






'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A     :      str   					=logging.get_logger(__name__)

_A     :      List[str]   					='''▁'''

_A     :      Optional[Any]   					={'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

_A     :      str   					={
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

_A     :      Optional[Any]   					={'''vinai/bartpho-syllable''': 1_024}

class _lowercase     (    _lowercase		):
							a    				=	VOCAB_FILES_NAMES
							a    				=	PRETRAINED_VOCAB_FILES_MAP
							a    				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a    				=	["""input_ids""", """attention_mask"""]




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Tuple="<s>"      ,							UpperCamelCase__:    Dict="</s>"      ,							UpperCamelCase__:    Dict="</s>"      ,							UpperCamelCase__:    Any="<s>"      ,							UpperCamelCase__:    Optional[Any]="<unk>"      ,							UpperCamelCase__:    List[str]="<pad>"      ,							UpperCamelCase__:    int="<mask>"      ,							UpperCamelCase__:    Optional[Dict[str, Any]] = None      ,							**UpperCamelCase__:    Any      ,							):
														# Mask token behave like a normal word, i.e. include the space before it
														lowerCamelCase__ :     str	  =	AddedToken(UpperCamelCase__      ,							lstrip=UpperCamelCase__      ,							rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__      ,							UpperCamelCase__					) else mask_token

														lowerCamelCase__ :     Optional[Any]	  =	{} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=UpperCamelCase__      ,							eos_token=UpperCamelCase__      ,							unk_token=UpperCamelCase__      ,							sep_token=UpperCamelCase__      ,							cls_token=UpperCamelCase__      ,							pad_token=UpperCamelCase__      ,							mask_token=UpperCamelCase__      ,							sp_model_kwargs=self.sp_model_kwargs      ,							**UpperCamelCase__      ,							)

														lowerCamelCase__ :     Tuple	  =	vocab_file
														lowerCamelCase__ :     Optional[Any]	  =	monolingual_vocab_file
														lowerCamelCase__ :     List[Any]	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
														self.sp_model.Load(str(UpperCamelCase__					)					)

														# Load the reduced vocab

														# Keep order of special tokens for backward compatibility
														lowerCamelCase__ :     Any	  =	{}
														lowerCamelCase__ :     Union[str, Any]	  =	0
														for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
																					if str(UpperCamelCase__					) not in self.fairseq_tokens_to_ids:
																												lowerCamelCase__ :     Dict	  =	cnt
																												cnt += 1
														with open(UpperCamelCase__      ,							"""r"""      ,							encoding="""utf-8"""					) as f:
																					for line in f.readlines():
																												lowerCamelCase__ :     Tuple	  =	line.strip().split()[0]
																												lowerCamelCase__ :     List[Any]	  =	len(self.fairseq_tokens_to_ids					)
														if str(UpperCamelCase__					) not in self.fairseq_tokens_to_ids:
																					lowerCamelCase__ :     Optional[Any]	  =	len(self.fairseq_tokens_to_ids					)

														lowerCamelCase__ :     Optional[int]	  =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def __getstate__(							self:    Union[str, Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	self.__dict__.copy()
														lowerCamelCase__ :     Union[str, Any]	  =	None
														lowerCamelCase__ :     Any	  =	self.sp_model.serialized_model_proto()
														return state




							def __setstate__(							self:    List[Any]      ,							UpperCamelCase__:    Tuple					):
														lowerCamelCase__ :     int	  =	d

														# for backward compatibility
														if not hasattr(self      ,							"""sp_model_kwargs"""					):
																					lowerCamelCase__ :     List[str]	  =	{}

														lowerCamelCase__ :     Optional[Any]	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
														self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None					):

														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														lowerCamelCase__ :     Union[str, Any]	  =	[self.cls_token_id]
														lowerCamelCase__ :     str	  =	[self.sep_token_id]
														return cls + token_ids_a + sep + sep + token_ids_a + sep




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None      ,							UpperCamelCase__:    bool = False					):

														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=UpperCamelCase__      ,							token_ids_a=UpperCamelCase__      ,							already_has_special_tokens=UpperCamelCase__					)

														if token_ids_a is None:
																					return [1] + ([0] * len(UpperCamelCase__					)) + [1]
														return [1] + ([0] * len(UpperCamelCase__					)) + [1, 1] + ([0] * len(UpperCamelCase__					)) + [1]




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None					):
														lowerCamelCase__ :     Any	  =	[self.sep_token_id]
														lowerCamelCase__ :     Tuple	  =	[self.cls_token_id]

														if token_ids_a is None:
																					return len(cls + token_ids_a + sep					) * [0]
														return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]




							@property
							def 		lowerCamelCase_    (							self:    str					):
														return len(self.fairseq_ids_to_tokens					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     Tuple	  =	{self.convert_ids_to_tokens(UpperCamelCase__					): i for i in range(self.vocab_size					)}
														vocab.update(self.added_tokens_encoder					)
														return vocab




