"""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__    (	lowercase_     :	Dict=None      , lowercase_     :	Optional[Any]=None						)  ->							List[Any]:
   return field(default_factory=lambda: default      , metadata=__lowerCamelCase						)






@dataclass
class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   __UpperCAmelCase	:							str    =    field(
       metadata={"help": "The csv file to plot."}    ,				)
   __UpperCAmelCase	:							bool    =    field(
       default=lowercase__    ,				metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}    ,				)
   __UpperCAmelCase	:							bool    =    field(
       default=lowercase__    ,				metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}    ,				)
   __UpperCAmelCase	:							bool    =    field(
       default=lowercase__    ,				metadata={"help": "Disable logarithmic scale when plotting"}    ,				)
   __UpperCAmelCase	:							bool    =    field(
       default=lowercase__    ,				metadata={
           "help": "Whether the csv file has training results or inference results. Defaults to inference results."
       }    ,				)
   __UpperCAmelCase	:							Optional[str]    =    field(
       default=lowercase__    ,				metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}    ,				)
   __UpperCAmelCase	:							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__    (	lowercase_     :	str						)  ->							Optional[Any]:
   try:
      int(__lowerCamelCase						)
      return True
   except ValueError:
      return False



def       A__    (	lowercase_     :	List[str]						)  ->							Union[str, Any]:
   try:
      float(__lowerCamelCase						)
      return True
   except ValueError:
      return False






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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

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







   def    _A				(	self	):


      '''simple docstring'''

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

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

         __lowerCamelCase    		=					(
             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    		=					np.asarray(
                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,				dtype=lowercase_ ,				)
            else:
               __lowerCamelCase    		=					np.asarray(
                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,				dtype=np.floataa ,				)

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

            __lowerCamelCase    		=					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.'''

      __lowerCamelCase    		=					title_str[:-4]
      __lowerCamelCase    		=					"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__    (	)  ->							Any:
   __lowerCamelCase    		=					HfArgumentParser(__lowerCamelCase						)
   __lowerCamelCase    		=					parser.parse_args_into_dataclasses()[0]
   __lowerCamelCase    		=					Plot(args=__lowerCamelCase						)
   plot.plot()


if __name__ == "__main__":
     main()








"""simple docstring"""





from math import exp







def       __a	(						__lowerCamelCase    ):
   return 8 * x - 2 * exp(-x    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Any     	=       lower_bound
   UpperCAmelCase_   :   int     	=       upper_bound
   for _ in range(0,						__lowerCamelCase    ):
      UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       xa, xa - (f(__lowerCamelCase    ) * (xa - xa)) / (f(__lowerCamelCase    ) - f(__lowerCamelCase    ))
   return xa


if __name__ == "__main__":
    print(f"""Example: {secant_method(1, 3, 2)}""")


