
'''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 			_a		(	_SCREAMING_SNAKE_CASE		:   int=None	,			_SCREAMING_SNAKE_CASE		:   str=None  ):
						return field(default_factory=lambda: default	,			metadata=_SCREAMING_SNAKE_CASE  )


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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						try:
												int(_SCREAMING_SNAKE_CASE  )
												return True
						except ValueError:
												return False



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						try:
												float(_SCREAMING_SNAKE_CASE  )
												return True
						except ValueError:
												return False


class 			lowerCAmelCase :




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

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= plt.subplots()
								_SCREAMING_SNAKE_CASE				= "Time usage" if self.args.is_time else "Memory usage"
								_SCREAMING_SNAKE_CASE				= 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				= sorted(set(self.result_dict[model_name]["bsz"]      )      )
														_SCREAMING_SNAKE_CASE				= sorted(set(self.result_dict[model_name]["seq_len"]      )      )
														_SCREAMING_SNAKE_CASE				= self.result_dict[model_name]["result"]

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

														_SCREAMING_SNAKE_CASE				= (
														    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				= np.asarray(
																										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,						dtype=UpperCamelCase					,						)
																				else:
																										_SCREAMING_SNAKE_CASE				= 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))				= (
																				    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
																				)

																				_SCREAMING_SNAKE_CASE				= np.asarray(UpperCamelCase					,						UpperCamelCase      )[: len(UpperCamelCase      )]
																				plt.scatter(
																				    UpperCamelCase					,						UpperCamelCase					,						label=F'{label_model_name} - {inner_loop_label}: {inner_loop_value}'      )
																				plt.plot(UpperCamelCase					,						UpperCamelCase					,						"--"      )

														title_str += F' {label_model_name} vs.'

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

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

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



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= HfArgumentParser(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= parser.parse_args_into_dataclasses()[0]
						_SCREAMING_SNAKE_CASE				= Plot(args=_SCREAMING_SNAKE_CASE  )
						plot.plot()


if __name__ == "__main__":
						main()

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   list[int]	,			_SCREAMING_SNAKE_CASE		:   list[int]  ):

						# Check if the input is valid
						if not len(_SCREAMING_SNAKE_CASE  ) == len(_SCREAMING_SNAKE_CASE  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa

						# Calculate the determinants of the matrices
						_SCREAMING_SNAKE_CASE				= aa * ba - aa * ba
						_SCREAMING_SNAKE_CASE				= ca * ba - ca * ba
						_SCREAMING_SNAKE_CASE				= aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_SCREAMING_SNAKE_CASE				= determinant_x / determinant
																		_SCREAMING_SNAKE_CASE				= determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)