
'''simple docstring'''



import sys





def  _A		(							lowercase__		,		lowercase__	):

						'''simple docstring'''





						if not numbers:
												return 0
						if target == 0 and target in numbers:
												return 0
						if not isinstance(lowercase__		,		(list, tuple)	) or not all(
						    isinstance(lowercase__		,		lowercase__	) for number in numbers	):
												raise ValueError("""numbers must be an iterable of integers"""	)

						lowercase__							 =       lowercase__							 =       lowercase__							 =       0
						lowercase__							 =       sys.maxsize

						while right < len(lowercase__	):
												curr_sum += numbers[right]
												while curr_sum >= target and left <= right:
																		lowercase__							 =       min(lowercase__		,		right - left + 1	)
																		curr_sum -= numbers[left]
																		left += 1
												right += 1

						return 0 if min_len == sys.maxsize else min_len








'''simple docstring'''



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		(							lowercase__=None		,		lowercase__=None	):
						return field(default_factory=lambda: default		,		metadata=lowercase__	)





@dataclass
class 			A							:
			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		(							lowercase__	):
						try:
												int(lowercase__	)
												return True
						except ValueError:
												return False





def  _A		(							lowercase__	):
						try:
												float(lowercase__	)
												return True
						except ValueError:
												return False





class 			A							:



			def __init__(					self       ,     lowerCamelCase__       )			->		int:




									'''simple docstring'''



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

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



			def A__ (					self       )			->		Tuple:




									'''simple docstring'''



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

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

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

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

																					lowercase__							 =       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.'''

									lowercase__							 =       title_str[:-4]
									lowercase__							 =       """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		(							):
						lowercase__							 =       HfArgumentParser(lowercase__	)
						lowercase__							 =       parser.parse_args_into_dataclasses()[0]
						lowercase__							 =       Plot(args=lowercase__	)
						plot.plot()


if __name__ == "__main__":
				main()






