

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()








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()}"""
			)





