





'''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 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]=None		,       SCREAMING_SNAKE_CASE_  :					Optional[int]=None    ):



    '''simple docstring'''





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

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







def 	__a(SCREAMING_SNAKE_CASE_  :					Dict    ):



    '''simple docstring'''





    try:
        int(__lowercase    )
        return True
    except ValueError:
        return False
def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





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

class    lowerCAmelCase_		:




 def __init__(	self			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      args
     _lowerCAmelCase				=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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





 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      plt.subplots()
     _lowerCAmelCase				=      'Time usage' if self.args.is_time else 'Memory usage'
     _lowerCAmelCase				=      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				=      sorted(set(self.result_dict[model_name]["bsz"]		)		)
         _lowerCAmelCase				=      sorted(set(self.result_dict[model_name]["seq_len"]		)		)
         _lowerCAmelCase				=      self.result_dict[model_name]['result']

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

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

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

             _lowerCAmelCase				=      np.asarray(_lowerCAmelCase			,     _lowerCAmelCase		)[: len(_lowerCAmelCase		)]
             plt.scatter(
                 _lowerCAmelCase			,     _lowerCAmelCase			,     label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''		)
             plt.plot(_lowerCAmelCase			,     _lowerCAmelCase			,     "--"		)

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

     _lowerCAmelCase				=      title_str[:-4]
     _lowerCAmelCase				=      'Time in s' if self.args.is_time else 'Memory in MB'

     # plot
     plt.title(_lowerCAmelCase		)
     plt.xlabel(_lowerCAmelCase		)
     plt.ylabel(_lowerCAmelCase		)
     plt.legend()

     if self.args.figure_png_file is not None:
         plt.savefig(self.args.figure_png_file		)
     else:
         plt.show()







def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      HfArgumentParser(__lowercase    )
    _lowerCAmelCase				=      parser.parse_args_into_dataclasses()[0]
    _lowerCAmelCase				=      Plot(args=__lowercase    )
    plot.plot()


if __name__ == "__main__":
       main()









'''simple docstring'''


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE	 =							{"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE	 =							{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

_SCREAMING_SNAKE_CASE	 =							{
    "AI-Sweden/gpt-sw3-126m": 20_48,
    "AI-Sweden/gpt-sw3-350m": 20_48,
    "AI-Sweden/gpt-sw3-1.6b": 20_48,
    "AI-Sweden/gpt-sw3-6.7b": 20_48,
    "AI-Sweden/gpt-sw3-20b": 20_48,
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					VOCAB_FILES_NAMES
 __lowerCamelCase						:							List[Any]         =					PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase						:							Tuple         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase						:							List[str]         =					["input_ids", "attention_mask"]




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=False			,     _lowerCAmelCase=False			,     _lowerCAmelCase=False			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							None:
     _lowerCAmelCase				=      {} if sp_model_kwargs is None else sp_model_kwargs

     _lowerCAmelCase				=      kwargs.get("name_or_path"		)
     if name_or_path is None:
         logger.warning(
             "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
             " you are testing the model, this can safely be ignored"		)
         _lowerCAmelCase				=      "None"

     # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
     _lowerCAmelCase				=      "<|endoftext|>" if eos_token is None else eos_token
     _lowerCAmelCase				=      "<unk>" if unk_token is None else unk_token
     if "gpt-sw3-7b" in name_or_path:
         _lowerCAmelCase				=      unk_token if pad_token is None else pad_token
         _lowerCAmelCase				=      eos_token if bos_token is None else bos_token
     else:
         _lowerCAmelCase				=      "<pad>" if pad_token is None else pad_token
         _lowerCAmelCase				=      "<s>" if bos_token is None else bos_token

     super().__init__(
         do_lower_case=_lowerCAmelCase			,     remove_space=_lowerCAmelCase			,     keep_accents=_lowerCAmelCase			,     bos_token=_lowerCAmelCase			,     eos_token=_lowerCAmelCase			,     unk_token=_lowerCAmelCase			,     pad_token=_lowerCAmelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **_lowerCAmelCase			,     )

