase					)
			__lowercase	:       List[Any]				=   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	:       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					) -> list[plt.Circle]:
						update_step(__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					)
						return patches

			__lowercase	:       Tuple				=   animation.FuncAnimation(  # noqa: F841
			    __UpperCamelCase					,							__UpperCamelCase					,							interval=__UpperCamelCase					,							blit=__UpperCamelCase					)

			plt.show()







def       __UpperCAmelCase      (				):
			__lowercase	:       Dict				=   0.9_700_436
			__lowercase	:       Tuple				=   -0.24_308_753
			__lowercase	:       Any				=   0.466_203_685
			__lowercase	:       str				=   0.43_236_573

			__lowercase	:       int				=   [
			    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       __UpperCAmelCase      (				):
			__lowercase	:       Tuple				=   7.3_476e22
			__lowercase	:       Optional[int]				=   5.972e24
			__lowercase	:       str				=   10_22
			__lowercase	:       Any				=   3_84_39_90_00
			__lowercase	:       Optional[int]				=   6.674e-11

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

			__lowercase	:       Tuple				=   Body(-earth_moon_distance					,							0					,							0					,							__UpperCamelCase					,							__UpperCamelCase					,							10_00_00_00					,							'''grey'''					)
			__lowercase	:       Dict				=   Body(0					,							0					,							0					,							__UpperCamelCase					,							__UpperCamelCase					,							50_00_00_00					,							'''blue'''					)
			return BodySystem([earth, moon]					,							__UpperCamelCase					,							time_factor=1_00_00_00					)







def       __UpperCAmelCase      (				):
			__lowercase	:       Optional[Any]				=   []
			for _ in range(10					):
						__lowercase	:       str				=   random.uniform(-0.5					,							0.5					)
						__lowercase	:       int				=   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(),
			    -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)









"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
			from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

			from .test_feature_extraction_speechta import floats_list


a_            =   get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')

@require_torch
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   Dict:
					__lowercase	:       Union[str, Any]				=   tempfile.mkdtemp()

					__lowercase	:       List[str]				=   SpeechTaTokenizer(UpperCamelCase_   )
					tokenizer.save_pretrained(self.tmpdirname   )

					__lowercase	:       Dict				=   {
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1E-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					__lowercase	:       List[Any]				=   os.path.join(self.tmpdirname							,		UpperCamelCase_   )
					with open(self.feature_extraction_file							,		'''w'''							,		encoding='''utf-8'''   ) as fp:
								fp.write(json.dumps(UpperCamelCase_   ) + '''\n'''   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Union[str, Any]:
					return SpeechTaTokenizer.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Optional[Any]:
					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					shutil.rmtree(self.tmpdirname   )
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					__lowercase	:       List[str]				=   self.get_tokenizer()
					__lowercase	:       int				=   self.get_feature_extractor()

					__lowercase	:       Any				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					processor.save_pretrained(self.tmpdirname   )
					__lowercase	:       List[Any]				=   SpeechTaProcessor.from_pretrained(self.tmpdirname   )

					self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,		UpperCamelCase_   )

					self.assertEqual(processor.feature_extractor.to_json_string()							,		feature_extractor.to_json_string()   )
					self.assertIsInstance(processor.feature_extractor							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   SpeechTaProcessor(tokenizer=self.get_tokenizer()							,		feature_extractor=self.get_feature_extractor()   )
					processor.save_pretrained(self.tmpdirname   )

					__lowercase	:       str				=   self.get_tokenizer(bos_token='''(BOS)'''							,		eos_token='''(EOS)'''   )
					__lowercase	:       Dict				=   self.get_feature_extractor(do_normalize=UpperCamelCase_							,		padding_value=1.0   )

					__lowercase	:       Tuple				=   SpeechTaProcessor.from_pretrained(
					    self.tmpdirname							,		bos_token='''(BOS)'''							,		eos_token='''(EOS)'''							,		do_normalize=UpperCamelCase_							,		padding_value=1.0   )

					self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,		UpperCamelCase_   )

					self.assertEqual(processor.feature_extractor.to_json_string()							,		feature_extractor_add_kwargs.to_json_string()   )
					self.assertIsInstance(processor.feature_extractor							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       Optional[Any]				=   self.get_feature_extractor()
					__lowercase	:       Optional[Any]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       int				=   floats_list((3, 10_00)   )

					__lowercase	:       str				=   feature_extractor(audio=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       Dict				=   processor(audio=UpperCamelCase_							,		return_tensors='''np'''   )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1E-2   )
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					__lowercase	:       str				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Optional[int]				=   floats_list((3, 10_00)   )

					__lowercase	:       List[str]				=   feature_extractor(audio_target=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       List[str]				=   processor(audio_target=UpperCamelCase_							,		return_tensors='''np'''   )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1E-2   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       List[Any]				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       List[str]				=   '''This is a test string'''

					__lowercase	:       Any				=   processor(text=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	:       int				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       List[Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   '''This is a test string'''

					__lowercase	:       Tuple				=   processor(text_target=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       int				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Optional[Any]				=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__lowercase	:       str				=   processor.batch_decode(UpperCamelCase_   )
					__lowercase	:       int				=   tokenizer.batch_decode(UpperCamelCase_   )

					self.assertListEqual(UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       Union[str, Any]				=   self.get_feature_extractor()
					__lowercase	:       int				=   self.get_tokenizer()

					__lowercase	:       Optional[int]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					self.assertListEqual(
					    processor.model_input_names							,		feature_extractor.model_input_names							,		msg='''`processor` and `feature_extractor` model input names do not match'''							,		)



