



'''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					:						Dict=None							,		_SCREAMING_SNAKE_CASE					:						Optional[int]=None							)      ->						Any:







	"""simple docstring"""
	return field(default_factory=lambda: default							,		metadata=_SCREAMING_SNAKE_CASE							)

@dataclass
class __snake_case:


						'''simple docstring'''



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







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							)      ->						int:







	"""simple docstring"""
	try:
		int(_SCREAMING_SNAKE_CASE							)
		return True
	except ValueError:
		return False






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							)      ->						Optional[Any]:







	"""simple docstring"""
	try:
		float(_SCREAMING_SNAKE_CASE							)
		return True
	except ValueError:
		return False

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_				)     ->     Union[str, Any]:
							lowerCAmelCase   =     args
							lowerCAmelCase   =     defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

							with open(self.args.csv_file     ,	newline=""""""				) as csv_file:
								lowerCAmelCase   =     csv.DictReader(A_				)
								for row in reader:
									lowerCAmelCase   =     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
										lowerCAmelCase   =     int(row["""result"""]				)
									elif can_convert_to_float(row["""result"""]				):
										# value is not None
										lowerCAmelCase   =     float(row["""result"""]				)




						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase,       lowerCAmelCase   =     plt.subplots()
							lowerCAmelCase   =     """Time usage""" if self.args.is_time else """Memory usage"""
							lowerCAmelCase   =     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()				):
								lowerCAmelCase   =     sorted(set(self.result_dict[model_name]["""bsz"""]				)				)
								lowerCAmelCase   =     sorted(set(self.result_dict[model_name]["""seq_len"""]				)				)
								lowerCAmelCase   =     self.result_dict[model_name]["""result"""]

								((lowerCAmelCase),       (lowerCAmelCase))   =     (
								    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
								)

								lowerCAmelCase   =     (
								    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:
										lowerCAmelCase   =     np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,	dtype=A_     ,	)
									else:
										lowerCAmelCase   =     np.asarray(
										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]     ,	dtype=np.floataa     ,	)

									((lowerCAmelCase),       (lowerCAmelCase))   =     (
									    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
									)

									lowerCAmelCase   =     np.asarray(A_     ,	A_				)[: len(A_				)]
									plt.scatter(
									    A_     ,	A_     ,	label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'				)
									plt.plot(A_     ,	A_     ,	"""--"""				)

								title_str += f' {label_model_name} vs.'

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

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

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







def 		_snake_case		( )      ->						Optional[int]:







	"""simple docstring"""
	lowerCAmelCase   =     HfArgumentParser(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     parser.parse_args_into_dataclasses()[0]
	lowerCAmelCase   =     Plot(args=_SCREAMING_SNAKE_CASE							)
	plot.plot()


if __name__ == "__main__":
				main()




'''simple docstring'''

import cmath
import math






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							)      ->						complex:







	"""simple docstring"""
	lowerCAmelCase   =     math.radians(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     math.radians(_SCREAMING_SNAKE_CASE							)

	# Convert voltage and current to rectangular form
	lowerCAmelCase   =     cmath.rect(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     cmath.rect(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()