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  snake_case(    __magic_name__=None			,		__magic_name__=None   )			->							Any:





     '''simple docstring'''


     return field(default_factory=lambda: default			,		metadata=SCREAMING_SNAKE_CASE__   )
@dataclass
class 						_A :
  _UpperCamelCase       :      str			=      field(
      metadata={'''help''': '''The csv file to plot.'''}      ,       )
  _UpperCamelCase       :      int			=      field(
      default=__SCREAMING_SNAKE_CASE      ,       metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}      ,       )
  _UpperCamelCase       :      int			=      field(
      default=__SCREAMING_SNAKE_CASE      ,       metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}      ,       )
  _UpperCamelCase       :      Dict			=      field(
      default=__SCREAMING_SNAKE_CASE      ,       metadata={'''help''': '''Disable logarithmic scale when plotting'''}      ,       )
  _UpperCamelCase       :      Any			=      field(
      default=__SCREAMING_SNAKE_CASE      ,       metadata={
          '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
      }      ,       )
  _UpperCamelCase       :      Dict			=      field(
      default=__SCREAMING_SNAKE_CASE      ,       metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}      ,       )
  _UpperCamelCase       :      str			=      list_field(
      default=__SCREAMING_SNAKE_CASE      ,       metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}				)






def  snake_case(    __magic_name__   )			->							Any:





     '''simple docstring'''


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






def  snake_case(    __magic_name__   )			->							Any:





     '''simple docstring'''


     try:
          float(SCREAMING_SNAKE_CASE__   )
          return True
     except ValueError:
          return False
class 						_A :






  def __init__(  self		:     List[Any]				,						_A		:     List[str]				)		->   str:






       """simple docstring"""







       lowercase :       int       =			args
       lowercase :       List[str]       =			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

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






  def  __a      (  self		:     Optional[int]				)		->   int:






       """simple docstring"""







       lowercase :       Tuple       =			plt.subplots()
       lowercase :       Dict       =			'''Time usage''' if self.args.is_time else '''Memory usage'''
       lowercase :       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()				):
            lowercase :       Optional[int]       =			sorted(set(self.result_dict[model_name]['''bsz''']				)				)
            lowercase :       int       =			sorted(set(self.result_dict[model_name]['''seq_len''']				)				)
            lowercase :       Tuple       =			self.result_dict[model_name]['''result''']

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

            lowercase :       List[str]       =			(
                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 :       List[Any]       =			np.asarray(
                          [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,						dtype=UpperCamelCase__				,						)
                 else:
                      lowercase :       Optional[Any]       =			np.asarray(
                          [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,						dtype=np.floataa				,						)

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

                 lowercase :       Union[str, Any]       =			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."""

       lowercase :       Dict       =			title_str[:-4]
       lowercase :       Dict       =			'''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  snake_case(    )			->							List[str]:





     '''simple docstring'''


     lowercase :       List[Any]       =			HfArgumentParser(SCREAMING_SNAKE_CASE__   )
     lowercase :       Any       =			parser.parse_args_into_dataclasses()[0]
     lowercase :       List[Any]       =			Plot(args=SCREAMING_SNAKE_CASE__   )
     plot.plot()


if __name__ == "__main__":
 main()





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


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_						=			{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

lowerCAmelCase_						=			{
    'moussaKam/mbarthez': 10_24,
    'moussaKam/barthez': 10_24,
    'moussaKam/barthez-orangesum-title': 10_24,
}

lowerCAmelCase_						=			'▁'
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Optional[Any]			=      VOCAB_FILES_NAMES
  _UpperCamelCase       :      Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
  _UpperCamelCase       :      Optional[Any]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCamelCase       :      List[Any]			=      ['''input_ids''', '''attention_mask''']






  def __init__(  self		:     Any				,						_A		:     Optional[int]				,						_A		:     List[str]="<s>"				,						_A		:     Tuple="</s>"				,						_A		:     Dict="</s>"				,						_A		:     Dict="<s>"				,						_A		:     List[str]="<unk>"				,						_A		:     str="<pad>"				,						_A		:     Any="<mask>"				,						_A		:     Optional[Dict[str, Any]] = None				,						**_A		:     Union[str, Any]				,						)		->   None:






       """simple docstring"""







       lowercase :       List[str]       =			AddedToken(_A				,						lstrip=_A				,						rstrip=_A				) if isinstance(_A				,						_A				) else mask_token

       lowercase :       List[Any]       =			{} if sp_model_kwargs is None else sp_model_kwargs

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

       lowercase :       Any       =			vocab_file
       lowercase :       Any       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(str(_A				)				)

       lowercase :       Optional[Any]       =			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       lowercase :       Tuple       =			len(self.sp_model				) - 1
       lowercase :       Any       =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def  __a      (  self		:     Tuple				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







       if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase :       Any       =			[self.cls_token_id]
       lowercase :       int       =			[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






  def  __a      (  self		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				,						_A		:     bool = False				)		->   List[int]:






       """simple docstring"""







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

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






  def  __a      (  self		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







       lowercase :       List[str]       =			[self.sep_token_id]
       lowercase :       List[str]       =			[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  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return len(self.sp_model				)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       lowercase :       int       =			{self.convert_ids_to_tokens(_A				): i for i in range(self.vocab_size				)}
       vocab.update(self.added_tokens_encoder				)
       return vocab






  def  __a      (  self		:     Union[str, Any]				,						_A		:     str				)		->   List[str]:






       """simple docstring"""







       return self.sp_model.encode(_A				,						out_type=_A				)






  def  __a      (  self		:     Optional[int]				,						_A		:     str				)		->   Dict:






       """simple docstring"""







       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       lowercase :       Optional[int]       =			self.sp_model.PieceToId(_A				)

       return spm_id if spm_id else self.unk_token_id






  def  __a      (  self		:     Any				,						_A		:     List[str]				)		->   List[str]:






       """simple docstring"""







       if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(_A				)






  def  __a      (  self		:     Any				,						_A		:     Tuple				)		->   Tuple:






       """simple docstring"""







       lowercase :       Dict       =			[]
       lowercase :       Any       =			''''''
       lowercase :       str       =			False
       for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                 if not prev_is_special:
                      out_string += " "
                 out_string += self.sp_model.decode(_A				) + token
                 lowercase :       int       =			True
                 lowercase :       Optional[Any]       =			[]
            else:
                 current_sub_tokens.append(_A				)
                 lowercase :       List[str]       =			False
       out_string += self.sp_model.decode(_A				)
       return out_string.strip()






  def __getstate__(  self		:     int				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       str       =			self.__dict__.copy()
       lowercase :       Any       =			None
       return state






  def __setstate__(  self		:     Optional[int]				,						_A		:     Tuple				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			d

       # for backward compatibility
       if not hasattr(self				,						'''sp_model_kwargs'''				):
            lowercase :       Union[str, Any]       =			{}

       lowercase :       List[Any]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(self.vocab_file				)






  def  __a      (  self		:     str				,						_A		:     str				,						_A		:     Optional[str] = None				)		->   Tuple[str]:






       """simple docstring"""







       if not os.path.isdir(_A				):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
            return
       lowercase :       Any       =			os.path.join(
           _A				,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

       if os.path.abspath(self.vocab_file				) != os.path.abspath(_A				) and os.path.isfile(self.vocab_file				):
            copyfile(self.vocab_file				,						_A				)
       elif not os.path.isfile(self.vocab_file				):
            with open(_A				,						'''wb'''				) as fi:
                 lowercase :       Optional[Any]       =			self.sp_model.serialized_model_proto()
                 fi.write(_A				)

       return (out_vocab_file,)