






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       _SCREAMING_SNAKE_CASE (							__snake_case=None   ,      __snake_case=None )				->						str:
       return field(default_factory=lambda: default   ,      metadata=__snake_case )
@dataclass
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







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




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




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Tuple:
       try:
              float(__snake_case )
              return True
       except ValueError:
              return False
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]       ,				lowerCamelCase		:			int							)						->	List[str]:


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

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





  def  lowerCamelCase       (      self		:			int							)						->	Tuple:


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

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

                _UpperCAmelCase							=    (
                    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							=    np.asarray(
                                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,				dtype=lowerCamelCase       ,				)
                       else:
                              _UpperCAmelCase							=    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))							=    (
                           ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                       )

                       _UpperCAmelCase							=    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."""

         _UpperCAmelCase							=    title_str[:-4]
         _UpperCAmelCase							=    """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       _SCREAMING_SNAKE_CASE (							)				->						Optional[int]:
       _UpperCAmelCase							=    HfArgumentParser(__snake_case )
       _UpperCAmelCase							=    parser.parse_args_into_dataclasses()[0]
       _UpperCAmelCase							=    Plot(args=__snake_case )
       plot.plot()


if __name__ == "__main__":
  main()







from __future__ import annotations
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			list[list[int]]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    TypeError(
             """Matrices must be formed from a list of zero or more lists containing at """
             """least one and the same number of values, each of which must be of type """
             """int or float."""							)
         if len(lowerCamelCase							) != 0:
                _UpperCAmelCase							=    len(rows[0]							)
                if cols == 0:
                       raise error
                for row in rows:
                       if len(lowerCamelCase							) != cols:
                              raise error
                       for value in row:
                              if not isinstance(lowerCamelCase       ,				(int, float)							):
                                     raise error
                _UpperCAmelCase							=    rows
         else:
                _UpperCAmelCase							=    []


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	list[list[int]]:


         """simple docstring"""
         return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]


  @property
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         return len(self.rows							)


  @property
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         return len(self.rows[0]							)


  @property
  def  lowerCamelCase       (      self		:			Tuple							)						->	tuple[int, int]:


         """simple docstring"""
         return (self.num_rows, self.num_columns)


  @property
  def  lowerCamelCase       (      self		:			Tuple							)						->	bool:


         """simple docstring"""
         return self.order[0] == self.order[1]


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Matrix:


         """simple docstring"""
         _UpperCAmelCase							=    [
             [0 if column_num != row_num else 1 for column_num in range(self.num_rows							)]
             for row_num in range(self.num_rows							)
         ]
         return Matrix(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	int:


         """simple docstring"""
         if not self.is_square:
                return 0
         if self.order == (0, 0):
                return 1
         if self.order == (1, 1):
                return int(self.rows[0][0]							)
         if self.order == (2, 2):
                return int(
                    (self.rows[0][0] * self.rows[1][1])
                    - (self.rows[0][1] * self.rows[1][0])							)
         else:
                return sum(
                    self.rows[0][column] * self.cofactors().rows[0][column]
                    for column in range(self.num_columns							)							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	bool:


         """simple docstring"""
         return bool(self.determinant()							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    [
             [
                 self.rows[other_row][other_column]
                 for other_column in range(self.num_columns							)
                 if other_column != column
             ]
             for other_row in range(self.num_rows							)
             if other_row != row
         ]
         return Matrix(lowerCamelCase							).determinant()


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int							)						->	int:


         """simple docstring"""
         if (row + column) % 2 == 0:
                return self.get_minor(lowerCamelCase       ,				lowerCamelCase							)
         return -1 * self.get_minor(lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			str							)						->	Matrix:


         """simple docstring"""
         return Matrix(
             [
                 [self.get_minor(lowerCamelCase       ,				lowerCamelCase							) for column in range(self.num_columns							)]
                 for row in range(self.num_rows							)
             ]							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Matrix:


         """simple docstring"""
         return Matrix(
             [
                 [
                     self.minors().rows[row][column]
                     if (row + column) % 2 == 0
                     else self.minors().rows[row][column] * -1
                     for column in range(self.minors().num_columns							)
                 ]
                 for row in range(self.minors().num_rows							)
             ]							)


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Matrix:


         """simple docstring"""
         _UpperCAmelCase							=    [
             [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
             for row in range(self.num_rows							)
         ]
         return Matrix(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Matrix:


         """simple docstring"""
         _UpperCAmelCase							=    self.determinant()
         if not determinant:
                raise TypeError("""Only matrices with a non-zero determinant have an inverse"""							)
         return self.adjugate() * (1 / determinant)


  def __repr__(      self		:			List[str]							)						->	str:


