


"""simple docstring"""






from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase  		=			TypeVar("""T""", bound=bool)


class 			SCREAMING_SNAKE_CASE_  (   Generic[T]):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__)   ->	None:







								'''simple docstring'''




								snake_case__  :						T		=      value
								snake_case__  :						SkewNode[T] | None		=      None
								snake_case__  :						SkewNode[T] | None		=      None




							@property
							def        UpperCAmelCase (						self)   ->	T:







								'''simple docstring'''




								return self._value







							@staticmethod
							def        UpperCAmelCase (						lowerCamelCase__	,					lowerCamelCase__)   ->	SkewNode[T] | None:







								'''simple docstring'''




								if not roota:
									return roota

								if not roota:
									return roota

								if roota.value > roota.value:
									snake_case__,					snake_case__  :						Any		=      roota, roota

								snake_case__  :						Optional[int]		=      roota
								snake_case__  :						int		=      roota.right
								snake_case__  :						Union[str, Any]		=      roota.left
								snake_case__  :						List[str]		=      SkewNode.merge(lowerCamelCase__	,					lowerCamelCase__)

								return result


class 			SCREAMING_SNAKE_CASE_  (   Generic[T]):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__ = ())   ->	None:







								'''simple docstring'''




								snake_case__  :						SkewNode[T] | None		=      None
								if data:
									for item in data:
										self.insert(lowerCamelCase__)




							def __bool__(						self)   ->	bool:







								'''simple docstring'''




								return self._root is not None




							def __iter__(						self)   ->	Iterator[T]:







								'''simple docstring'''




								snake_case__  :						list[Any]		=      []
								while self:
									result.append(self.pop())

								# Pushing items back to the heap not to clear it.
								for item in result:
									self.insert(lowerCamelCase__)

								return iter(lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	None:







								'''simple docstring'''




								snake_case__  :						List[str]		=      SkewNode.merge(self._root	,					SkewNode(lowerCamelCase__))




							def        UpperCAmelCase (						self)   ->	T | None:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.top()
								snake_case__  :						List[Any]		=      (
								    SkewNode.merge(self._root.left	,					self._root.right) if self._root else None
								)

								return result




							def        UpperCAmelCase (						self)   ->	T:







								'''simple docstring'''




								if not self._root:
									raise IndexError("Can't get top element for the empty heap.")
								return self._root.value







							def        UpperCAmelCase (						self)   ->	None:







								'''simple docstring'''




								snake_case__  :						Any		=      None


if __name__ == "__main__":
					import doctest

					doctest.testmod()











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    (							UpperCAmelCase_	:      Union[str, Any]=None   ,       UpperCAmelCase_	:      Union[str, Any]=None      )->			Tuple:

       """simple docstring"""




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


@dataclass
class        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					str     					=    field(
         metadata={"help": "The csv file to plot."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Disable logarithmic scale when plotting"}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={
             "help": "Whether the csv file has training results or inference results. Defaults to inference results."
         }      ,		)
     _SCREAMING_SNAKE_CASE      :					Optional[str]     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}      ,		)
     _SCREAMING_SNAKE_CASE      :					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 							lowerCamelCase    (							UpperCAmelCase_	:      Dict      )->			int:

       """simple docstring"""




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





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]      )->			str:

       """simple docstring"""




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


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase	):
            a			   =args
            a			   =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

            with open(self.args.csv_file ,    newline=""""""	) as csv_file:
                   a			   =csv.DictReader(_lowerCAmelCase	)
                   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	):
            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=_lowerCAmelCase ,    )
                          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(_lowerCAmelCase ,    _lowerCAmelCase	)[: len(_lowerCAmelCase	)]
                          plt.scatter(
                              _lowerCAmelCase ,    _lowerCAmelCase ,    label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''	)
                          plt.plot(_lowerCAmelCase ,    _lowerCAmelCase ,    """--"""	)

                   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(_lowerCAmelCase	)
            plt.xlabel(_lowerCAmelCase	)
            plt.ylabel(_lowerCAmelCase	)
            plt.legend()

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





def 							lowerCamelCase    (							)->			Optional[Any]:

       """simple docstring"""




       a			   =HfArgumentParser(UpperCAmelCase_      )
       a			   =parser.parse_args_into_dataclasses()[0]
       a			   =Plot(args=UpperCAmelCase_      )
       plot.plot()


if __name__ == "__main__":
    main()
