

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 	lowerCamelCase__			(   __A						:str=None  ,__A						:Dict=None		):


       """simple docstring"""




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

@dataclass
class 						__snake_case						:
       """simple docstring"""







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







def 	lowerCamelCase__			(   __A						:str		):


       """simple docstring"""




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







def 	lowerCamelCase__			(   __A						:Any		):


       """simple docstring"""




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

class 						__snake_case						:
       """simple docstring"""







       def __init__(		self			,   _UpperCamelCase      )			->       Union[str, Any]:






              """simple docstring"""


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

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






       def  a		(		self      )			->       List[str]:






              """simple docstring"""


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

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

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

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

                            __snake_case					  =       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.'

              __snake_case					  =       title_str[:-4]
              __snake_case					  =       """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 	lowerCamelCase__			(   ):


       """simple docstring"""




       __snake_case					  =       HfArgumentParser(__A		)
       __snake_case					  =       parser.parse_args_into_dataclasses()[0]
       __snake_case					  =       Plot(args=__A		)
       plot.plot()


if __name__ == "__main__":
    main()




from __future__ import annotations

class 						__snake_case						:
       """simple docstring"""







       def __init__(		self			,   _UpperCamelCase      )			->       None:






              """simple docstring"""


              __snake_case					  =       value
              __snake_case					  =       None
              __snake_case					  =       None






class 						__snake_case						:
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       42


       def __init__(		self      )			->       None:






              """simple docstring"""


              __snake_case					  =       0


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase      )			->       None:






              """simple docstring"""


              if node is None:
                     return

              if path_sum == self.target:
                     self.paths += 1

              if node.left:
                     self.depth_first_search(node.left			,   path_sum + node.left.value      )
              if node.right:
                     self.depth_first_search(node.right			,   path_sum + node.right.value      )






       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase = None      )			->       int:






              """simple docstring"""


              if node is None:
                     return 0
              if target is not None:
                     __snake_case					  =       target

              self.depth_first_search(_UpperCamelCase			,   node.value      )
              self.path_sum(node.left      )
              self.path_sum(node.right      )

              return self.paths


if __name__ == "__main__":
    import doctest

    doctest.testmod()

