'''simple docstring'''


def   A_      (       SCREAMING_SNAKE_CASE_						)			->None:
  lowercase_     =       generate_pascal_triangle(SCREAMING_SNAKE_CASE_						)
  for row_idx in range(SCREAMING_SNAKE_CASE_						):
    # Print left spaces
    for _ in range(num_rows - row_idx - 1						):
      print(end=""" """						)
    # Print row values
    for col_idx in range(row_idx + 1						):
      if col_idx != row_idx:
        print(triangle[row_idx][col_idx] ,	end=""" """						)
      else:
        print(triangle[row_idx][col_idx] ,	end=""""""						)
    print()

def   A_      (       SCREAMING_SNAKE_CASE_						)			->list[list[int]]:

  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    raise TypeError("""The input value of 'num_rows' should be 'int'"""						)

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        """The input value of 'num_rows' should be greater than or equal to 0"""						)

  lowercase_     =       []
  for current_row_idx in range(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       populate_current_row(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    triangle.append(SCREAMING_SNAKE_CASE_						)
  return triangle

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->list[int]:
  lowercase_     =       [-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  lowercase_     ,	lowercase_     =       1, 1
  for current_col_idx in range(1 ,	SCREAMING_SNAKE_CASE_						):
    calculate_current_element(
        SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  return current_row

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	)			->None:
  lowercase_     =       triangle[current_row_idx - 1][current_col_idx - 1]
  lowercase_     =       triangle[current_row_idx - 1][current_col_idx]
  lowercase_     =       above_to_left_elt + above_to_right_elt

def   A_      (       SCREAMING_SNAKE_CASE_						)			->list[list[int]]:

  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    raise TypeError("""The input value of 'num_rows' should be 'int'"""						)

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        """The input value of 'num_rows' should be greater than or equal to 0"""						)

  lowercase_     =       [[1]]

  for row_index in range(1 ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       [0] + result[-1] + [0]
    lowercase_     =       row_index + 1
    # Calculate the number of distinct elements in a row
    lowercase_     =       sum(divmod(SCREAMING_SNAKE_CASE_ ,	2						)						)
    lowercase_     =       [
        temp_row[i - 1] + temp_row[i] for i in range(1 ,	distinct_elements + 1						)
    ]
    lowercase_     =       row_first_half[: (row_index + 1) // 2]
    row_second_half.reverse()
    lowercase_     =       row_first_half + row_second_half
    result.append(SCREAMING_SNAKE_CASE_						)

  return result

def   A_      (       )			->None:
  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) -> None:
    lowercase_     =       f"""{func.__name__}({value})"""
    lowercase_     =       timeit(f"""__main__.{call}""" ,	setup="""import __main__"""						)
    # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
    print(f"""{call:38} -- {timing:.4f} seconds"""						)

  for value in range(15						):  # (1, 7, 14):
    for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
      benchmark_a_function(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    print()


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()


'''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()

