




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  snake_case(    __magic_name__=None			,		__magic_name__=None   )			->							List[str]:
     return field(default_factory=lambda: default			,		metadata=__magic_name__   )
@dataclass
class 						_A :
  _UpperCamelCase       :      str			=      field(
      metadata={'''help''': '''The csv file to plot.'''}      ,       )
  _UpperCamelCase       :      bool			=      field(
      default=_lowerCamelCase      ,       metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}      ,       )
  _UpperCamelCase       :      bool			=      field(
      default=_lowerCamelCase      ,       metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}      ,       )
  _UpperCamelCase       :      bool			=      field(
      default=_lowerCamelCase      ,       metadata={'''help''': '''Disable logarithmic scale when plotting'''}      ,       )
  _UpperCamelCase       :      bool			=      field(
      default=_lowerCamelCase      ,       metadata={
          '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
      }      ,       )
  _UpperCamelCase       :      Optional[str]			=      field(
      default=_lowerCamelCase      ,       metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}      ,       )
  _UpperCamelCase       :      Optional[List[str]]			=      list_field(
      default=_lowerCamelCase      ,       metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}				)






def  snake_case(    __magic_name__   )			->							Union[str, Any]:


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






def  snake_case(    __magic_name__   )			->							Tuple:


     try:
          float(__magic_name__   )
          return True
     except ValueError:
          return False
class 						_A :






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






       """simple docstring"""







       lowercase :       Any       =			args
       lowercase :       Optional[int]       =			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

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






  def  __a      (  self		:     Any				)		->   int:






       """simple docstring"""







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

            (lowercase) :       int       =			(
                (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
            )

            lowercase :       Union[str, Any]       =			(
                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 :       Union[str, Any]       =			np.asarray(
                          [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,						dtype=_A				,						)
                 else:
                      lowercase :       str       =			np.asarray(
                          [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,						dtype=np.floataa				,						)

                 (lowercase) :       int       =			(
                     ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                 )

                 lowercase :       int       =			np.asarray(_A				,						_A				)[: len(_A				)]
                 plt.scatter(
                     _A				,						_A				,						label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""				)
                 plt.plot(_A				,						_A				,						'''--'''				)

            title_str += f""" {label_model_name} vs."""

       lowercase :       Any       =			title_str[:-4]
       lowercase :       Dict       =			'''Time in s''' if self.args.is_time else '''Memory in MB'''

       # plot
       plt.title(_A				)
       plt.xlabel(_A				)
       plt.ylabel(_A				)
       plt.legend()

       if self.args.figure_png_file is not None:
            plt.savefig(self.args.figure_png_file				)
       else:
            plt.show()






def  snake_case(    )			->							List[str]:
     lowercase :       List[str]       =			HfArgumentParser(__magic_name__   )
     lowercase :       Tuple       =			parser.parse_args_into_dataclasses()[0]
     lowercase :       List[str]       =			Plot(args=__magic_name__   )
     plot.plot()


if __name__ == "__main__":
 main()





from typing import Union

import fire
import torch
from tqdm import tqdm






def  snake_case(    __magic_name__			,		__magic_name__ = "cpu"			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     lowercase :       int       =			torch.load(__magic_name__			,		map_location=__magic_name__   )
     for k, v in tqdm(state_dict.items()   ):
          if not isinstance(__magic_name__			,		torch.Tensor   ):
               raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''   )
          lowercase :       Any       =			v.half()
     if save_path is None:  # overwrite src_path
          lowercase :       str       =			src_path
     torch.save(__magic_name__			,		__magic_name__   )


if __name__ == "__main__":
 fire.Fire(convert)