
"""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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=None      ):
     return field(default_factory=lambda: default,					metadata=_a      )




@dataclass
class 					UpperCamelCase     :

    """simple docstring"""



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






def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
     try:
          int(_a      )
          return True
     except ValueError:
          return False

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
     try:
          float(_a      )
          return True
     except ValueError:
          return False




class 					UpperCamelCase     :

    """simple docstring"""



    def __init__(    self		,     lowercase__      )   ->   Dict:
         """simple docstring"""







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

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




    def 							A (    self      )   ->   List[str]:
         """simple docstring"""







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

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

              SCREAMING_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:
                        SCREAMING_SNAKE_CASE           =     np.asarray(
                            [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,     dtype=lowercase__		,     )
                   else:
                        SCREAMING_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		,     )

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

                   SCREAMING_SNAKE_CASE           =     np.asarray(lowercase__		,     lowercase__      )[: len(lowercase__      )]
                   plt.scatter(
                       lowercase__		,     lowercase__		,     label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''      )
                   plt.plot(lowercase__		,     lowercase__		,     '--'      )

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

         SCREAMING_SNAKE_CASE           =     title_str[:-4]
         SCREAMING_SNAKE_CASE           =     "Time in s" if self.args.is_time else "Memory in MB"

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

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






def   UpperCamelCase_  (		):
     SCREAMING_SNAKE_CASE           =     HfArgumentParser(_a      )
     SCREAMING_SNAKE_CASE           =     parser.parse_args_into_dataclasses()[0]
     SCREAMING_SNAKE_CASE           =     Plot(args=_a      )
     plot.plot()


if __name__ == "__main__":
 main()


"""simple docstring"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case			       =		TypeVar('T', bound='TaskTemplate')




@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     (	abc.ABC				):

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				ClassVar[Features]
				UpperCAmelCase_    :				ClassVar[Features]


				def 							A (    self		,     lowercase__      )   ->   T:
									"""simple docstring"""







									return copy.deepcopy(self      )


				@property
				def 							A (    self      )   ->   Features:
									"""simple docstring"""







									return Features(**self.input_schema		,     **self.label_schema      )


				@property
				@abc.abstractmethod
				def 							A (    self      )   ->   Dict[str, str]:
									"""simple docstring"""







									raise NotImplementedError




				@classmethod
				def 							A (    cls		,     lowercase__      )   ->   T:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     {f.name for f in dataclasses.fields(cls      )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )
