


"""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    :    Dict=None				,      SCREAMING_SNAKE_CASE    :    Optional[int]=None				):


  '''simple docstring'''







  return field(default_factory=lambda: default				,      metadata=SCREAMING_SNAKE_CASE				)


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


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

def 					a__			(				SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  try:
    int(SCREAMING_SNAKE_CASE				)
    return True
  except ValueError:
    return False
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  try:
    float(SCREAMING_SNAKE_CASE				)
    return True
  except ValueError:
    return False


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							args
         lowerCAmelCase     :     Union[str, Any]								=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

         with open(self.args.csv_file		,     newline=""						) as csv_file:
           lowerCAmelCase     :     Union[str, Any]								=							csv.DictReader(snake_case__						)
           for row in reader:
             lowerCAmelCase     :     Dict								=							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
               lowerCAmelCase     :     Union[str, Any]								=							int(row["result"]						)
             elif can_convert_to_float(row["result"]						):
               # value is not None
               lowerCAmelCase     :     Union[str, Any]								=							float(row["result"]						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase     :     int								=							plt.subplots()
         lowerCAmelCase     :     Optional[int]								=							"Time usage" if self.args.is_time else "Memory usage"
         lowerCAmelCase     :     Tuple								=							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()						):
           lowerCAmelCase     :     str								=							sorted(set(self.result_dict[model_name]["bsz"]						)						)
           lowerCAmelCase     :     Union[str, Any]								=							sorted(set(self.result_dict[model_name]["seq_len"]						)						)
           lowerCAmelCase     :     str								=							self.result_dict[model_name]["result"]

           ((lowerCAmelCase)						,  (lowerCAmelCase))     :     Union[str, Any]								=							(
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           lowerCAmelCase     :     str								=							(
               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:
               lowerCAmelCase     :     List[str]								=							np.asarray(
                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,     dtype=snake_case__		,     )
             else:
               lowerCAmelCase     :     Tuple								=							np.asarray(
                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,     dtype=np.floataa		,     )

             ((lowerCAmelCase)						,  (lowerCAmelCase))     :     Dict								=							(
                 ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
             )

             lowerCAmelCase     :     Any								=							np.asarray(snake_case__		,     snake_case__						)[: len(snake_case__						)]
             plt.scatter(
                 snake_case__		,     snake_case__		,     label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""						)
             plt.plot(snake_case__		,     snake_case__		,     "--"						)

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

         lowerCAmelCase     :     str								=							title_str[:-4]
         lowerCAmelCase     :     Optional[int]								=							"Time in s" if self.args.is_time else "Memory in MB"

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

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

def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[Any]								=							HfArgumentParser(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Tuple								=							parser.parse_args_into_dataclasses()[0]
  lowerCAmelCase     :     Tuple								=							Plot(args=SCREAMING_SNAKE_CASE				)
  plot.plot()


if __name__ == "__main__":
     main()









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[str]						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  int )	->						List[Any]:


    """simple docstring"""





    if index == r:
        for j in range(lowercase ):
            print(data[j]						,							end=" " )
        print(" " )
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    snake_case    :      Union[str, Any]      					=    arr[i]
    combination_util(lowercase						,							lowercase						,							lowercase						,							index + 1						,							lowercase						,							i + 1 )
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							i + 1 )
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Any      					=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(lowercase						,							lowercase						,							lowercase						,							0						,							lowercase						,							0 )


if __name__ == "__main__":
  # Driver code to check the function above
  __snake_case   			=    [10, 20, 30, 40, 50]
  print_combination(arr, len(arr), 3)
  # This code is contributed by Ambuj sahu





