code: 
  '''simple docstring'''
  
  
  
  import enum
  import inspect
  import warnings
  from functools import wraps
  from typing import Callable, Optional
  
  from .logging import get_logger
  
  
  __A          =    set()
  __A          =    get_logger(__name__)
  
  
  
  
  
  def  _A		(							lowercase__ = None	):
  
  						'''simple docstring'''
  
  
  
  
  
  						def decorator(lowercase__	):
  												global _emitted_deprecation_warnings
  
  												if inspect.isclass(lowercase__	):
  																		lowercase__							 =       deprecated_class_or_function.__init__
  																		lowercase__							 =       deprecated_class_or_function.__name__
  												else:
  																		lowercase__							 =       deprecated_class_or_function
  																		lowercase__							 =       deprecated_function.__name__
  																		# Support deprecating __init__ class method: class name instead
  																		lowercase__							 =       name if name != '''__init__''' else deprecated_function.__qualname__.split("""."""	)[-2]
  
  												lowercase__							 =       (
  												    f'''{name} is deprecated and will be removed in the next major version of datasets.''' + f''' {help_message}'''
  												    if help_message
  												    else ''''''
  												)
  
  												@wraps(lowercase__	)
  												def wrapper(*lowercase__		,		**lowercase__	):
  																		lowercase__							 =       hash(lowercase__	)
  																		if func_hash not in _emitted_deprecation_warnings:
  																								warnings.warn(lowercase__		,		category=lowercase__		,		stacklevel=2	)
  																								_emitted_deprecation_warnings.add(lowercase__	)
  																		return deprecated_function(*lowercase__		,		**lowercase__	)
  
  												lowercase__							 =       '''deprecated'''
  
  												if inspect.isclass(lowercase__	):
  																		lowercase__							 =       wrapper
  																		return deprecated_class_or_function
  												else:
  																		return wrapper
  
  						return decorator
  
  
  
  
  
  class 			A							(       enum.EnumMeta				):
  
  
  
  			def __getattribute__(					cls       ,     lowerCamelCase__       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       super().__getattribute__(_snake_case       )
  									if isinstance(_snake_case       ,     enum.Enum       ) and obj._on_access:
  															obj._on_access()
  									return obj
  
  
  
  			def __getitem__(					cls       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       super().__getitem__(_snake_case       )
  									if member._on_access:
  															member._on_access()
  									return member
  
  
  
  			def __call__(					cls       ,     lowerCamelCase__       ,     lowerCamelCase__=None       ,     *, lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=1       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       super().__call__(_snake_case       ,     _snake_case       ,     module=_snake_case       ,     qualname=_snake_case       ,     type=_snake_case       ,     start=_snake_case       )
  									if isinstance(_snake_case       ,     enum.Enum       ) and obj._on_access:
  															obj._on_access()
  									return obj
  
  
  
  
  
  class 			A							(       enum.Enum		,						metaclass=a_				):
  
  
  
  			def __new__(					cls       ,     lowerCamelCase__       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       object.__new__(cls       )
  									lowercase__							 =       value
  									lowercase__							 =       member.deprecate
  									return member
  
  
  
  			@property
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return ""
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       F''' {self.help_message}''' if self.help_message else ''''''
  									warnings.warn(
  									    F'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
  									    + help_message       ,     _snake_case       ,     stacklevel=3       ,     )
  
  
  
  
  
  
  
style_context: 
  '''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  _A		(							lowercase__=None		,		lowercase__=None	):
  						return field(default_factory=lambda: default		,		metadata=lowercase__	)
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							str       		=  field(
  			    metadata={"""help""": """The csv file to plot."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Disable logarithmic scale when plotting"""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={
  			        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
  			    }		,						)
  			lowerCamelCase							:							Optional[str]       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,						)
  			lowerCamelCase							:							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		(							lowercase__	):
  						try:
  												int(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  def  _A		(							lowercase__	):
  						try:
  												float(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       args
  									lowercase__							 =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )
  
  									with open(self.args.csv_file       ,     newline=""""""       ) as csv_file:
  															lowercase__							 =       csv.DictReader(lowerCamelCase__       )
  															for row in reader:
  																					lowercase__							 =       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__							 =       int(row["""result"""]       )
  																					elif can_convert_to_float(row["""result"""]       ):
  																											# value is not None
  																											lowercase__							 =       float(row["""result"""]       )
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       plt.subplots()
  									lowercase__							 =       """Time usage""" if self.args.is_time else """Memory usage"""
  									lowercase__							 =       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__							 =       sorted(set(self.result_dict[model_name]["""bsz"""]       )       )
  															lowercase__							 =       sorted(set(self.result_dict[model_name]["""seq_len"""]       )       )
  															lowercase__							 =       self.result_dict[model_name]["""result"""]
  
  															((lowercase__) ,						(lowercase__))							 =       (
  															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
  															)
  
  															lowercase__							 =       (
  															    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__							 =       np.asarray(
  																											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,     dtype=lowerCamelCase__       ,     )
  																					else:
  																											lowercase__							 =       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__))							 =       (
  																					    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
  																					)
  
  																					lowercase__							 =       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.'''
  
  									lowercase__							 =       title_str[:-4]
  									lowercase__							 =       """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  _A		(							):
  						lowercase__							 =       HfArgumentParser(lowercase__	)
  						lowercase__							 =       parser.parse_args_into_dataclasses()[0]
  						lowercase__							 =       Plot(args=lowercase__	)
  						plot.plot()
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  