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





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    List[Any]:
  try:
    int(lowerCamelCase__				)
    return True
  except ValueError:
    return False





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Optional[int]:
  try:
    float(lowerCamelCase__				)
    return True
  except ValueError:
    return False
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__				):




       """simple docstring"""


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

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




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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

         ((_SCREAMING_SNAKE_CASE)    ,		(_SCREAMING_SNAKE_CASE))   :				Optional[int]							    = (
             (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
         )

         _SCREAMING_SNAKE_CASE   :				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:
             _SCREAMING_SNAKE_CASE   :				Tuple							    = np.asarray(
                 [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,     dtype=snake_case__  ,     )
           else:
             _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = np.asarray(
                 [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,     dtype=np.floataa  ,     )

           ((_SCREAMING_SNAKE_CASE)    ,		(_SCREAMING_SNAKE_CASE))   :				Optional[int]							    = (
               ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
           )

           _SCREAMING_SNAKE_CASE   :				List[str]							    = np.asarray(snake_case__  ,     snake_case__				)[: len(snake_case__				)]
           plt.scatter(
               snake_case__  ,     snake_case__  ,     label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''				)
           plt.plot(snake_case__  ,     snake_case__  ,     "--"				)

         title_str += F''' {label_model_name} vs.'''

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = title_str[:-4]
       _SCREAMING_SNAKE_CASE   :				str							    = "Time in s" if self.args.is_time else "Memory in MB"

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

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





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = HfArgumentParser(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = parser.parse_args_into_dataclasses()[0]
  _SCREAMING_SNAKE_CASE   :				int							    = Plot(args=lowerCamelCase__				)
  plot.plot()


if __name__ == "__main__":
      main()





"""simple docstring"""
from typing import List

from .keymap import KEYMAP, get_character





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Optional[int]:

  def decorator(lowerCamelCase__							:							int				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += [key]
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator





def    _lowerCAmelCase    (     *lowerCamelCase__							:							List[str]				)  ->    Tuple:

  def decorator(lowerCamelCase__							:							Dict				):
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += keys
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __new__(     cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = super().__new__(cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       if not hasattr(snake_case__  ,     "key_handler"				):
         setattr(snake_case__  ,     "key_handler"  ,     {}				)
       setattr(snake_case__  ,     "handle_input"  ,     KeyHandler.handle_input				)

       for value in attrs.values():
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = getattr(snake_case__  ,     "handle_key"  ,     []				)
         for key in handled_keys:
           _SCREAMING_SNAKE_CASE   :				Tuple							    = value
       return new_cls




     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = get_character()
       if char != KEYMAP["undefined"]:
         _SCREAMING_SNAKE_CASE   :				Dict							    = ord(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = cls.key_handler.get(snake_case__				)
       if handler:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = char
         return handler(cls				)
       else:
         return None





def    _lowerCAmelCase    (     cls							:							List[Any]				)  ->    str:
  return KeyHandler(cls.__name__,						cls.__bases__,						cls.__dict__.copy()				)