         """simple docstring"""
         return str(self.rows							)


  def __str__(      self		:			Optional[Any]							)						->	str:


         """simple docstring"""
         if self.num_rows == 0:
                return "[]"
         if self.num_rows == 1:
                return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     """[""" + """. """.join([str(lowerCamelCase							) for value in row]							) + """.]"""
                     for row in self.rows
                 ]							)
             + "]"
         )


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			list[int]       ,				lowerCamelCase		:			int | None = None							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    TypeError("""Row must be a list containing all ints and/or floats"""							)
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                raise type_error
         for value in row:
                if not isinstance(lowerCamelCase       ,				(int, float)							):
                       raise type_error
         if len(lowerCamelCase							) != self.num_columns:
                raise ValueError(
                    """Row must be equal in length to the other rows in the matrix"""							)
         if position is None:
                self.rows.append(lowerCamelCase							)
         else:
                _UpperCAmelCase							=    self.rows[0:position] + [row] + self.rows[position:]


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			list[int]       ,				lowerCamelCase		:			int | None = None							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    TypeError(
             """Column must be a list containing all ints and/or floats"""							)
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                raise type_error
         for value in column:
                if not isinstance(lowerCamelCase       ,				(int, float)							):
                       raise type_error
         if len(lowerCamelCase							) != self.num_rows:
                raise ValueError(
                    """Column must be equal in length to the other columns in the matrix"""							)
         if position is None:
                _UpperCAmelCase							=    [self.rows[i] + [column[i]] for i in range(self.num_rows							)]
         else:
                _UpperCAmelCase							=    [
                    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                    for i in range(self.num_rows							)
                ]


  def __eq__(      self		:			Dict       ,				lowerCamelCase		:			object							)						->	bool:


         """simple docstring"""
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                return NotImplemented
         return self.rows == other.rows


  def __ne__(      self		:			List[str]       ,				lowerCamelCase		:			object							)						->	bool:


         """simple docstring"""
         return not self == other


  def __neg__(      self		:			Union[str, Any]							)						->	Matrix:


         """simple docstring"""
         return self * -1


  def __add__(      self		:			Any       ,				lowerCamelCase		:			Matrix							)						->	Matrix:


         """simple docstring"""
         if self.order != other.order:
                raise ValueError("""Addition requires matrices of the same order"""							)
         return Matrix(
             [
                 [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns							)]
                 for i in range(self.num_rows							)
             ]							)


  def __sub__(      self		:			Optional[Any]       ,				lowerCamelCase		:			Matrix							)						->	Matrix:


         """simple docstring"""
         if self.order != other.order:
                raise ValueError("""Subtraction requires matrices of the same order"""							)
         return Matrix(
             [
                 [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns							)]
                 for i in range(self.num_rows							)
             ]							)


  def __mul__(      self		:			List[Any]       ,				lowerCamelCase		:			Matrix | int | float							)						->	Matrix:


         """simple docstring"""
         if isinstance(lowerCamelCase       ,				(int, float)							):
                return Matrix(
                    [[int(element * other							) for element in row] for row in self.rows]							)
         elif isinstance(lowerCamelCase       ,				lowerCamelCase							):
                if self.num_columns != other.num_rows:
                       raise ValueError(
                           """The number of columns in the first matrix must """
                           """be equal to the number of rows in the second"""							)
                return Matrix(
                    [
                        [Matrix.dot_product(lowerCamelCase       ,				lowerCamelCase							) for column in other.columns()]
                        for row in self.rows
                    ]							)
         else:
                raise TypeError(
                    """A Matrix can only be multiplied by an int, float, or another matrix"""							)


  def __pow__(      self		:			Any       ,				lowerCamelCase		:			int							)						->	Matrix:


         """simple docstring"""
         if not isinstance(lowerCamelCase       ,				lowerCamelCase							):
                raise TypeError("""A Matrix can only be raised to the power of an int"""							)
         if not self.is_square:
                raise ValueError("""Only square matrices can be raised to a power"""							)
         if other == 0:
                return self.identity()
         if other < 0:
                if self.is_invertable():
                       return self.inverse() ** (-other)
                raise ValueError(
                    """Only invertable matrices can be raised to a negative power"""							)
         _UpperCAmelCase							=    self
         for _ in range(other - 1							):
                result *= self
         return result





  @classmethod
  def  lowerCamelCase       (      cls		:			Tuple       ,				lowerCamelCase		:			list[int]       ,				lowerCamelCase		:			list[int]							)						->	int:


         """simple docstring"""
         return sum(row[i] * column[i] for i in range(len(lowerCamelCase							)							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()