





'''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 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any]=None,      SCREAMING_SNAKE_CASE__   :      List[str]=None )			->						Optional[int]:
						return field(default_factory=lambda: default,      metadata=SCREAMING_SNAKE_CASE__ )


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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Optional[Any]:
						try:
												int(SCREAMING_SNAKE_CASE__ )
												return True
						except ValueError:
												return False





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						int:
						try:
												float(SCREAMING_SNAKE_CASE__ )
												return True
						except ValueError:
												return False


class        __a   :

							def __init__(				self				:  Optional[int]							,  __magic_name__				:  int )       ->							Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	args
													UpperCAmelCase_      :   List[Any]      =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

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



							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[Any]      =	plt.subplots()
													UpperCAmelCase_      :   Dict      =	'''Time usage''' if self.args.is_time else '''Memory usage'''
													UpperCAmelCase_      :   Dict      =	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_      :   List[str]      =	sorted(set(self.result_dict[model_name]['''bsz'''] ) )
																			UpperCAmelCase_      :   int      =	sorted(set(self.result_dict[model_name]['''seq_len'''] ) )
																			UpperCAmelCase_      :   List[str]      =	self.result_dict[model_name]['''result''']

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

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

																									UpperCAmelCase_      :   Tuple      =	np.asarray(__magic_name__							,  __magic_name__ )[: len(__magic_name__ )]
																									plt.scatter(
																									    __magic_name__							,  __magic_name__							,  label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}""" )
																									plt.plot(__magic_name__							,  __magic_name__							,  '''--''' )

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

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

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

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





def 							lowerCamelCase_   (	)			->						str:
						UpperCAmelCase_      :   int      =	HfArgumentParser(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Dict      =	parser.parse_args_into_dataclasses()[0]
						UpperCAmelCase_      :   List[Any]      =	Plot(args=SCREAMING_SNAKE_CASE__ )
						plot.plot()


if __name__ == "__main__":
					main()










from typing import Any

import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_		):
											# Check if RHS is negative
											if np.any(tableau[:, -1]	,	where=tableau[:, -1] < 0		):
															raise ValueError("""RHS must be > 0"""		)

											a__					   =		tableau
											a__   ,      a__					   =		tableau.shape

											# Number of decision variables x1, x2, x3...
											a__					   =		n_vars

											# Number of artificial variables to be minimised
											a__					   =		len(np.where(tableau[self.n_vars : -1] == -1		)[0]		)

											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
											a__					   =		(self.n_art_vars > 0) + 1

											# Number of slack variables added to make inequalities into equalities
											a__					   =		self.n_rows - self.n_stages

											# Objectives for each stage
											a__					   =		["""max"""]

											# In two stage simplex, first minimise then maximise
											if self.n_art_vars:
															self.objectives.append("""min"""		)

											a__					   =		[""""""]

											# Index of current pivot row and column
											a__					   =		None
											a__					   =		None

											# Does objective row only contain (non)-negative values?
											a__					   =		False


							@staticmethod
							def _a   (				*a_		):
											if len(a_		) != 3:
															raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""		)

											if not all(x >= 0 and isinstance(a_	,	a_		) for x in args		):
															raise ValueError("""All arguments must be non-negative integers"""		)

											# decision | slack | artificial
											a__					   =		["""x""", """s""", """a"""]
											a__					   =		[]
											for i in range(3		):
															for j in range(args[i]		):
																			titles.append(string_starts[i] + str(j + 1		)		)
											titles.append("""RHS"""		)
											return titles


							def _a   (				self	,	a_		):
											a__					   =		self.objectives[-1]

											# Find entries of highest magnitude in objective rows
											a__					   =		(objective == """min""") - (objective == """max""")
											a__					   =		np.argmax(sign * tableau[0, : self.n_vars]		)

											# Choice is only valid if below 0 for maximise, and above for minimise
											if sign * self.tableau[0, col_idx] <= 0:
															a__					   =		True
															return 0, 0

											# Pivot row is chosen as having the lowest quotient when elements of
											# the pivot column divide the right-hand side

											# Slice excluding the objective rows
											a__					   =		slice(self.n_stages	,	self.n_rows		)

											# RHS
											a__					   =		tableau[s, -1]

											# Elements of pivot column within slice
											a__					   =		tableau[s, col_idx]

											# Array filled with nans
											a__					   =		np.full(self.n_rows - self.n_stages	,	np.nan		)

											# If element in pivot column is greater than zeron_stages, return
											# quotient or nan otherwise
											a__					   =		np.divide(a_	,	a_	,	out=a_	,	where=divisor > 0		)

											# Arg of minimum quotient excluding the nan values. n_stages is added
											# to compensate for earlier exclusion of objective columns
											a__					   =		np.nanargmin(a_		) + self.n_stages
											return row_idx, col_idx


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		tableau[row_idx].copy()

											a__					   =		piv_row[col_idx]

											# Entry becomes 1
											piv_row *= 1 / piv_val

											# Variable in pivot column becomes basic, ie the only non-zero entry
											for idx, coeff in enumerate(tableau[:, col_idx]		):
															tableau[idx] += -coeff * piv_row
											a__					   =		piv_row
											return tableau


							def _a   (				self	,	a_		):
											self.objectives.pop()

											if not self.objectives:
															return tableau

											# Slice containing ids for artificial columns
											a__					   =		slice(-self.n_art_vars - 1	,	-1		)

											# Delete the artificial variable columns
											a__					   =		np.delete(a_	,	a_	,	axis=1		)

											# Delete the objective row of the first stage
											a__					   =		np.delete(a_	,	0	,	axis=0		)

											a__					   =		1
											self.n_rows -= 1
											a__					   =		0
											a__					   =		False
											return tableau


							def _a   (				self		):
											# Stop simplex algorithm from cycling.
											for _ in range(100		):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																			a__					   =		self.generate_col_titles(
																			    self.n_vars	,	self.n_slack	,	self.n_art_vars		)

																			# Find the values of each variable at optimal solution
																			return self.interpret_tableau(self.tableau	,	self.col_titles		)

															a__   ,      a__					   =		self.find_pivot(self.tableau		)

															# If there are no more negative values in objective row
															if self.stop_iter:
																			# Delete artificial variable columns and rows. Update attributes
																			a__					   =		self.change_stage(self.tableau		)
															else:
																			a__					   =		self.pivot(self.tableau	,	a_	,	a_		)
											return {}







							def _a   (				self	,	a_	,	a_		):
											a__					   =		{"""P""": abs(tableau[0, -1]		)}

											for i in range(self.n_vars		):
															# Gives ids of nonzero entries in the ith column
															a__					   =		np.nonzero(tableau[:, i]		)
															a__					   =		len(nonzero[0]		)

															# First entry in the nonzero ids
															a__					   =		nonzero[0][0]
															a__					   =		tableau[nonzero_rowidx, i]

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																			a__					   =		tableau[nonzero_rowidx, -1]
																			a__					   =		rhs_val

        # Check for basic variables
											for title in col_titles:
															# Don't add RHS or slack variables to output dict
															if title[0] not in "R-s-a":
																			output_dict.setdefault(a_	,	0		)
											return output_dict


if __name__ == "__main__":
	import doctest

	doctest.testmod()

