





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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__=None			,			UpperCamelCase__=None )  ->				str:







			'''simple docstring'''

			return field(default_factory=lambda: default			,			metadata=UpperCamelCase__ )

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





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				int:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

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

class 	A_       :
	def __init__(    self							,					_A		):






				'''simple docstring'''







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

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







	def      _lowercase				(    self		):






				'''simple docstring'''







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

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

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

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

										UpperCAmelCase   		=				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."""

				UpperCAmelCase   		=				title_str[:-4]
				UpperCAmelCase   		=				'''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 						__SCREAMING_SNAKE_CASE  (				)  ->				Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				HfArgumentParser(UpperCamelCase__ )
			UpperCAmelCase   		=				parser.parse_args_into_dataclasses()[0]
			UpperCAmelCase   		=				Plot(args=UpperCamelCase__ )
			plot.plot()


if __name__ == "__main__":
				main()










from __future__ import annotations

from collections.abc import Callable





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ = 100			,			)  ->				float:







			'''simple docstring'''

			UpperCAmelCase   		=				x_start
			UpperCAmelCase   		=				fnc(UpperCamelCase__ )
			UpperCAmelCase   		=				0.0
			for _ in range(UpperCamelCase__ ):
						# Approximates small segments of curve as linear and solve
						# for trapezoidal area
						UpperCAmelCase   		=				(x_end - x_start) / steps + xa
						UpperCAmelCase   		=				fnc(UpperCamelCase__ )
						area += abs(fxa + fxa ) * (xa - xa) / 2
						# Increment step
						UpperCAmelCase   		=				xa
						UpperCAmelCase   		=				fxa
			return area


if __name__ == "__main__":





				def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				str:







							'''simple docstring'''

							return x**3 + x**2

				print("f(x) = x^3 + x^2")
				print("The area between the curve, x = -5, x = 5 and the x axis is:")
				__A				:    List[Any]       =   10
				while i <= 100_000:
								print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
								i *= 10