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    str					):
														return self.sp_model.encode(UpperCamelCase__      ,							out_type=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    Optional[Any]					):
														if token in self.fairseq_tokens_to_ids:
																					return self.fairseq_tokens_to_ids[token]
														else:
																					return self.unk_token_id




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    str					):
														return self.fairseq_ids_to_tokens[index]




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     Dict	  =	"""""".join(UpperCamelCase__					).replace(UpperCamelCase__      ,							""" """					).strip()
														return out_string




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[str] = None					):
														if not os.path.isdir(UpperCamelCase__					):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
																					return
														lowerCamelCase__ :     int	  =	os.path.join(
														    UpperCamelCase__      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
														lowerCamelCase__ :     List[Any]	  =	os.path.join(
														    UpperCamelCase__      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]      ,							)

														if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCamelCase__					) and os.path.isfile(self.vocab_file					):
																					copyfile(self.vocab_file      ,							UpperCamelCase__					)
														elif not os.path.isfile(self.vocab_file					):
																					with open(UpperCamelCase__      ,							"""wb"""					) as fi:
																												lowerCamelCase__ :     Optional[int]	  =	self.sp_model.serialized_model_proto()
																												fi.write(UpperCamelCase__					)

														if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
														    UpperCamelCase__					) and os.path.isfile(self.monolingual_vocab_file					):
																					copyfile(self.monolingual_vocab_file      ,							UpperCamelCase__					)
														elif not os.path.isfile(self.monolingual_vocab_file					):
																					with open(UpperCamelCase__      ,							"""w"""      ,							encoding="""utf-8"""					) as fp:
																												for token in self.fairseq_tokens_to_ids:
																																			if token not in self.all_special_tokens:
																																										fp.write(F'''{str(UpperCamelCase__					)} \n'''					)

														return out_vocab_file, out_monolingual_vocab_file









'''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_	(UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Any:
							return field(default_factory=lambda: default    ,      metadata=UpperCamelCase				)

@dataclass
class _lowercase     :
							a    				=	field(
							    metadata={"""help""": """The csv file to plot."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Disable logarithmic scale when plotting"""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={
							        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
							    }    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}    ,				)
							a    				=	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_	(UpperCamelCase				)				->	Dict:
							try:
														int(UpperCamelCase				)
														return True
							except ValueError:
														return False





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							try:
														float(UpperCamelCase				)
														return True
							except ValueError:
														return False

class _lowercase     :




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

														with open(self.args.csv_file      ,							newline=""""""					) as csv_file:
																					lowerCamelCase__ :     str	  =	csv.DictReader(UpperCamelCase__					)
																					for row in reader:
																												lowerCamelCase__ :     Optional[int]	  =	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__ :     Tuple	  =	int(row["""result"""]					)
																												elif can_convert_to_float(row["""result"""]					):
																																			# value is not None
																																			lowerCamelCase__ :     Any	  =	float(row["""result"""]					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Tuple	  =	plt.subplots()
														lowerCamelCase__ :     Any	  =	"""Time usage""" if self.args.is_time else """Memory usage"""
														lowerCamelCase__ :     List[str]	  =	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__ :     Any	  =	sorted(set(self.result_dict[model_name]["""bsz"""]					)					)
																					lowerCamelCase__ :     int	  =	sorted(set(self.result_dict[model_name]["""seq_len"""]					)					)
																					lowerCamelCase__ :     Any	  =	self.result_dict[model_name]["""result"""]

																					((lowerCamelCase__)  ,		(lowerCamelCase__)) :     Dict	  =	(
																					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																					)

																					lowerCamelCase__ :     Any	  =	(
																					    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__ :     int	  =	np.asarray(
																																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=UpperCamelCase__      ,							)
																												else:
																																			lowerCamelCase__ :     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      ,							)

																												((lowerCamelCase__)  ,		(lowerCamelCase__)) :     List[str]	  =	(
																												    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																												)

																												lowerCamelCase__ :     int	  =	np.asarray(UpperCamelCase__      ,							UpperCamelCase__					)[: len(UpperCamelCase__					)]
																												plt.scatter(
																												    UpperCamelCase__      ,							UpperCamelCase__      ,							label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''					)
																												plt.plot(UpperCamelCase__      ,							UpperCamelCase__      ,							"""--"""					)

																					title_str += F''' {label_model_name} vs.'''

														lowerCamelCase__ :     Any	  =	title_str[:-4]
														lowerCamelCase__ :     Optional[int]	  =	"""Time in s""" if self.args.is_time else """Memory in MB"""

														# plot
														plt.title(UpperCamelCase__					)
														plt.xlabel(UpperCamelCase__					)
														plt.ylabel(UpperCamelCase__					)
														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:
							lowerCamelCase__ :     str	  =	HfArgumentParser(UpperCamelCase				)
							lowerCamelCase__ :     str	  =	parser.parse_args_into_dataclasses()[0]
							lowerCamelCase__ :     Any	  =	Plot(args=UpperCamelCase				)
							plot.plot()


if __name__ == "__main__":
			main()

