'''simple docstring'''


from hashlib import mda, shaaaa

snake_case__	:     Union[str, Any]      =    (shaaaa, mda)






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ = 8		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = 0B0
       UpperCAmelCase_				:  Optional[Any]  = size


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = self.hash_(__lowerCAmelCase		)
       self.bitarray |= h


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.hash_(__lowerCAmelCase		)
       return (h & self.bitarray) == h


  def __contains__(					self ,						snake_case_		):




       '''simple docstring'''

       return self.exists(__lowerCAmelCase		)


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = bin(__lowerCAmelCase		)[2:]
       return res.zfill(self.size		)


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return self.format_bin(self.bitarray		)


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = 0B0
       for func in HASH_FUNCTIONS:
            UpperCAmelCase_				:  Optional[int]  = (
                int.from_bytes(func(value.encode()		).digest() ,						'little'		) % self.size
            )
            res |= 2**position
       return res


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       return self.format_bin(self.hash_(__lowerCAmelCase		)		)






  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = bin(self.bitarray		).count('1'		)
       return (n_ones / self.size) ** len(__lowerCAmelCase		)







'''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      :    Optional[Any]=None			,		__lowerCamelCase      :    List[str]=None					)						->				Any:
      '''simple docstring'''


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





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




def __a			(						__lowerCamelCase      :    int					)						->				Tuple:
      '''simple docstring'''


      try:
            int(__lowerCamelCase					)
            return True
      except ValueError:
            return False


def __a			(						__lowerCamelCase      :    Union[str, Any]					)						->				str:
      '''simple docstring'''


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





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Tuple)   ->       Dict:
         lowercase_     			=   args
         lowercase_     			=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

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


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Tuple:
         lowercase_			,				lowercase_     			=   plt.subplots()
         lowercase_     			=   "Time usage" if self.args.is_time else "Memory usage"
         lowercase_     			=   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()):
               lowercase_     			=   sorted(set(self.result_dict[model_name]["bsz"]))
               lowercase_     			=   sorted(set(self.result_dict[model_name]["seq_len"]))
               lowercase_     			=   self.result_dict[model_name]["result"]

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

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

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

                     lowercase_     			=   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.'

         lowercase_     			=   title_str[:-4]
         lowercase_     			=   "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 __a			(						)						->				Tuple:
      '''simple docstring'''


      lowercase_     			=   HfArgumentParser(__lowerCamelCase					)
      lowercase_     			=   parser.parse_args_into_dataclasses()[0]
      lowercase_     			=   Plot(args=__lowerCamelCase					)
      plot.plot()


if __name__ == "__main__":
       main()






