





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__(		__lowerCAmelCase=None	,			__lowerCAmelCase=None ):
				return field(default_factory=lambda: default	,			metadata=a__ )


@dataclass
class 			lowercase				:



						"""simple docstring"""

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



def       A__(		__lowerCAmelCase ):






				try:
								int(a__ )
								return True
				except ValueError:
								return False

def       A__(		__lowerCAmelCase ):






				try:
								float(a__ )
								return True
				except ValueError:
								return False


class 			lowercase				:



						"""simple docstring"""

						def __init__(    self			:		Optional[int] ,				lowerCamelCase_			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			str  						=   args
										_snake_case :			Tuple  						=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

										with open(self.args.csv_file ,				newline=''					) as csv_file:
														_snake_case :			Union[str, Any]  						=   csv.DictReader(__A					)
														for row in reader:
																		_snake_case :			List[str]  						=   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
																						_snake_case :			str  						=   int(row['result']					)
																		elif can_convert_to_float(row['result']					):
																						# value is not None
																						_snake_case :			List[Any]  						=   float(row['result']					)







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case						,			_snake_case :			str  						=   plt.subplots()
										_snake_case :			List[Any]  						=   'Time usage' if self.args.is_time else 'Memory usage'
										_snake_case :			Union[str, Any]  						=   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()					):
														_snake_case :			Any  						=   sorted(set(self.result_dict[model_name]['bsz']					)					)
														_snake_case :			Dict  						=   sorted(set(self.result_dict[model_name]['seq_len']					)					)
														_snake_case :			int  						=   self.result_dict[model_name]['result']

														((_snake_case)						,			(_snake_case)) :			List[Any]  						=   (
														    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
														)

														_snake_case :			str  						=   (
														    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:
																						_snake_case :			Dict  						=   np.asarray(
																						    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,				dtype=__A ,				)
																		else:
																						_snake_case :			List[Any]  						=   np.asarray(
																						    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,				dtype=np.floataa ,				)

																		((_snake_case)						,			(_snake_case)) :			Dict  						=   (
																		    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																		)

																		_snake_case :			Any  						=   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.'''

										_snake_case :			List[Any]  						=   title_str[:-4]
										_snake_case :			Optional[Any]  						=   '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       A__(		):
				_snake_case :			List[Any]  						=   HfArgumentParser(a__ )
				_snake_case :			Optional[Any]  						=   parser.parse_args_into_dataclasses()[0]
				_snake_case :			int  						=   Plot(args=a__ )
				plot.plot()


if __name__ == "__main__":
		main()













import math

def       A__(		__lowerCAmelCase ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5	,			int(math.sqrt(__lowerCAmelCase ) + 1 )	,			6 ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def       A__(		__lowerCAmelCase = 1_00_01 ):

				try:
								_snake_case :			int  						=   int(__lowerCAmelCase )
				except (TypeError, ValueError):
								raise TypeError('Parameter nth must be int or castable to int.' ) from None
				if nth <= 0:
								raise ValueError('Parameter nth must be greater than or equal to one.' )
				_snake_case :			list[int]  						=   []
				_snake_case :			List[Any]  						=   2
				while len(__lowerCAmelCase ) < nth:
								if is_prime(__lowerCAmelCase ):
												primes.append(__lowerCAmelCase )
												num += 1
								else:
												num += 1
				return primes[len(__lowerCAmelCase ) - 1]


if __name__ == "__main__":
		print(F'''{solution() = }''')






