





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							(			snake_case=None	,      snake_case=None       )		->						Optional[Any]:
		return field(default_factory=lambda: default	,      metadata=snake_case       )

@dataclass
class 		snake_case_      :







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





def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						List[Any]:



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



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Tuple:



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

class 		snake_case_      :







	'''simple docstring'''




	def __init__(	self       :    str				,			__lowerCamelCase       :    str      )					->   Optional[Any]:

			'''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       UpperCAmelCase (	self       :    int      )					->   Any:

			'''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    SCREAMING_SNAKE_CASE							(			)		->						str:
		__lowercase					=    HfArgumentParser(snake_case       )
		__lowercase					=    parser.parse_args_into_dataclasses()[0]
		__lowercase					=    Plot(args=snake_case       )
		plot.plot()


if __name__ == "__main__":
		main()











SCREAMING_SNAKE_CASE_      :			str		  =      [chr(i) for i in range(32, 126)]
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			List[str]		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Dict		  =      list(reversed(range(len(alphabets))))
SCREAMING_SNAKE_CASE_      :			int		  =      []
SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      0



def    SCREAMING_SNAKE_CASE							(			)		->						int:
		global gear_one_pos
		global gear_two_pos
		global gear_three_pos
		__lowercase					=    gear_one[0]
		gear_one.append(snake_case       )
		del gear_one[0]
		gear_one_pos += 1
		if gear_one_pos % int(len(snake_case       )       ) == 0:
				__lowercase					=    gear_two[0]
				gear_two.append(snake_case       )
				del gear_two[0]
				gear_two_pos += 1
				if gear_two_pos % int(len(snake_case       )       ) == 0:
						__lowercase					=    gear_three[0]
						gear_three.append(snake_case       )
						del gear_three[0]
						gear_three_pos += 1



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    alphabets.index(snake_case       )
		__lowercase					=    gear_one[target]
		__lowercase					=    gear_two[target]
		__lowercase					=    gear_three[target]
		__lowercase					=    reflector[target]
		__lowercase					=    gear_three.index(snake_case       )
		__lowercase					=    gear_two.index(snake_case       )
		__lowercase					=    gear_one.index(snake_case       )
		code.append(alphabets[target]       )
		rotator()


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			Tuple		  =      list(input('''Type your message:\n'''))
		while True:
				try:
						SCREAMING_SNAKE_CASE_      :			List[str]		  =      int(input('''Please set token:(must be only digits)\n'''))
						break
				except Exception as error:
						print(error)
		for _ in range(token):
				rotator()
		for j in decode:
				engine(j)
		print('''\n''' + ''''''.join(code))
		print(
		    F"""\nYour Token is {token} please write it down.\nIf you want to decode """
		    '''this message again you should input same digits as token!'''
		)




