


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 			__lowercase	(						_UpperCamelCase=None,    _UpperCamelCase=None     )    ->Union[str, Any]:







						"""simple docstring"""

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







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





def 			__lowercase	(						_UpperCamelCase     )    ->Optional[Any]:







						"""simple docstring"""

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




def 			__lowercase	(						_UpperCamelCase     )    ->Union[str, Any]:







						"""simple docstring"""

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







class 				__SCREAMING_SNAKE_CASE   :


						def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
												lowercase			:   Dict					   =		args
												lowercase			:   List[Any]					   =		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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






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

																		(lowercase)			:   Dict					   =		(
																		    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																		)

																		lowercase			:   Union[str, 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:
																														lowercase			:   Tuple					   =		np.asarray(
																														    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,						dtype=SCREAMING_SNAKE_CASE__							,						)
																								else:
																														lowercase			:   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							,						)

																								(lowercase)			:   Optional[Any]					   =		(
																								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																								)

																								lowercase			:   List[Any]					   =		np.asarray(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)[: len(SCREAMING_SNAKE_CASE__		)]
																								plt.scatter(
																								    SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""		)
																								plt.plot(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						'''--'''		)

																		title_str += f""" {label_model_name} vs."""

												lowercase			:   Optional[int]					   =		title_str[:-4]
												lowercase			:   List[str]					   =		'Time in s' if self.args.is_time else 'Memory in MB'

												# plot
												plt.title(SCREAMING_SNAKE_CASE__		)
												plt.xlabel(SCREAMING_SNAKE_CASE__		)
												plt.ylabel(SCREAMING_SNAKE_CASE__		)
												plt.legend()

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





def 			__lowercase	(						)    ->Union[str, Any]:







						"""simple docstring"""

						lowercase			:   str					   =		HfArgumentParser(lowerCAmelCase__     )
						lowercase			:   List[str]					   =		parser.parse_args_into_dataclasses()[0]
						lowercase			:   List[Any]					   =		Plot(args=lowerCAmelCase__     )
						plot.plot()


if __name__ == "__main__":
		main()





import numpy as np




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Dict:







      """simple docstring"""

      lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
      lowercase			:   Dict					   =		np.array(_UpperCamelCase     )

      lowercase			:   List[Any]					   =		abs(predict - actual     )
      lowercase			:   int					   =		difference.mean()

      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







      """simple docstring"""

      lowercase			:   Tuple					   =		np.array(_UpperCamelCase     )
      lowercase			:   str					   =		np.array(_UpperCamelCase     )

      lowercase			:   List[str]					   =		predict - actual
      lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )

      lowercase			:   Union[str, Any]					   =		square_diff.mean()
      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->str:







      """simple docstring"""

      lowercase			:   Union[str, Any]					   =		np.array(_UpperCamelCase     )
      lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )

      lowercase			:   Tuple					   =		predict - actual
      lowercase			:   Dict					   =		np.square(_UpperCamelCase     )
      lowercase			:   Union[str, Any]					   =		square_diff.mean()
      lowercase			:   Union[str, Any]					   =		np.sqrt(_UpperCamelCase     )
      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







      """simple docstring"""

      lowercase			:   List[str]					   =		np.array(_UpperCamelCase     )
      lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )

      lowercase			:   Dict					   =		np.log(predict + 1     )
      lowercase			:   List[str]					   =		np.log(actual + 1     )

      lowercase			:   int					   =		log_predict - log_actual
      lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
      lowercase			:   List[Any]					   =		square_diff.mean()

      lowercase			:   int					   =		np.sqrt(_UpperCamelCase     )

      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[str]:







      """simple docstring"""

      lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
      lowercase			:   Optional[int]					   =		np.array(_UpperCamelCase     )

      lowercase			:   Optional[int]					   =		predict - actual
      lowercase			:   Optional[int]					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
      lowercase			:   Dict					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
      # print(numerator, denumerator)
      lowercase			:   int					   =		float(_UpperCamelCase     ) / denumerator * 100

      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







      """simple docstring"""

      return np.mean(np.array(_UpperCamelCase     ) == np.array(_UpperCamelCase     )     )

