






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	(			a_=None				,a_=None   )	->		List[Any]:
					return field(default_factory=lambda: default				,metadata=a_   )




@dataclass
class 							__A	:





				"""simple docstring"""







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






def 				A	(			a_   )	->		Union[str, Any]:
					try:
										int(a_   )
										return True
					except ValueError:
										return False






def 				A	(			a_   )	->		Any:
					try:
										float(a_   )
										return True
					except ValueError:
										return False




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   List[Any]									=args
									__UpperCamelCase :   Union[str, Any]									=defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

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







				def  __lowercase				(					self       ):




									"""simple docstring"""

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

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

														__UpperCamelCase :   Optional[Any]									=(
														    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 :   str									=np.asarray(
																								    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,    dtype=lowerCamelCase__				,    )
																			else:
																								__UpperCamelCase :   Union[str, Any]									=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)) :   str									=(
																			    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																			)

																			__UpperCamelCase :   Any									=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.'

									__UpperCamelCase :   List[str]									=title_str[:-4]
									__UpperCamelCase :   str									='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 				A	(			)	->		str:
					__UpperCamelCase :   Optional[int]									=HfArgumentParser(a_   )
					__UpperCamelCase :   List[str]									=parser.parse_args_into_dataclasses()[0]
					__UpperCamelCase :   Any									=Plot(args=a_   )
					plot.plot()


if __name__ == "__main__":
							main()









from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_			:Union[str, Any]	    =		load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_			:Optional[Any]	    =		[[1, 2, 1, 4], [2, 3, 4, 5]]

A_			:Union[str, Any]	    =		knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)

