


'''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_					( lowerCamelCase	:     Any=None	, lowerCamelCase	:     int=None				):
   return field(default_factory=lambda: default	, metadata=lowerCamelCase				)

@dataclass
class 		UpperCAmelCase_      :
     lowerCamelCase				:	str		=     field(
         metadata={'''help''': '''The csv file to plot.'''}       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Disable logarithmic scale when plotting'''}       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
         }       ,	)
     lowerCamelCase				:	Optional[str]		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}       ,	)
     lowerCamelCase				:	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_					( lowerCamelCase	:     Union[str, Any]				):
   try:
      int(lowerCamelCase				)
      return True
   except ValueError:
      return False





def 	a_					( lowerCamelCase	:     List[Any]				):
   try:
      float(lowerCamelCase				)
      return True
   except ValueError:
      return False

class 		UpperCAmelCase_      :





     def __init__( self       :      int    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  List[str]:
        lowerCAmelCase     =			args
        lowerCAmelCase     =			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

        with open(self.args.csv_file    ,      newline=''    ) as csv_file:
           lowerCAmelCase     =			csv.DictReader(UpperCAmelCase__    )
           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 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Dict:
        lowerCAmelCase     ,  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)     ,  (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=UpperCAmelCase__    ,      )
              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)     ,  (lowerCAmelCase))     =			(
                  ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
              )

              lowerCAmelCase     =			np.asarray(UpperCAmelCase__    ,      UpperCAmelCase__    )[: len(UpperCAmelCase__    )]
              plt.scatter(
                  UpperCAmelCase__    ,      UpperCAmelCase__    ,      label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''    )
              plt.plot(UpperCAmelCase__    ,      UpperCAmelCase__    ,      '--'    )

           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(UpperCAmelCase__    )
        plt.xlabel(UpperCAmelCase__    )
        plt.ylabel(UpperCAmelCase__    )
        plt.legend()

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





def 	a_					( ):
   lowerCAmelCase     =			HfArgumentParser(lowerCamelCase				)
   lowerCAmelCase     =			parser.parse_args_into_dataclasses()[0]
   lowerCAmelCase     =			Plot(args=lowerCamelCase				)
   plot.plot()


if __name__ == "__main__":
       main()






"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:
   lowercase__:   Optional[Any]					=      0
   lowercase__:   List[Any]					=      len(__UpperCAmelCase ) - 1

   while left <= right:
      # avoid divided by 0 during interpolation
      if sorted_collection[left] == sorted_collection[right]:
         if sorted_collection[left] == item:
            return left
         else:
            return None

      lowercase__:   Tuple					=      left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(__UpperCAmelCase ):
         return None

      lowercase__:   Optional[int]					=      sorted_collection[point]
      if current_item == item:
         return point
      else:
         if point < left:
            lowercase__:   List[Any]					=      left
            lowercase__:   int					=      point
         elif point > right:
            lowercase__:   Dict					=      right
            lowercase__:   List[str]					=      point
         else:
            if item < current_item:
               lowercase__:   int					=      point - 1
            else:
               lowercase__:   int					=      point + 1
   return None



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:

   # avoid divided by 0 during interpolation
   if sorted_collection[left] == sorted_collection[right]:
      if sorted_collection[left] == item:
         return left
      else:
         return None

   lowercase__:   Optional[int]					=      left + ((item - sorted_collection[left]) * (right - left)) // (
       sorted_collection[right] - sorted_collection[left]
   )

   # out of range check
   if point < 0 or point >= len(__UpperCAmelCase ):
      return None

   if sorted_collection[point] == item:
      return point
   elif point < left:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   elif point > right:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   else:
      if sorted_collection[point] > item:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    point - 1 )
      else:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    point + 1       ,    __UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[Any]:
   if collection != sorted(__UpperCAmelCase ):
      raise ValueError('''Collection must be ascending sorted''' )
   return True


if __name__ == "__main__":
       import sys
       __A			   = 0
       if debug == 1:
              __A			   = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
              try:
                     __assert_sorted(collection)
              except ValueError:
                     sys.exit("Sequence must be ascending sorted to apply interpolation search")
              __A			   = 6_7

       __A			   = interpolation_search(collection, target)
       if result is not None:
              print(f'''{target} found at positions: {result}''')
       else:
              print("Not found")
