





'''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							:      int=None			,     __lowercase							:      int=None       )		->       Optional[int]:


     '''simple docstring'''



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







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





def 	UpperCAmelCase_							(					__lowercase							:      Dict       )		->       List[Any]:


     '''simple docstring'''



     try:
          int(__SCREAMING_SNAKE_CASE       )
          return True
     except ValueError:
          return False
def 	UpperCAmelCase_							(					__lowercase							:      Tuple       )		->       str:


     '''simple docstring'''



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







class   A_	:




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Optional[int]    ):
      _UpperCAmelCase     							=   args
      _UpperCAmelCase     							=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

      with open(self.args.csv_file	,			newline=""    ) as csv_file:
           _UpperCAmelCase     							=   csv.DictReader(__UpperCamelCase    )
           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      lowercase	(					self       :    int    ):
      _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)     							=   (
               (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=__UpperCamelCase	,			)
                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)     							=   (
                    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                )

                _UpperCAmelCase     							=   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.'

      _UpperCAmelCase     							=   title_str[:-4]
      _UpperCAmelCase     							=   '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 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   HfArgumentParser(__SCREAMING_SNAKE_CASE       )
     _UpperCAmelCase     							=   parser.parse_args_into_dataclasses()[0]
     _UpperCAmelCase     							=   Plot(args=__SCREAMING_SNAKE_CASE       )
     plot.plot()


if __name__ == "__main__":
  main()





"""simple docstring"""







__SCREAMING_SNAKE_CASE    	={}



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ):

     # if we are absent twice, or late 3 consecutive days,
     # no further prize strings are possible
     if late == 3 or absent == 2:
          return 0

     # if we have no days left, and have not failed any other rules,
     # we have a prize string
     if days == 0:
          return 1

     # No easy solution, so now we need to do the recursive calculation

     # First, check if the combination is already in the cache, and
     # if yes, return the stored value from there since we already
     # know the number of possible prize strings from this point on
     lowercase_	:				Any													=	(days, absent, late)
     if key in cache:
          return cache[key]

     # now we calculate the three possible ways that can unfold from
     # this point on, depending on our attendance today

     # 1) if we are late (but not absent), the "absent" counter stays as
     # it is, but the "late" counter increases by one
     lowercase_	:				Optional[int]													=	_calculate(days - 1     ,			__SCREAMING_SNAKE_CASE     ,			late + 1      )

     # 2) if we are absent, the "absent" counter increases by 1, and the
     # "late" counter resets to 0
     lowercase_	:				Any													=	_calculate(days - 1     ,			absent + 1     ,			0      )

     # 3) if we are on time, this resets the "late" counter and keeps the
     # absent counter
     lowercase_	:				Dict													=	_calculate(days - 1     ,			__SCREAMING_SNAKE_CASE     ,			0      )

     lowercase_	:				str													=	state_late + state_absent + state_ontime

     lowercase_	:				Tuple													=	prizestrings
     return prizestrings



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int = 30      ):
     return _calculate(__SCREAMING_SNAKE_CASE     ,			absent=0     ,			late=0      )


if __name__ == "__main__":
       print(solution())