     _lowerCAmelCase				=      do_lower_case
     _lowerCAmelCase				=      remove_space
     _lowerCAmelCase				=      keep_accents
     _lowerCAmelCase				=      vocab_file

     _lowerCAmelCase				=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(_lowerCAmelCase		)

     # Used for whitespace normalization in input texts
     # fmt : off
     _lowerCAmelCase				=      {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
     # fmt : on

     # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
     _lowerCAmelCase				=      re.compile(
         f'''[{''.join(map(_lowerCAmelCase			,     list(range(0			,     9		)		) + list(range(11			,     32		)		) + list(range(127			,     160		)		) + [160, 173, 8203]		)		)}]'''		)




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




 def __setstate__(	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      d

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

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




 @property
 # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
 def 							_snake_case   (	self		)    ->							int:
     return len(self.sp_model		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      self.non_printing_characters_re.sub(""			,     _lowerCAmelCase		)

     # Normalize whitespaces
     _lowerCAmelCase				=      "".join([char if char not in self.whitespaces else " " for char in text]		)

     # NFC Unicode normalization
     _lowerCAmelCase				=      unicodedata.normalize("NFC"			,     _lowerCAmelCase		)
     return text




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[str]:
     _lowerCAmelCase				=      self.preprocess_text(_lowerCAmelCase		)
     return self.sp_model.encode(_lowerCAmelCase			,     out_type=_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							int:
     return self.sp_model.PieceToId(_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     return self.sp_model.IdToPiece(_lowerCAmelCase		)




 @staticmethod
 def 							_snake_case   (	_lowerCAmelCase		)    ->							str:
     return out_string




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      []
     _lowerCAmelCase				=      ""
     _lowerCAmelCase				=      False
     for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
             # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
             if not prev_is_special:
                 out_string += " "

             out_string += self.sp_model.decode(_lowerCAmelCase		) + token
             _lowerCAmelCase				=      True
             _lowerCAmelCase				=      []
         else:
             current_sub_tokens.append(_lowerCAmelCase		)
             _lowerCAmelCase				=      False
     out_string += self.sp_model.decode(_lowerCAmelCase		)

     return out_string




 def 							_snake_case   (	self		)    ->							Dict[str, int]:
     _lowerCAmelCase				=      {self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
     vocab.update(self.added_tokens_encoder		)
     return vocab




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							Tuple[str]:
     if not os.path.isdir(_lowerCAmelCase		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
         return
     _lowerCAmelCase				=      os.path.join(
         _lowerCAmelCase			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

     if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCAmelCase		) and os.path.isfile(self.vocab_file		):
         copyfile(self.vocab_file			,     _lowerCAmelCase		)
     elif not os.path.isfile(self.vocab_file		):
         with open(_lowerCAmelCase			,     "wb"		) as fi:
             _lowerCAmelCase				=      self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase		)

     return (out_vocab_file,)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False		)    ->							Union[List[int], List[List[int]], "torch.Tensor"]:

     if isinstance(_lowerCAmelCase			,     _lowerCAmelCase		):
         _lowerCAmelCase				=      self.preprocess_text(_lowerCAmelCase		)
         _lowerCAmelCase				=      self.sp_model.encode(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      [self.preprocess_text(_lowerCAmelCase		) for t in text]
         _lowerCAmelCase				=      self.sp_model.encode(_lowerCAmelCase		)

     if return_tensors is True or return_tensors == "pt":
         _lowerCAmelCase				=      torch.tensor(_lowerCAmelCase		)

     return token_ids




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     return self.sp_model.decode(_lowerCAmelCase		)





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[int]:
     _lowerCAmelCase				=      [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
     _lowerCAmelCase				=      (
         f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(_lowerCAmelCase		) + f'''{self.bos_token}Bot:'''
     )
     return self.encode(text=_lowerCAmelCase		)


