


"""simple docstring"""







import os
import unicodedata
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 SPIECE_UNDERLINE, logging


a__       :							List[Any]        =  logging.get_logger(__name__)

a__       :							Dict        =  {'''vocab_file''': '''spiece.model'''}

a__       :							str        =  {
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

a__       :							Any        =  {
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
a__       :							str        =  0
a__       :							str        =  1
a__       :							Any        =  2
a__       :							Any        =  3
a__       :							Tuple        =  4




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Tuple					      =    VOCAB_FILES_NAMES
	snake_case__       :						Optional[Any]					      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__       :						int					      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__       :						str					      =    "left"




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

						__SCREAMING_SNAKE_CASE		     =       {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    do_lower_case=UpperCAmelCase__    ,    remove_space=UpperCAmelCase__    ,    keep_accents=UpperCAmelCase__    ,    bos_token=UpperCAmelCase__    ,    eos_token=UpperCAmelCase__    ,    unk_token=UpperCAmelCase__    ,    sep_token=UpperCAmelCase__    ,    pad_token=UpperCAmelCase__    ,    cls_token=UpperCAmelCase__    ,    mask_token=UpperCAmelCase__    ,    additional_special_tokens=UpperCAmelCase__    ,    sp_model_kwargs=self.sp_model_kwargs    ,    **UpperCAmelCase__    ,    )

						__SCREAMING_SNAKE_CASE		     =       3

						__SCREAMING_SNAKE_CASE		     =       do_lower_case
						__SCREAMING_SNAKE_CASE		     =       remove_space
						__SCREAMING_SNAKE_CASE		     =       keep_accents
						__SCREAMING_SNAKE_CASE		     =       vocab_file

						__SCREAMING_SNAKE_CASE		     =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(UpperCAmelCase__     )




	@property
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[Any]:
						return len(self.sp_model     )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       {self.convert_ids_to_tokens(UpperCAmelCase__     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab




	def __getstate__(				self    :       Any     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       self.__dict__.copy()
						__SCREAMING_SNAKE_CASE		     =       None
						return state




	def __setstate__(				self    :       Dict    ,    UpperCAmelCase__    :       Optional[int]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       d

						# for backward compatibility
						if not hasattr(self    ,    "sp_model_kwargs"     ):
											__SCREAMING_SNAKE_CASE		     =       {}

						__SCREAMING_SNAKE_CASE		     =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Union[str, Any]     ) ->			Any:
						if self.remove_space:
											__SCREAMING_SNAKE_CASE		     =       " ".join(inputs.strip().split()     )
						else:
											__SCREAMING_SNAKE_CASE		     =       inputs
						__SCREAMING_SNAKE_CASE		     =       outputs.replace("``"    ,    "\""     ).replace("''"    ,    "\""     )

						if not self.keep_accents:
											__SCREAMING_SNAKE_CASE		     =       unicodedata.normalize("NFKD"    ,    UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       "".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase__     )]     )
						if self.do_lower_case:
											__SCREAMING_SNAKE_CASE		     =       outputs.lower()

						return outputs




	def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       str     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       self.preprocess_text(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.sp_model.encode(UpperCAmelCase__    ,    out_type=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       []
						for piece in pieces:
											if len(UpperCAmelCase__     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
																__SCREAMING_SNAKE_CASE		     =       self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase__    ,    ""     )     )
																if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																					if len(cur_pieces[0]     ) == 1:
																										__SCREAMING_SNAKE_CASE		     =       cur_pieces[1:]
																					else:
																										__SCREAMING_SNAKE_CASE		     =       cur_pieces[0][1:]
																cur_pieces.append(piece[-1]     )
																new_pieces.extend(UpperCAmelCase__     )
											else:
																new_pieces.append(UpperCAmelCase__     )

						return new_pieces




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       Union[str, Any]     ) ->			str:
						return self.sp_model.PieceToId(UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Any     ) ->			List[Any]:
						return self.sp_model.IdToPiece(UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       str     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       "".join(UpperCAmelCase__     ).replace(UpperCAmelCase__    ,    " "     ).strip()
						return out_string




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       bool = True    ,    **UpperCAmelCase__    :       Tuple    ,    ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       kwargs.pop("use_source_tokenizer"    ,    UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       self.convert_ids_to_tokens(UpperCAmelCase__    ,    skip_special_tokens=UpperCAmelCase__     )

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						__SCREAMING_SNAKE_CASE		     =       []
						__SCREAMING_SNAKE_CASE		     =       []
						for token in filtered_tokens:
											if skip_special_tokens and token in self.all_special_ids:
																continue
											if token in self.added_tokens_encoder:
																if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase__     )     )
																					__SCREAMING_SNAKE_CASE		     =       []
																sub_texts.append(UpperCAmelCase__     )
											else:
																current_sub_text.append(UpperCAmelCase__     )
						if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase__     )     )

						# Mimic the behavior of the Rust tokenizer:
						# By default, there are no spaces between special tokens
						__SCREAMING_SNAKE_CASE		     =       "".join(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       (
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
											__SCREAMING_SNAKE_CASE		     =       self.clean_up_tokenization(UpperCAmelCase__     )
											return clean_text
						else:
											return text




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None     ) ->			List[int]:
						__SCREAMING_SNAKE_CASE		     =       [self.sep_token_id]
						__SCREAMING_SNAKE_CASE		     =       [self.cls_token_id]
						if token_ids_a is None:
											return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None    ,    UpperCAmelCase__    :       bool = False     ) ->			List[int]:

						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 not None:
											return ([0] * len(UpperCAmelCase__     )) + [1] + ([0] * len(UpperCAmelCase__     )) + [1, 1]
						return ([0] * len(UpperCAmelCase__     )) + [1, 1]




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None     ) ->			List[int]:
						__SCREAMING_SNAKE_CASE		     =       [self.sep_token_id]
						__SCREAMING_SNAKE_CASE		     =       [2]

						if token_ids_a is None:
											return len(token_ids_a + sep     ) * [0] + cls_segment_id
						return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Optional[str] = None     ) ->			Tuple[str]:
						if not os.path.isdir(UpperCAmelCase__     ):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
											return
						__SCREAMING_SNAKE_CASE		     =       os.path.join(
						    UpperCAmelCase__    ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["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:
																__SCREAMING_SNAKE_CASE		     =       self.sp_model.serialized_model_proto()
																fi.write(UpperCAmelCase__     )

						return (out_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      UpperCAmelCase__ (lowerCAmelCase_=None						,		lowerCAmelCase_=None     ):




					'''simple docstring'''


					return field(default_factory=lambda: default						,		metadata=lowerCAmelCase_     )




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Dict     ) ->			Optional[Any]:
						__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(UpperCAmelCase__     )
											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    :       List[str]     ) ->			Tuple:
						__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=UpperCAmelCase__    ,    )
																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(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."""

						__SCREAMING_SNAKE_CASE		     =       title_str[:-4]
						__SCREAMING_SNAKE_CASE		     =       "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      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       HfArgumentParser(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       parser.parse_args_into_dataclasses()[0]
					__SCREAMING_SNAKE_CASE		     =       Plot(args=lowerCAmelCase_     )
					plot.plot()


if __name__ == "__main__":
		main()
