


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   lowerCamelCase(      a__=None		,a__=None):
				return field(default_factory=lambda: default		,metadata=a__)

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







def   lowerCamelCase(      a__):
				try:
								int(a__)
								return True
				except ValueError:
								return False







def   lowerCamelCase(      a__):
				try:
								float(a__)
								return True
				except ValueError:
								return False

class   A__							:




						def __init__(				self    :					Optional[int] ,			_a    :					List[str]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =args
										_SCREAMING_SNAKE_CASE	     =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

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




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =plt.subplots()
										_SCREAMING_SNAKE_CASE	     ='''Time usage''' if self.args.is_time else '''Memory usage'''
										_SCREAMING_SNAKE_CASE	     =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()     ):
														_SCREAMING_SNAKE_CASE	     =sorted(set(self.result_dict[model_name]['''bsz''']     )     )
														_SCREAMING_SNAKE_CASE	     =sorted(set(self.result_dict[model_name]['''seq_len''']     )     )
														_SCREAMING_SNAKE_CASE	     =self.result_dict[model_name]['''result''']

														((_SCREAMING_SNAKE_CASE)     , (_SCREAMING_SNAKE_CASE))	     =(
														    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
														)

														_SCREAMING_SNAKE_CASE	     =(
														    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:
																						_SCREAMING_SNAKE_CASE	     =np.asarray(
																						    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,			dtype=_a ,			)
																		else:
																						_SCREAMING_SNAKE_CASE	     =np.asarray(
																						    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,			dtype=np.floataa ,			)

																		((_SCREAMING_SNAKE_CASE)     , (_SCREAMING_SNAKE_CASE))	     =(
																		    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																		)

																		_SCREAMING_SNAKE_CASE	     =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."

										_SCREAMING_SNAKE_CASE	     =title_str[:-4]
										_SCREAMING_SNAKE_CASE	     ='''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   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =HfArgumentParser(a__)
				_SCREAMING_SNAKE_CASE	     =parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE	     =Plot(args=a__)
				plot.plot()


if __name__ == "__main__":
							main()



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class   A__							:




						def __init__(				self    :					List[Any] ,			_a    :					List[Any]     )							->   None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =vocab
										_SCREAMING_SNAKE_CASE	     =normalizers.BertNormalizer(
										    clean_text=_a ,			handle_chinese_chars=_a ,			strip_accents=_a ,			lowercase=_a ,			)
										try:
														import rjieba
										except ImportError:
														raise ImportError(
														    '''You need to install rjieba to use RoFormerTokenizer. '''
														    '''See https://pypi.org/project/rjieba/ for installation.'''     )
										_SCREAMING_SNAKE_CASE	     =rjieba




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					int ,			_a    :					NormalizedString     )							->   List[NormalizedString]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[]

										# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
										for token, start, end in self.jieba.tokenize(str(_a     ) ,			hmm=_a     ):
														if token in self.vocab:
																		splits.append(normalized_string[start:end]     )
														else:
																		_SCREAMING_SNAKE_CASE	     =self.normalizers.normalize_str(_a     ).split()
																		for token in token_list:
																						if token:
																										_SCREAMING_SNAKE_CASE	     =start + len(_a     )
																										splits.append(normalized_string[start:end]     )
																										_SCREAMING_SNAKE_CASE	     =end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

										return splits




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					PreTokenizedString     )							->   int:


										"""simple docstring"""





										pretok.split(self.jieba_split     )