






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__    (	A=None		,					A=None       ):
   return field(default_factory=lambda: default		,					metadata=A       )
@dataclass
class 							__snake_case:
 lowercase			:							str		   =		field(
     metadata={'''help''': '''The csv file to plot.'''}  ,							)
 lowercase			:							bool		   =		field(
     default=__UpperCamelCase  ,							metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}  ,							)
 lowercase			:							bool		   =		field(
     default=__UpperCamelCase  ,							metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}  ,							)
 lowercase			:							bool		   =		field(
     default=__UpperCamelCase  ,							metadata={'''help''': '''Disable logarithmic scale when plotting'''}  ,							)
 lowercase			:							bool		   =		field(
     default=__UpperCamelCase  ,							metadata={
         '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
     }  ,							)
 lowercase			:							Optional[str]		   =		field(
     default=__UpperCamelCase  ,							metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}  ,							)
 lowercase			:							Optional[List[str]]		   =		list_field(
     default=__UpperCamelCase  ,							metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}   )
def    a__    (	A       ):
   try:
      int(A       )
      return True
   except ValueError:
      return False
def    a__    (	A       ):
   try:
      float(A       )
      return True
   except ValueError:
      return False
class 							__snake_case:



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

    """simple docstring"""






    lowercase__    :						List[Any]       =			args
    lowercase__    :						Union[str, Any]       =			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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







 def     UpperCAmelCase     (						self : Optional[int]						):

    """simple docstring"""






    lowercase__  ,       lowercase__    :						Tuple       =			plt.subplots()
    lowercase__    :						List[Any]       =			"""Time usage""" if self.args.is_time else """Memory usage"""
    lowercase__    :						List[str]       =			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__    :						str       =			sorted(set(self.result_dict[model_name]["""bsz"""]						)						)
       lowercase__    :						Optional[int]       =			sorted(set(self.result_dict[model_name]["""seq_len"""]						)						)
       lowercase__    :						Dict       =			self.result_dict[model_name]["""result"""]

       ((lowercase__)  ,       (lowercase__))    :						Tuple       =			(
           (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
       )

       lowercase__    :						int       =			(
           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__    :						Any       =			np.asarray(
                 [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]			,		dtype=UpperCAmelCase__			,		)
          else:
             lowercase__    :						Tuple       =			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__))    :						Optional[Any]       =			(
              ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
          )

          lowercase__    :						List[str]       =			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.'''

    lowercase__    :						Optional[int]       =			title_str[:-4]
    lowercase__    :						List[Any]       =			"""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__    (	):
   lowercase__    :						Optional[int]       =			HfArgumentParser(A       )
   lowercase__    :						List[str]       =			parser.parse_args_into_dataclasses()[0]
   lowercase__    :						List[Any]       =			Plot(args=A       )
   plot.plot()


if __name__ == "__main__":
 main()










from __future__ import annotations

from typing import Any
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : Any			,		UpperCAmelCase__ : Any						):  # noqa: A002

    """simple docstring"""






    lowercase__    :						Optional[int]       =			item
    lowercase__    :						Union[str, Any]       =			next
class 							__snake_case:



 def __init__(						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						Node | None       =			None
    lowercase__    :						Optional[Any]       =			0



 def     UpperCAmelCase     (						self : List[Any]			,		UpperCAmelCase__ : Any						):

    """simple docstring"""






    lowercase__    :						Any       =			Node(UpperCAmelCase__			,		self.head						)
    self.size += 1



 def     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






    if self.head is None:
       return None
    else:
       lowercase__    :						List[Any]       =			self.head.item
       lowercase__    :						Optional[Any]       =			self.head.next
       self.size -= 1
       return item



 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    return self.head is None



 def __str__(						self : List[str]						):

    """simple docstring"""






    if self.is_empty():
       return ""
    else:
       lowercase__    :						Optional[Any]       =			self.head
       lowercase__    :						str       =			""""""
       lowercase__    :						list[str]       =			[]
       while iterate:
          item_list.append(str(iterate.item						)						)
          lowercase__    :						List[str]       =			iterate.next

       lowercase__    :						List[str]       =			""" --> """.join(UpperCAmelCase__						)

       return item_str







 def __len__(						self : List[Any]						):

    """simple docstring"""






    return self.size


