




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   (   _SCREAMING_SNAKE_CASE=None				,_SCREAMING_SNAKE_CASE=None					)			->				Optional[int]:
							return field(default_factory=lambda: default				,metadata=lowercase__					)




@dataclass
class 	UpperCamelCase__			:

				'''simple docstring'''



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




def   A   (   _SCREAMING_SNAKE_CASE					)			->				Dict:
							try:
														int(lowercase__					)
														return True
							except ValueError:
														return False

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Optional[int]:
							try:
														float(lowercase__					)
														return True
							except ValueError:
														return False




class 	UpperCamelCase__			:

				'''simple docstring'''



				def __init__(					self  ,						UpperCamelCase__  )  ->   int:
											lowerCamelCase     :   List[str]    				=      args
											lowerCamelCase     :   List[Any]    				=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

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







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

																		((lowerCamelCase)				,   (lowerCamelCase))     :   int    				=      (
																		    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																		)

																		lowerCamelCase     :   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:
																																lowerCamelCase     :   Union[str, Any]    				=      np.asarray(
																																    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,						dtype=lowerCamelCase__  ,						)
																									else:
																																lowerCamelCase     :   Any    				=      np.asarray(
																																    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,						dtype=np.floataa  ,						)

																									((lowerCamelCase)				,   (lowerCamelCase))     :   Any    				=      (
																									    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
																									)

																									lowerCamelCase     :   List[str]    				=      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.'''

											lowerCamelCase     :   Tuple    				=      title_str[:-4]
											lowerCamelCase     :   Tuple    				=      "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   (   )			->				List[Any]:
							lowerCamelCase     :   Tuple    				=      HfArgumentParser(lowercase__					)
							lowerCamelCase     :   Optional[Any]    				=      parser.parse_args_into_dataclasses()[0]
							lowerCamelCase     :   Tuple    				=      Plot(args=lowercase__					)
							plot.plot()


if __name__ == "__main__":
							main()












from __future__ import annotations

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:
       assert (
           isinstance(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					) and mask > 0
       ), f'''mask needs to be positive integer, your input {mask}'''
       lowerCamelCase     :   str    				=      []
       lowerCamelCase     :   Any    				=      mask

       while submask:
              all_submasks.append(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Union[str, Any]    				=      (submask - 1) & mask

       return all_submasks


if __name__ == "__main__":
       import doctest

       doctest.testmod()






