


'''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       (					A=None			,   A=None	)						->   Any:
							return field(default_factory=lambda: default			,   metadata=UpperCamelCase__	)




@dataclass
class 		a__    :





							'''simple docstring'''






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


def  _snake_case       (					A	)						->   Optional[int]:
							try:
														int(UpperCamelCase__	)
														return True
							except ValueError:
														return False


def  _snake_case       (					A	)						->   List[Any]:
							try:
														float(UpperCamelCase__	)
														return True
							except ValueError:
														return False




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							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(__lowerCamelCase       )
																					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   __SCREAMING_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=__lowerCamelCase ,   )
																												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(__lowerCamelCase ,   __lowerCamelCase       )[: len(__lowerCamelCase       )]
																												plt.scatter(
																												    __lowerCamelCase ,   __lowerCamelCase ,   label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""       )
																												plt.plot(__lowerCamelCase ,   __lowerCamelCase ,   '''--'''       )

																					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(__lowerCamelCase       )
														plt.xlabel(__lowerCamelCase       )
														plt.ylabel(__lowerCamelCase       )
														plt.legend()

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


def  _snake_case       (					)						->   Tuple:
							lowerCAmelCase__	       =     HfArgumentParser(UpperCamelCase__	)
							lowerCAmelCase__	       =     parser.parse_args_into_dataclasses()[0]
							lowerCAmelCase__	       =     Plot(args=UpperCamelCase__	)
							plot.plot()


if __name__ == "__main__":
		main()



'''simple docstring'''






from __future__ import annotations

import math
from collections.abc import Callable


def  _snake_case       (					A			,   A			,   A			,   A = 100			,   )						->   float:
							lowerCAmelCase__	       =     x_start
							lowerCAmelCase__	       =     fnc(A	)
							lowerCAmelCase__	       =     0.0

							for _ in range(A	):
														# Approximates curve as a sequence of linear lines and sums their length
														lowerCAmelCase__	       =     (x_end - x_start) / steps + xa
														lowerCAmelCase__	       =     fnc(A	)
														length += math.hypot(xa - xa			,   fxa - fxa	)

														# Increment step
														lowerCAmelCase__	       =     xa
														lowerCAmelCase__	       =     fxa

							return length


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   List[Any]:
									return math.sin(10 * x	)

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		__UpperCAmelCase       =							10
		while i <= 100_000:
				print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
				i *= 10