




"""simple docstring"""




import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
			import torch

			from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
			import pyanvml.pyanvml as nvml


a_            =   logging.get_logger(__name__)

class        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									=42
		UpperCamelCase									=42
		UpperCamelCase									="PyTorch"
		@property
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					return torch.__version__
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   float:
					__lowercase	:       Optional[int]				=   self._prepare_inference_func(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					return self._measure_speed(_inference   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   [Memory, Optional[MemorySummary]]:
					__lowercase	:       str				=   self._prepare_inference_func(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					return self._measure_memory(_inference   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   float:
					__lowercase	:       Any				=   self._prepare_train_func(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					return self._measure_speed(_train   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   [Memory, Optional[MemorySummary]]:
					__lowercase	:       int				=   self._prepare_train_func(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					return self._measure_memory(_train   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Callable[[], None]:
					__lowercase	:       List[Any]				=   self.config_dict[model_name]

					if self.args.torchscript:
								__lowercase	:       Tuple				=   True

					__lowercase	:       List[str]				=   (
					    hasattr(UpperCamelCase_							,		'''architectures'''   )
					    and isinstance(config.architectures							,		UpperCamelCase_   )
					    and len(config.architectures   ) > 0
					)
					if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
											__lowercase	:       Dict				=   config.architectures[0]
											__lowercase	:       Union[str, Any]				=   __import__('''transformers'''							,		fromlist=[model_class]   )
											__lowercase	:       Union[str, Any]				=   getattr(UpperCamelCase_							,		UpperCamelCase_   )
											__lowercase	:       List[str]				=   model_cls(UpperCamelCase_   )
								except ImportError:
											raise ImportError(
											    F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
											    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''   )
					else:
								__lowercase	:       Union[str, Any]				=   MODEL_MAPPING[config.__class__](UpperCamelCase_   )

					model.eval()
					model.to(self.args.device   )

					# encoder-decoder has vocab size saved differently
					__lowercase	:       Dict				=   config.vocab_size if hasattr(UpperCamelCase_							,		'''vocab_size'''   ) else config.encoder.vocab_size
					__lowercase	:       str				=   torch.randint(UpperCamelCase_							,		(batch_size, sequence_length)							,		dtype=torch.long							,		device=self.args.device   )

					if self.args.fpaa:
								logger.info('''Running training in Mixed Precision...'''   )
								if not self.args.is_gpu:
											raise ValueError('''Mixed precision is possible only for GPU.'''   )
								# amp seems to have memory leaks so that memory usage
								# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
								model.half()

					if self.args.torchscript:
								with torch.no_grad():
											__lowercase	:       Tuple				=   torch.jit.trace(UpperCamelCase_							,		UpperCamelCase_   )
					else:
								__lowercase	:       int				=   model

					def encoder_decoder_forward():
								with torch.no_grad():
											__lowercase	:       List[Any]				=   inference_model(UpperCamelCase_							,		decoder_input_ids=UpperCamelCase_   )
								return outputs

					def encoder_forward():
								with torch.no_grad():
											__lowercase	:       Dict				=   inference_model(UpperCamelCase_   )
								return outputs

					__lowercase	:       Tuple				=   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
					return _forward
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Callable[[], None]:
					__lowercase	:       Optional[int]				=   self.config_dict[model_name]

					__lowercase	:       List[str]				=   (
					    hasattr(UpperCamelCase_							,		'''architectures'''   )
					    and isinstance(config.architectures							,		UpperCamelCase_   )
					    and len(config.architectures   ) > 0
					)
					if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
											__lowercase	:       Optional[int]				=   config.architectures[0]
											__lowercase	:       List[Any]				=   __import__('''transformers'''							,		fromlist=[model_class]   )
											__lowercase	:       int				=   getattr(UpperCamelCase_							,		UpperCamelCase_   )
											__lowercase	:       Dict				=   model_cls(UpperCamelCase_   )
								except ImportError:
											raise ImportError(
											    F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
											    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''   )
					else:
								__lowercase	:       Tuple				=   MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase_   )

					if self.args.torchscript:
								raise NotImplementedError('''Training for torchscript is currently not implemented'''   )
					else:
								__lowercase	:       Any				=   model

					model.train()
					model.to(self.args.device   )

