



"""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     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      PyTorchBenchmarkArguments
					_A		:      PretrainedConfig
					_A		:      str						=					"PyTorch"






					@property
					def        lowerCAmelCase_    (	self:							Any						)       ->     int:
											return torch.__version__






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							str						,					snake_case:							int						,					snake_case:							int						)       ->     float:
											snake_case_			:Tuple						=      self._prepare_inference_func(snake_case						,					snake_case						,					snake_case						)
											return self._measure_speed(_inference						)






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							str						,					snake_case:							int						,					snake_case:							int						)       ->     [Memory, Optional[MemorySummary]]:
											snake_case_			:Union[str, Any]						=      self._prepare_inference_func(snake_case						,					snake_case						,					snake_case						)
											return self._measure_memory(_inference						)






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							str						,					snake_case:							int						,					snake_case:							int						)       ->     float:
											snake_case_			:List[str]						=      self._prepare_train_func(snake_case						,					snake_case						,					snake_case						)
											return self._measure_speed(_train						)






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							str						,					snake_case:							int						,					snake_case:							int						)       ->     [Memory, Optional[MemorySummary]]:
											snake_case_			:List[str]						=      self._prepare_train_func(snake_case						,					snake_case						,					snake_case						)
											return self._measure_memory(_train						)






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							str						,					snake_case:							int						,					snake_case:							int						)       ->     Callable[[], None]:
											snake_case_			:str						=      self.config_dict[model_name]

											if self.args.torchscript:
																	snake_case_			:Dict						=      True

											snake_case_			:List[Any]						=      (
											    hasattr(snake_case						,					"""architectures"""						)
											    and isinstance(config.architectures						,					snake_case						)
											    and len(config.architectures						) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
																	try:
																							snake_case_			:Optional[int]						=      config.architectures[0]
																							snake_case_			:List[str]						=      __import__("""transformers"""						,					fromlist=[model_class]						)
																							snake_case_			:List[Any]						=      getattr(snake_case						,					snake_case						)
																							snake_case_			:Optional[int]						=      model_cls(snake_case						)
																	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:
																	snake_case_			:Tuple						=      MODEL_MAPPING[config.__class__](snake_case						)

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

											# encoder-decoder has vocab size saved differently
											snake_case_			:Tuple						=      config.vocab_size if hasattr(snake_case						,					"""vocab_size"""						) else config.encoder.vocab_size
											snake_case_			:List[str]						=      torch.randint(snake_case						,					(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():
																							snake_case_			:Any						=      torch.jit.trace(snake_case						,					snake_case						)
											else:
																	snake_case_			:Optional[Any]						=      model

											def encoder_decoder_forward():
																	with torch.no_grad():
																							snake_case_			:List[str]						=      inference_model(snake_case						,					decoder_input_ids=snake_case						)
																	return outputs

											def encoder_forward():
																	with torch.no_grad():
																							snake_case_			:Any						=      inference_model(snake_case						)
																	return outputs

											snake_case_			:List[str]						=      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
											return _forward






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							str						,					snake_case:							int						,					snake_case:							int						)       ->     Callable[[], None]:
											snake_case_			:List[Any]						=      self.config_dict[model_name]

											snake_case_			:str						=      (
											    hasattr(snake_case						,					"""architectures"""						)
											    and isinstance(config.architectures						,					snake_case						)
											    and len(config.architectures						) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
																	try:
																							snake_case_			:str						=      config.architectures[0]
																							snake_case_			:Tuple						=      __import__("""transformers"""						,					fromlist=[model_class]						)
																							snake_case_			:Optional[Any]						=      getattr(snake_case						,					snake_case						)
																							snake_case_			:Optional[Any]						=      model_cls(snake_case						)
																	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:
																	snake_case_			:str						=      MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case						)

											if self.args.torchscript:
																	raise NotImplementedError("""Training for torchscript is currently not implemented"""						)
											else:
																	snake_case_			:int						=      model

