code: 
  
  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        __SCREAMING_SNAKE_CASE    (      a__						:      List[Any]=None ,a__						:      int=None					)				->							str:
  	return field(default_factory=lambda: default ,metadata=a__					)
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			:
  	_lowercase		:      str    				=							field(
  	    metadata={'''help''': '''The csv file to plot.'''}	,			)
  	_lowercase		:      bool    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}	,			)
  	_lowercase		:      bool    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}	,			)
  	_lowercase		:      bool    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Disable logarithmic scale when plotting'''}	,			)
  	_lowercase		:      bool    				=							field(
  	    default=_lowercase	,			metadata={
  	        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
  	    }	,			)
  	_lowercase		:      Optional[str]    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}	,			)
  	_lowercase		:      Optional[List[str]]    				=							list_field(
  	    default=_lowercase	,			metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}  )
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Dict					)				->							Optional[int]:
  	try:
  		int(a__					)
  		return True
  	except ValueError:
  		return False
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Any					)				->							str:
  	try:
  		float(a__					)
  		return True
  	except ValueError:
  		return False
  
  
  
  
  
  
  class     lowerCamelCase_			:
  
  
  
  
  	def __init__(    self	:    int				,			__A	:    int       ):
  		__A   : Optional[Any]								= args
  		__A   : Optional[Any]								= defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )
  
  		with open(self.args.csv_file				,			newline=""""""       ) as csv_file:
  			__A   : Dict								= csv.DictReader(__A       )
  			for row in reader:
  				__A   : Union[str, Any]								= 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   : List[str]								= int(row["""result"""]       )
  				elif can_convert_to_float(row["""result"""]       ):
  					# value is not None
  					__A   : Dict								= float(row["""result"""]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int       ):
  		__A						,							__A   : Optional[Any]								= plt.subplots()
  		__A   : str								= """Time usage""" if self.args.is_time else """Memory usage"""
  		__A   : Optional[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()       ):
  			__A   : Union[str, Any]								= sorted(set(self.result_dict[model_name]["""bsz"""]       )       )
  			__A   : Any								= sorted(set(self.result_dict[model_name]["""seq_len"""]       )       )
  			__A   : Any								= self.result_dict[model_name]["""result"""]
  
  			((__A)						,							(__A))   : Any								= (
  			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
  			)
  
  			__A   : str								= (
  			    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   : 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:
  					__A   : List[Any]								= 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))   : str								= (
  				    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
  				)
  
  				__A   : Any								= 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."""
  
  		__A   : Union[str, Any]								= title_str[:-4]
  		__A   : 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        __SCREAMING_SNAKE_CASE    (      )				->							Dict:
  	__A   : List[Any]								= HfArgumentParser(a__					)
  	__A   : Optional[Any]								= parser.parse_args_into_dataclasses()[0]
  	__A   : Optional[int]								= Plot(args=a__					)
  	plot.plot()
  
  
  if __name__ == "__main__":
  			main()
  
  
  
  
  
  
style_context: 
  
  import math
  
  
  
  
  
  
  class     lowerCamelCase_			:
  
  
  
  
  	def __init__(    self	:    Dict				,			__A	:    int				,			__A	:    int				,			__A	:    int				,			__A	:    Any       ):
  		__A   : Union[str, Any]								= x
  		__A   : Union[str, Any]								= y
  		__A   : Optional[int]								= step_size
  		__A   : Dict								= function_to_optimize
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return self.function(self.x				,			self.y       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int       ):
  		__A   : Dict								= self.step_size
  		return [
  		    SearchProblem(__A				,			__A				,			__A				,			self.function       )
  		    for x, y in (
  		        (self.x - step_size, self.y - step_size),
  		        (self.x - step_size, self.y),
  		        (self.x - step_size, self.y + step_size),
  		        (self.x, self.y - step_size),
  		        (self.x, self.y + step_size),
  		        (self.x + step_size, self.y - step_size),
  		        (self.x + step_size, self.y),
  		        (self.x + step_size, self.y + step_size),
  		    )
  		]
  
  
  
  
  	def __hash__(    self	:    str       ):
  		return hash(str(self       )       )
  
  
  
  
  	def __eq__(    self	:    Union[str, Any]				,			__A	:    Optional[int]       ):
  		if isinstance(__A				,			__A       ):
  			return hash(str(self       )       ) == hash(str(__A       )       )
  		return False
  
  
  
  
  	def __str__(    self	:    List[Any]       ):
  		return F"""x: {self.x} y: {self.y}"""
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      bool = True ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      bool = False ,a__						:      int = 10000 ,)				->							SearchProblem:
  	__A   : int								= search_prob
  	__A   : List[str]								= []  # list to store the current score at each iteration
  	__A   : Optional[Any]								= 0
  	__A   : Any								= False
  	__A   : Dict								= set()
  	while not solution_found and iterations < max_iter:
  		visited.add(a__					)
  		iterations += 1
  		__A   : Union[str, Any]								= current_state.score()
  		scores.append(a__					)
  		__A   : List[Any]								= current_state.get_neighbors()
  		__A   : Tuple								= -math.inf
  		__A   : str								= math.inf
  		__A   : Optional[int]								= None  # to hold the next best neighbor
  		for neighbor in neighbors:
  			if neighbor in visited:
  				continue  # do not want to visit the same state again
  			if (
  			    neighbor.x > max_x
  			    or neighbor.x < min_x
  			    or neighbor.y > max_y
  			    or neighbor.y < min_y
  			):
  				continue  # neighbor outside our bounds
  			__A   : int								= neighbor.score() - current_score
  			if find_max:  # finding max
  				# going to direction with greatest ascent
  				if change > max_change and change > 0:
  					__A   : str								= change
  					__A   : Optional[int]								= neighbor
  			else:  # finding min
  				# to direction with greatest descent
  				if change < min_change and change < 0:
  					__A   : List[Any]								= change
  					__A   : List[str]								= neighbor
  		if next_state is not None:
  			# we found at least one neighbor which improved the current state
  			__A   : Tuple								= next_state
  		else:
  			# since we have no neighbor that improves the solution we stop the search
  			__A   : str								= True
  
  	if visualization:
  		from matplotlib import pyplot as plt
  
  		plt.plot(range(a__					) ,a__					)
  		plt.xlabel("""Iterations"""					)
  		plt.ylabel("""Function values"""					)
  		plt.show()
  
  	return current_state
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Any					)				->							Dict:
  				return (x**2) + (y**2)
  
  			# starting the problem with initial coordinates (3, 4)
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(prob, find_max=False)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  			# starting the problem with initial coordinates (12, 47)
  			UpperCAmelCase_       :				List[Any]		   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(
  			    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  			)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
  			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
  			)
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[str]					)				->							Any:
  				return (3 * x**2) - (6 * y)
  
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Dict		   =      hill_climbing(prob, find_max=True)
  
  
  
  
  
  
  
  			print(
  			    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  
  
  
  
  