					# encoder-decoder has vocab size saved differently
					__lowercase	:       Optional[Any]				=   config.vocab_size if hasattr(UpperCamelCase_							,		'''vocab_size'''   ) else config.encoder.vocab_size
					__lowercase	:       Union[str, Any]				=   torch.randint(UpperCamelCase_							,		(batch_size, sequence_length)							,		dtype=torch.long							,		device=self.args.device   )

					if self.args.fpaa:
								logger.info('''Running training in Mixed Precision...'''   )
								if not self.args.is_gpu:
											raise ValueError('''Mixed precision is possible only for GPU.'''   )

								# amp seems to have memory leaks so that memory usage
								# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
								model.half()

					def compute_loss_and_backprob_encoder():
								__lowercase	:       str				=   train_model(UpperCamelCase_							,		labels=UpperCamelCase_   )[0]
								loss.backward()
								return loss

					def compute_loss_and_backprob_encoder_decoder():
								__lowercase	:       List[Any]				=   train_model(UpperCamelCase_							,		decoder_input_ids=UpperCamelCase_							,		labels=UpperCamelCase_   )[0]
								loss.backward()
								return loss

					__lowercase	:       Optional[Any]				=   (
					    compute_loss_and_backprob_encoder_decoder
					    if config.is_encoder_decoder
					    else compute_loss_and_backprob_encoder
					)
					return _train
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   float:
					try:
								if self.args.is_tpu or self.args.torchscript:
											# run additional 10 times to stabilize compilation for tpu and torchscript
											logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''   )
											timeit.repeat(
											    UpperCamelCase_							,		repeat=1							,		number=5							,		)

								# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
								__lowercase	:       List[str]				=   timeit.repeat(
								    UpperCamelCase_							,		repeat=self.args.repeat							,		number=10							,		)

								if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
											import torch_xla.debug.metrics as met

											self.print_fn(met.metrics_report()   )

								return min(UpperCamelCase_   ) / 1_0.0
					except RuntimeError as e:
								self.print_fn(F"""Doesn't fit on GPU. {e}"""   )
								return "N/A"
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   [Memory, MemorySummary]:
					try:
								if self.args.trace_memory_line_by_line:
											__lowercase	:       List[Any]				=   start_memory_tracing('''transformers'''   )

								if self.args.is_tpu:
											# tpu
											raise NotImplementedError(
											    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
											    ''' `--no-memory` or `args.memory=False`'''   )
								elif self.args.is_gpu:
											if not is_pyanvml_available():
														logger.warning(
														    '''py3nvml not installed, we won\'t log GPU memory usage. '''
														    '''Install py3nvml (pip install py3nvml) to log information about GPU.'''   )
														__lowercase	:       List[str]				=   '''N/A'''
											else:
														logger.info(
														    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
														    ''' on the same GPU.'''   )
														# init nvml
														nvml.nvmlInit()
														func()
														__lowercase	:       Optional[int]				=   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
														__lowercase	:       Any				=   nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase_   )
														__lowercase	:       Any				=   meminfo.used
														__lowercase	:       str				=   Memory(UpperCamelCase_   )
														# shutdown nvml
														nvml.nvmlShutdown()
								else:
											# cpu
											__lowercase	:       Optional[Any]				=   measure_peak_memory_cpu(UpperCamelCase_   )
											__lowercase	:       List[str]				=   Memory(UpperCamelCase_   ) if isinstance(UpperCamelCase_							,		UpperCamelCase_   ) else memory_bytes

								if self.args.trace_memory_line_by_line:
											__lowercase	:       Any				=   stop_memory_tracing(UpperCamelCase_   )
								else:
											__lowercase	:       int				=   None

								return memory, summary
					except RuntimeError as e:
								self.print_fn(F"""Doesn't fit on GPU. {e}"""   )
								return "N/A", None









"""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)