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

											# encoder-decoder has vocab size saved differently
											snake_case_			:Tuple						=      config.vocab_size if hasattr(snake_case						,					"""vocab_size"""						) else config.encoder.vocab_size
											snake_case_			:Optional[int]						=      torch.randint(snake_case						,					(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():
																	snake_case_			:Any						=      train_model(snake_case						,					labels=snake_case						)[0]
																	loss.backward()
																	return loss

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

											snake_case_			:Optional[int]						=      (
											    compute_loss_and_backprob_encoder_decoder
											    if config.is_encoder_decoder
											    else compute_loss_and_backprob_encoder
											)
											return _train






					def        lowerCAmelCase_    (	self:							str						,					snake_case:							int						)       ->     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(
																							    snake_case						,					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
																	snake_case_			:Any						=      timeit.repeat(
																	    snake_case						,					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(snake_case						) / 1_0.0
											except RuntimeError as e:
																	self.print_fn(f"""Doesn't fit on GPU. {e}"""						)
																	return "N/A"






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							Callable[[], None]						)       ->     [Memory, MemorySummary]:
											try:
																	if self.args.trace_memory_line_by_line:
																							snake_case_			:str						=      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."""						)
																													snake_case_			:Optional[int]						=      """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()
																													snake_case_			:str						=      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx						)
																													snake_case_			:Tuple						=      nvml.nvmlDeviceGetMemoryInfo(snake_case						)
																													snake_case_			:List[Any]						=      meminfo.used
																													snake_case_			:Any						=      Memory(snake_case						)
																													# shutdown nvml
																													nvml.nvmlShutdown()
																	else:
																							# cpu
																							snake_case_			:List[str]						=      measure_peak_memory_cpu(snake_case						)
																							snake_case_			:int						=      Memory(snake_case						) if isinstance(snake_case						,					snake_case						) else memory_bytes

																	if self.args.trace_memory_line_by_line:
																							snake_case_			:Any						=      stop_memory_tracing(snake_case						)
																	else:
																							snake_case_			:Optional[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 math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
__a     =    9.8_06_65
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						if not isinstance(_lowercase, (int, float)     ):
												raise TypeError("""Invalid velocity. Should be a positive number."""     )

						if not isinstance(_lowercase, (int, float)     ):
												raise TypeError("""Invalid angle. Range is 1-90 degrees."""     )

						# Ensure valid angle
						if angle > 90 or angle < 1:
												raise ValueError("""Invalid angle. Range is 1-90 degrees."""     )

						# Ensure valid velocity
						if init_velocity < 0:
												raise ValueError("""Invalid velocity. Should be a positive number."""     )
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						check_args(_lowercase, _lowercase     )
						snake_case_			:str						=      angle_to_radians(2 * angle     )
						return round(init_velocity**2 * sin(_lowercase     ) / g, 2     )
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						check_args(_lowercase, _lowercase     )
						snake_case_			:str						=      angle_to_radians(_lowercase     )
						return round(init_velocity**2 * sin(_lowercase     ) ** 2 / (2 * g), 2     )
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						check_args(_lowercase, _lowercase     )
						snake_case_			:Optional[int]						=      angle_to_radians(_lowercase     )
						return round(2 * init_velocity * sin(_lowercase     ) / g, 2     )
def        A_  (      ):







						'''simple docstring'''


						snake_case_,	snake_case_			:List[str]						=      25, 20
						assert horizontal_distance(_lowercase, _lowercase     ) == 40.97
						assert max_height(_lowercase, _lowercase     ) == 3.73
						assert total_time(_lowercase, _lowercase     ) == 1.74


if __name__ == "__main__":
						from doctest import testmod

						testmod()

						# Get input from user
						__a     =    float(input("Initial Velocity: ").strip())

						# Get input from user
						__a     =    float(input("angle: ").strip())

						# Print results
						print()
						print("Results: ")
						print(F"""Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]""")
						print(F"""Maximum Height: {max_height(init_vel, angle)!s} [m]""")
						print(F"""Total Time: {total_time(init_vel, angle)!s} [s]""")
