






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       (				snake_case_      :							Tuple=None  ,						snake_case_      :							Optional[Any]=None     ):
	return field(default_factory=lambda: default  ,						metadata=snake_case_     )



@dataclass
class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]     ):
	try:
		int(snake_case_     )
		return True
	except ValueError:
		return False







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]     ):
	try:
		float(snake_case_     )
		return True
	except ValueError:
		return False



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						str							,	__A  :						List[str] ):
						snake_case__				:   List[str]     =						args
						snake_case__				:   Any     =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

						with open(self.args.csv_file							,	newline="" ) as csv_file:
							snake_case__				:   Tuple     =						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__				:   Tuple     =						int(row["result"] )
								elif can_convert_to_float(row["result"] ):
									# value is not None
									snake_case__				:   Any     =						float(row["result"] )






					def  _lowercase  (      self  :						Any ):
						snake_case__,		snake_case__				:   Optional[Any]     =						plt.subplots()
						snake_case__				:   Tuple     =						"Time usage" if self.args.is_time else "Memory usage"
						snake_case__				:   str     =						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__				:   Tuple     =						sorted(set(self.result_dict[model_name]["bsz"] ) )
							snake_case__				:   Optional[int]     =						sorted(set(self.result_dict[model_name]["seq_len"] ) )
							snake_case__				:   str     =						self.result_dict[model_name]["result"]

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

							snake_case__				:   Tuple     =						(
							    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__				:   Optional[Any]     =						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__				:   str     =						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__))				:   str     =						(
								    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
								)

								snake_case__				:   Optional[int]     =						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__				:   str     =						title_str[:-4]
						snake_case__				:   Optional[int]     =						"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       (				):
	snake_case__				:   Union[str, Any]     =						HfArgumentParser(snake_case_     )
	snake_case__				:   Dict     =						parser.parse_args_into_dataclasses()[0]
	snake_case__				:   Optional[Any]     =						Plot(args=snake_case_     )
	plot.plot()


if __name__ == "__main__":
	main()











import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							dict     ):
	return (data["data"], data["target"])







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							np.ndarray  ,						snake_case_      :							np.ndarray     ):
	snake_case__				:   Optional[int]     =						XGBClassifier()
	classifier.fit(snake_case_  ,						snake_case_     )
	return classifier







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						load_iris()
	snake_case__,		snake_case__				:   str     =						data_handling(snake_case_     )
	snake_case__,		snake_case__,		snake_case__,		snake_case__				:   int     =						train_test_split(
	    snake_case_  ,						snake_case_  ,						test_size=0.25     )

	snake_case__				:   Dict     =						iris["target_names"]

	# Create an XGBoost Classifier from the training data
	snake_case__				:   Dict     =						xgboost(snake_case_  ,						snake_case_     )

	# Display the confusion matrix of the classifier with both training and test sets
	ConfusionMatrixDisplay.from_estimator(
	    snake_case_  ,						snake_case_  ,						snake_case_  ,						display_labels=snake_case_  ,						cmap="Blues"  ,						normalize="true"  ,						)
	plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
	plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()



