



'''simple docstring'''


from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
UpperCAmelCase_	:     Union[str, Any]							     =	20

# Time between time steps in seconds
UpperCAmelCase_	:     str							     =	INTERVAL / 1000





class 					lowercase__						:





							'''simple docstring'''


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






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






							@property
							def UpperCAmelCase_			(					self						):
											return self.velocity_x, self.velocity_y






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case						):
											self.velocity_x += force_x * delta_time
											self.velocity_y += force_y * delta_time






							def UpperCAmelCase_			(					self					,      __snake_case						):
											self.position_x += self.velocity_x * delta_time
											self.position_y += self.velocity_y * delta_time





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case = 1.0					,      __snake_case = 1.0					,      __snake_case = 0.0					,      ):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   bodies
											_SCREAMING_SNAKE_CASE				: Dict  	=   gravitation_constant
											_SCREAMING_SNAKE_CASE				: int  	=   time_factor
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   softening_factor






							def __len__(					self						):
											return len(self.bodies						)






							def UpperCAmelCase_			(					self					,      __snake_case						):
											for bodya in self.bodies:
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   0.0
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   0.0
															for bodya in self.bodies:
																			if bodya != bodya:
																							_SCREAMING_SNAKE_CASE				: Any  	=   bodya.position_x - bodya.position_x
																							_SCREAMING_SNAKE_CASE				: str  	=   bodya.position_y - bodya.position_y

																							# Calculation of the distance using Pythagoras's theorem
																							# Extra factor due to the softening technique
																							_SCREAMING_SNAKE_CASE				: Optional[int]  	=   (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(__snake_case					,      __snake_case					,      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_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				body_system.update_system(SCREAMING_SNAKE_CASE__  )

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







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__ = -1							,   SCREAMING_SNAKE_CASE__ = 1							,   SCREAMING_SNAKE_CASE__ = -1							,   SCREAMING_SNAKE_CASE__ = 1							,   ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   plt.figure()
				fig.canvas.set_window_title(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: int  	=   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
				_SCREAMING_SNAKE_CASE				: Dict  	=   [
				    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(SCREAMING_SNAKE_CASE__  )

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

				_SCREAMING_SNAKE_CASE				: List[str]  	=   animation.FuncAnimation(  # noqa: F841
				    SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   interval=SCREAMING_SNAKE_CASE__							,   blit=SCREAMING_SNAKE_CASE__  )

				plt.show()







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Tuple  	=   0.9_7_0_0_4_3_6
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   -0.2_4_3_0_8_7_5_3
				_SCREAMING_SNAKE_CASE				: Tuple  	=   0.4_6_6_2_0_3_6_8_5
				_SCREAMING_SNAKE_CASE				: List[str]  	=   0.4_3_2_3_6_5_7_3

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







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   7.34_76E22
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   5.9_72E24
				_SCREAMING_SNAKE_CASE				: Tuple  	=   1022
				_SCREAMING_SNAKE_CASE				: int  	=   3_8439_9000
				_SCREAMING_SNAKE_CASE				: Dict  	=   6.6_74E-11

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

				_SCREAMING_SNAKE_CASE				: str  	=   Body(-earth_moon_distance							,   0							,   0							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   1000_0000							,   """grey"""  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   Body(0							,   0							,   0							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   5000_0000							,   """blue"""  )
				return BodySystem([earth, moon]							,   SCREAMING_SNAKE_CASE__							,   time_factor=100_0000  )







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   []
				for _ in range(10  ):
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   random.uniform(-0.5							,   0.5  )
								_SCREAMING_SNAKE_CASE				: 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  )							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   size=0.0_5							,   )  )
								bodies.append(
								    Body(
								        random.uniform(-0.5							,   0.5  )							,   random.uniform(-0.5							,   0.5  )							,   -velocity_x							,   -velocity_y							,   size=0.0_5							,   )  )
				return BodySystem(SCREAMING_SNAKE_CASE__							,   0.0_1							,   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_3000_0000,
				    4_3000_0000,
				    -4_3000_0000,
				    4_3000_0000,
				)
				plot('Random system with many bodies', example_a(), -1.5, 1.5, -1.5, 1.5)








