"""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 			__UpperCAmelCase    (	lowercase=None      ,lowercase=None						):







  """simple docstring"""
  return field(default_factory=lambda: default      ,metadata=lowercase						)


@dataclass
class 				a :
    _snake_case   :     str   							=							field(
        metadata={'help': 'The csv file to plot.'}		,							)
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}		,							)
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}		,							)
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Disable logarithmic scale when plotting'}		,							)
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={
            'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
        }		,							)
    _snake_case   :     Optional[str]   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}		,							)
    _snake_case   :     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 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  try:
    int(lowercase						)
    return True
  except ValueError:
    return False






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  try:
    float(lowercase						)
    return True
  except ValueError:
    return False


class 				a :


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Tuple    ):
      _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		:     Optional[Any]    ):
      _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 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					HfArgumentParser(lowercase						)
  _UpperCAmelCase         =					parser.parse_args_into_dataclasses()[0]
  _UpperCAmelCase         =					Plot(args=lowercase						)
  plot.plot()


if __name__ == "__main__":
    main()

"""simple docstring"""


UpperCAmelCase__     						=  [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # Return True if there is node that has not iterated.
  _UpperCAmelCase         =					[False] * len(lowercase						)
  _UpperCAmelCase         =					[s]
  _UpperCAmelCase         =					True

  while queue:
    _UpperCAmelCase         =					queue.pop(0						)
    for ind in range(len(graph[u]						)						):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase						)
        _UpperCAmelCase         =					True
        _UpperCAmelCase         =					u

  return visited[t]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[-1] * (len(lowercase						))
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					[i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase      ,lowercase      ,lowercase      ,lowercase						):
    _UpperCAmelCase         =					float("""Inf"""						)
    _UpperCAmelCase         =					sink

    while s != source:
      # Find the minimum value in select path
      _UpperCAmelCase         =					min(lowercase      ,graph[parent[s]][s]						)
      _UpperCAmelCase         =					parent[s]

    max_flow += path_flow
    _UpperCAmelCase         =					sink

    while v != source:
      _UpperCAmelCase         =					parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      _UpperCAmelCase         =					parent[v]

  for i in range(len(lowercase						)						):
    for j in range(len(graph[0]						)						):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)						)

  return res


if __name__ == "__main__":
    print(mincut(test_graph, source=0, sink=5))
