






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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Any=None     ,			__lowercase				:     Tuple=None      )  -> str:





    """simple docstring"""

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


@dataclass
class  __lowercase :







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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Union[str, Any]      )  -> str:





    """simple docstring"""

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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Tuple      )  -> Optional[int]:





    """simple docstring"""

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


class  __lowercase :







     '''simple docstring'''




     def __init__(			self    :     List[str]	,		UpperCamelCase_    :     Optional[Any]			):


         """simple docstring"""



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

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





     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



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

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

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

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

                 __A             =		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."

         __A             =		title_str[:-4]
         __A             =		"""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      _SCREAMING_SNAKE_CASE  (	)  -> Optional[int]:





    """simple docstring"""

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


if __name__ == "__main__":
  main()












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     list[int]      )  -> list[int]:





    """simple docstring"""

    if len(__lowercase      ) > 1:
        __A             =		len(__lowercase      ) // 2  # Finds the middle of the array
        __A             =		arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        __A             =		arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        __A             =		len(__lowercase      )
        __A             =		len(__lowercase      )
        merge(__lowercase      )  # Starts sorting the left.
        merge(__lowercase      )  # Starts sorting the right
        __A             =		0  # Left Counter
        __A             =		0  # Right Counter
        __A             =		0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                __A             =		left_array[left_index]
                left_index += 1
            else:
                __A             =		right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            __A             =		left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            __A             =		right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
  import doctest

  doctest.testmod()