'''simple docstring'''


import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def     snake_case_     (						SCREAMING_SNAKE_CASE__=None							,   SCREAMING_SNAKE_CASE__=None  ):







				"""simple docstring"""





				return field(default_factory=lambda: default							,   metadata=SCREAMING_SNAKE_CASE__  )





@dataclass
class 					lowercase__						:





							'''simple docstring'''


							A_							:   str   			=						field(
							    metadata={"""help""": """The csv file to plot."""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Disable logarithmic scale when plotting"""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={
							        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
							    }  ,  )
							A_							:   Optional[str]   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}  ,  )
							A_							:   Optional[List[str]]   			=						list_field(
							    default=_snake_case  ,  metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}      )







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				try:
								int(SCREAMING_SNAKE_CASE__  )
								return True
				except ValueError:
								return False







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				try:
								float(SCREAMING_SNAKE_CASE__  )
								return True
				except ValueError:
								return False





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args
											_SCREAMING_SNAKE_CASE				: Dict  	=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

											with open(self.args.csv_file					,      newline=""""""						) as csv_file:
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   csv.DictReader(__snake_case						)
															for row in reader:
																			_SCREAMING_SNAKE_CASE				: int  	=   row["""model"""]
																			self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]						)						)
																			self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]						)						)
																			if can_convert_to_int(row["""result"""]						):
																							# value is not None
																							_SCREAMING_SNAKE_CASE				: Any  	=   int(row["""result"""]						)
																			elif can_convert_to_float(row["""result"""]						):
																							# value is not None
																							_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   float(row["""result"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   plt.subplots()
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   """Time usage""" if self.args.is_time else """Memory usage"""
											_SCREAMING_SNAKE_CASE				: int  	=   title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

											if not self.args.no_log_scale:
															# set logarithm scales
															ax.set_xscale("""log"""						)
															ax.set_yscale("""log"""						)

											for axis in [ax.xaxis, ax.yaxis]:
															axis.set_major_formatter(ScalarFormatter()						)

											for model_name_idx, model_name in enumerate(self.result_dict.keys()						):
															_SCREAMING_SNAKE_CASE				: str  	=   sorted(set(self.result_dict[model_name]["""bsz"""]						)						)
															_SCREAMING_SNAKE_CASE				: List[str]  	=   sorted(set(self.result_dict[model_name]["""seq_len"""]						)						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.result_dict[model_name]["""result"""]

															((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: Tuple  	=   (
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															_SCREAMING_SNAKE_CASE				: Any  	=   (
															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
															)

															for inner_loop_value in inner_loop_array:
																			if self.args.plot_along_batch:
																							_SCREAMING_SNAKE_CASE				: Any  	=   np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,      dtype=__snake_case					,      )
																			else:
																							_SCREAMING_SNAKE_CASE				: Dict  	=   np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,      dtype=np.floataa					,      )

																			((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: List[Any]  	=   (
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			_SCREAMING_SNAKE_CASE				: str  	=   np.asarray(__snake_case					,      __snake_case						)[: len(__snake_case						)]
																			plt.scatter(
																			    __snake_case					,      __snake_case					,      label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""						)
																			plt.plot(__snake_case					,      __snake_case					,      """--"""						)

															title_str += f""" {label_model_name} vs."""

											_SCREAMING_SNAKE_CASE				: str  	=   title_str[:-4]
											_SCREAMING_SNAKE_CASE				: int  	=   """Time in s""" if self.args.is_time else """Memory in MB"""

											# plot
											plt.title(__snake_case						)
											plt.xlabel(__snake_case						)
											plt.ylabel(__snake_case						)
											plt.legend()

											if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file						)
											else:
															plt.show()







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   HfArgumentParser(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE				: int  	=   Plot(args=SCREAMING_SNAKE_CASE__  )
				plot.plot()


if __name__ == "__main__":
				main()




