'''simple docstring'''


from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	)			->list[float]:
  lowercase_     ,	lowercase_     =       coefficient_matrix.shape
  lowercase_     ,	lowercase_     =       constant_matrix.shape

  if rowsa != colsa:
    lowercase_     =       f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
    raise ValueError(SCREAMING_SNAKE_CASE_						)

  if colsa != 1:
    lowercase_     =       f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
    raise ValueError(SCREAMING_SNAKE_CASE_						)

  if rowsa != rowsa:
    lowercase_     =       (
        """Coefficient and constant matrices dimensions must be nxn and nx1 but """
        f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
    )
    raise ValueError(SCREAMING_SNAKE_CASE_						)

  if len(SCREAMING_SNAKE_CASE_						) != rowsa:
    lowercase_     =       (
        """Number of initial values must be equal to number of rows in coefficient """
        f"""matrix but received {len(SCREAMING_SNAKE_CASE_						)} and {rowsa}"""
    )
    raise ValueError(SCREAMING_SNAKE_CASE_						)

  if iterations <= 0:
    raise ValueError("""Iterations must be at least 1"""						)

  lowercase_     =       np.concatenate(
      (coefficient_matrix, constant_matrix) ,	axis=1						)

  lowercase_     ,	lowercase_     =       table.shape

  strictly_diagonally_dominant(SCREAMING_SNAKE_CASE_						)

  # Iterates the whole matrix for given number of times
  for _ in range(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       []
    for row in range(SCREAMING_SNAKE_CASE_						):
      lowercase_     =       0
      for col in range(SCREAMING_SNAKE_CASE_						):
        if col == row:
          lowercase_     =       table[row][col]
        elif col == cols - 1:
          lowercase_     =       table[row][col]
        else:
          temp += (-1) * table[row][col] * init_val[col]
      lowercase_     =       (temp + val) / denom
      new_val.append(SCREAMING_SNAKE_CASE_						)
    lowercase_     =       new_val

  return [float(SCREAMING_SNAKE_CASE_						) for i in new_val]

def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  lowercase_     ,	lowercase_     =       table.shape

  lowercase_     =       True

  for i in range(0 ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       0
    for j in range(0 ,	cols - 1						):
      if i == j:
        continue
      else:
        total += table[i][j]

    if table[i][i] <= total:
      raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""						)

  return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
       import doctest

       doctest.testmod()


'''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_      (       SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None						)			->str:
  return field(default_factory=lambda: default ,	metadata=SCREAMING_SNAKE_CASE_						)





@dataclass
class       _a    :


  """simple docstring"""

  A_    							=						field(
      metadata={'''help''': '''The csv file to plot.'''} ,				)
  A_    							=						field(
      default=__a ,				metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''} ,				)
  A_    							=						field(
      default=__a ,				metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''} ,				)
  A_    							=						field(
      default=__a ,				metadata={'''help''': '''Disable logarithmic scale when plotting'''} ,				)
  A_    							=						field(
      default=__a ,				metadata={
          '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
      } ,				)
  A_    							=						field(
      default=__a ,				metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''} ,				)
  A_    							=						list_field(
      default=__a ,				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(SCREAMING_SNAKE_CASE_						)
    return True
  except ValueError:
    return False

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  try:
    float(SCREAMING_SNAKE_CASE_						)
    return True
  except ValueError:
    return False





class       _a    :


  """simple docstring"""

  def __init__(		self      :       str   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


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

    with open(self.args.csv_file   ,				newline=""""""      ) as csv_file:
      lowercase_     =       csv.DictReader(lowercase_      )
      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 		lowerCamelCase__  (		self      :       str      ):







    '''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=lowercase_   ,				)
        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(lowercase_   ,				lowercase_      )[: len(lowercase_      )]
        plt.scatter(
            lowercase_   ,				lowercase_   ,				label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""      )
        plt.plot(lowercase_   ,				lowercase_   ,				"""--"""      )

      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(lowercase_      )
    plt.xlabel(lowercase_      )
    plt.ylabel(lowercase_      )
    plt.legend()

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






def   A_      (       )			->Tuple:
  lowercase_     =       HfArgumentParser(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       parser.parse_args_into_dataclasses()[0]
  lowercase_     =       Plot(args=SCREAMING_SNAKE_CASE_						)
  plot.plot()


if __name__ == "__main__":
       main()

