

"""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     __SCREAMING_SNAKE_CASE    ( A_=None		,	A_=None					):
	return field(default_factory=lambda: default		,	metadata=A_					)







@dataclass
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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

def     __SCREAMING_SNAKE_CASE    ( A_					):
	try:
		int(A_					)
		return True
	except ValueError:
		return False

def     __SCREAMING_SNAKE_CASE    ( A_					):
	try:
		float(A_					)
		return True
	except ValueError:
		return False







class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							Tuple  ,lowercase_     :							Optional[int]						):
						lowerCAmelCase__      :		Tuple    							=      args
						lowerCAmelCase__      :		str    							=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

						with open(self.args.csv_file  ,newline=''''''						) as csv_file:
							lowerCAmelCase__      :		List[Any]    							=      csv.DictReader(lowercase_						)
							for row in reader:
								lowerCAmelCase__      :		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
									lowerCAmelCase__      :		Union[str, Any]    							=      int(row['''result''']						)
								elif can_convert_to_float(row['''result''']						):
									# value is not None
									lowerCAmelCase__      :		List[str]    							=      float(row['''result''']						)







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Tuple    							=      plt.subplots()
						lowerCAmelCase__      :		Union[str, Any]    							=      '''Time usage''' if self.args.is_time else '''Memory usage'''
						lowerCAmelCase__      :		Union[str, Any]    							=      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()						):
							lowerCAmelCase__      :		List[str]    							=      sorted(set(self.result_dict[model_name]['''bsz''']						)						)
							lowerCAmelCase__      :		Union[str, Any]    							=      sorted(set(self.result_dict[model_name]['''seq_len''']						)						)
							lowerCAmelCase__      :		List[Any]    							=      self.result_dict[model_name]['''result''']

							((lowerCAmelCase__)							,(lowerCAmelCase__))      :		List[Any]    							=      (
							    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
							)

							lowerCAmelCase__      :		Dict    							=      (
							    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:
									lowerCAmelCase__      :		Union[str, Any]    							=      np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,dtype=lowercase_  ,)
								else:
									lowerCAmelCase__      :		List[str]    							=      np.asarray(
									    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,dtype=np.floataa  ,)

								((lowerCAmelCase__)							,(lowerCAmelCase__))      :		Union[str, Any]    							=      (
								    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
								)

								lowerCAmelCase__      :		str    							=      np.asarray(lowercase_  ,lowercase_						)[: len(lowercase_						)]
								plt.scatter(
								    lowercase_  ,lowercase_  ,label=F'{label_model_name} - {inner_loop_label}: {inner_loop_value}'						)
								plt.plot(lowercase_  ,lowercase_  ,'''--'''						)

							title_str += F' {label_model_name} vs.'

						lowerCAmelCase__      :		Optional[int]    							=      title_str[:-4]
						lowerCAmelCase__      :		Union[str, Any]    							=      '''Time in s''' if self.args.is_time else '''Memory in MB'''

						# plot
						plt.title(lowercase_						)
						plt.xlabel(lowercase_						)
						plt.ylabel(lowercase_						)
						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    ( ):
	lowerCAmelCase__      :		List[str]    							=      HfArgumentParser(A_					)
	lowerCAmelCase__      :		Optional[int]    							=      parser.parse_args_into_dataclasses()[0]
	lowerCAmelCase__      :		Optional[int]    							=      Plot(args=A_					)
	plot.plot()


if __name__ == "__main__":
							main()







"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      analyze_text(A_					)
	lowerCAmelCase__      :		Any    							=      list(''' ''' + ascii_lowercase					)
	# what is our total sum of probabilities.
	lowerCAmelCase__      :		Optional[Any]    							=      sum(single_char_strings.values()					)

	# one length string
	lowerCAmelCase__      :		int    							=      0
	# for each alpha we go in our dict and if it is in it we calculate entropy
	for ch in my_alphas:
		if ch in single_char_strings:
			lowerCAmelCase__      :		Union[str, Any]    							=      single_char_strings[ch]
			lowerCAmelCase__      :		Dict    							=      my_str / all_sum
			my_fir_sum += prob * math.loga(A_					)  # entropy formula.

    # print entropy
	print(f'{round(-1 * my_fir_sum					):.1f}'					)

	# two len string
	lowerCAmelCase__      :		List[Any]    							=      sum(two_char_strings.values()					)
	lowerCAmelCase__      :		Optional[int]    							=      0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			lowerCAmelCase__      :		int    							=      cha + cha
			if sequence in two_char_strings:
				lowerCAmelCase__      :		Optional[Any]    							=      two_char_strings[sequence]
				lowerCAmelCase__      :		int    							=      int(A_					) / all_sum
				my_sec_sum += prob * math.loga(A_					)

    # print second entropy
	print(f'{round(-1 * my_sec_sum					):.1f}'					)

	# print the difference between them
	print(f'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		List[str]    							=      Counter()  # type: ignore
	lowerCAmelCase__      :		Optional[int]    							=      Counter()  # type: ignore
	single_char_strings[text[-1]] += 1

	# first case when we have space at start.
	two_char_strings[" " + text[0]] += 1
	for i in range(0		,	len(A_					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings

def     __SCREAMING_SNAKE_CASE    ( ):
	import doctest

	doctest.testmod()
	# text = (
	#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
	#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
	#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
	#     "beloved evident through no service elderly is. Blind there if every no so "
	#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
	#     "of message cottage windows do besides against uncivil.  Delightful "
	#     "unreserved impossible few estimating men favourable see entreaties. She "
	#     "propriety immediate was improving. He or entrance humoured likewise "
	#     "moderate. Much nor game son say feel. Fat make met can must form into "
	#     "gate. Me we offending prevailed discovery. "
	# )

	# calculate_prob(text)


if __name__ == "__main__":
							main()




