



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 logging


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

UpperCAmelCase_		 =					{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

UpperCAmelCase_		 =					{
    """albert-base-v1""": 5_1_2,
    """albert-large-v1""": 5_1_2,
    """albert-xlarge-v1""": 5_1_2,
    """albert-xxlarge-v1""": 5_1_2,
    """albert-base-v2""": 5_1_2,
    """albert-large-v2""": 5_1_2,
    """albert-xlarge-v2""": 5_1_2,
    """albert-xxlarge-v2""": 5_1_2,
}

UpperCAmelCase_		 =					"""▁"""


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Tuple =  VOCAB_FILES_NAMES
 a__					: str =  PRETRAINED_VOCAB_FILES_MAP
 a__					: int =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

 def __init__(    self					:					Dict			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					Tuple=True			,						__lowerCAmelCase					:					Optional[Any]=False			,						__lowerCAmelCase					:					Optional[Any]="[CLS]"			,						__lowerCAmelCase					:					List[Any]="[SEP]"			,						__lowerCAmelCase					:					List[str]="<unk>"			,						__lowerCAmelCase					:					str="[SEP]"			,						__lowerCAmelCase					:					Optional[Any]="<pad>"			,						__lowerCAmelCase					:					int="[CLS]"			,						__lowerCAmelCase					:					List[str]="[MASK]"			,						__lowerCAmelCase					:					Optional[Dict[str, Any]] = None			,						**__lowerCAmelCase					:					List[Any]			,						)   ->	None:
    # Mask token behave like a normal word, i.e. include the space before it and
    # is included in the raw text, there should be a match in a non-normalized sentence.
    _A      =       (
        AddedToken(__lowerCAmelCase			,						lstrip=__lowerCAmelCase			,						rstrip=__lowerCAmelCase			,						normalized=__lowerCAmelCase      )
        if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      )
        else mask_token
    )

    _A      =       {} if sp_model_kwargs is None else sp_model_kwargs

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

    _A      =       do_lower_case
    _A      =       remove_space
    _A      =       keep_accents
    _A      =       vocab_file

    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					Optional[int]      )   ->	List[str]:
    _A      =       {self.convert_ids_to_tokens(__lowerCAmelCase      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab

 def __getstate__(    self					:					str      )   ->	str:
    _A      =       self.__dict__.copy()
    _A      =       None
    return state

 def __setstate__(    self					:					Dict			,						__lowerCAmelCase					:					Dict      )   ->	Tuple:
    _A      =       d

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

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

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					str      )   ->	Any:
    if self.remove_space:
       _A      =       ''' '''.join(inputs.strip().split()      )
    else:
       _A      =       inputs
    _A      =       outputs.replace('''``'''			,						'''"'''      ).replace('''\'\''''			,						'''"'''      )

    if not self.keep_accents:
       _A      =       unicodedata.normalize('''NFKD'''			,						__lowerCAmelCase      )
       _A      =       ''''''.join([c for c in outputs if not unicodedata.combining(__lowerCAmelCase      )]      )
    if self.do_lower_case:
       _A      =       outputs.lower()

    return outputs

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					str      )   ->	List[str]:
    _A      =       self.preprocess_text(__lowerCAmelCase      )
    _A      =       self.sp_model.encode(__lowerCAmelCase			,						out_type=__lowerCAmelCase      )
    _A      =       []
    for piece in pieces:
       if len(__lowerCAmelCase      ) > 1 and piece[-1] == str(''','''      ) and piece[-2].isdigit():
          _A      =       self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCAmelCase			,						''''''      )      )
          if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
             if len(cur_pieces[0]      ) == 1:
                _A      =       cur_pieces[1:]
             else:
                _A      =       cur_pieces[0][1:]
          cur_pieces.append(piece[-1]      )
          new_pieces.extend(__lowerCAmelCase      )
       else:
          new_pieces.append(__lowerCAmelCase      )

    return new_pieces

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Optional[int]      )   ->	Dict:
    return self.sp_model.PieceToId(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[int]      )   ->	List[Any]:
    return self.sp_model.IdToPiece(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Optional[Any]:
    _A      =       []
    _A      =       ''''''
    _A      =       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(__lowerCAmelCase      ) + token
          _A      =       True
          _A      =       []
       else:
          current_sub_tokens.append(__lowerCAmelCase      )
          _A      =       False
    out_string += self.sp_model.decode(__lowerCAmelCase      )
    return out_string.strip()

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

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None			,						__lowerCAmelCase					:					bool = False      )   ->	List[int]:

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

    if token_ids_a is not None:
       return [1] + ([0] * len(__lowerCAmelCase      )) + [1] + ([0] * len(__lowerCAmelCase      )) + [1]
    return [1] + ([0] * len(__lowerCAmelCase      )) + [1]

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:
    _A      =       [self.sep_token_id]
    _A      =       [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      ) * [0] + len(token_ids_a + sep      ) * [1]

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    if not os.path.isdir(__lowerCAmelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _A      =       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:
          _A      =       self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase      )

    return (out_vocab_file,)











'''simple docstring'''
import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed


class 							_UpperCAmelCase    (		snake_case      ):




    def __init__(      self	:							Optional[Any]	,  a	:							Dict	,  a	:							int	,  a	:							Any=1_0_2_4	,  a	:							Tuple=1_0_2_4	,  a	:							Optional[int]=3.6      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  tokenizer
       lowercase_						:     Union[str, Any]							     =  tokenizer.bos_token_id
       lowercase_						:     Union[str, Any]							     =  dataset
       lowercase_						:     Optional[Any]							     =  seq_length
       lowercase_						:     Any							     =  seq_length * chars_per_token * num_of_sequences





    def __iter__(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  iter(self.dataset      )
       lowercase_						:     Dict							     =  True
       while more_examples:
          lowercase_     ,		lowercase_						:     Optional[int]							     =  [], 0
          while True:
             if buffer_len >= self.input_characters:
                break
             try:
                buffer.append(next(a      )["content"]      )
                buffer_len += len(buffer[-1]      )
             except StopIteration:
                lowercase_						:     int							     =  False
                break
          lowercase_						:     Any							     =  tokenizer(a	,  truncation=a      )["input_ids"]
          lowercase_						:     List[str]							     =  []
          for tokenized_input in tokenized_inputs:
             all_token_ids.extend(tokenized_input + [self.concat_token_id]      )
          for i in range(0	,  len(a      )	,  self.seq_length      ):
             lowercase_						:     List[Any]							     =  all_token_ids[i : i + self.seq_length]
             if len(a      ) == self.seq_length:
                yield torch.tensor(a      )




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  {"streaming": True}
   lowercase_						:     List[Any]							     =  load_dataset(args.dataset_name    ,		split="train"    ,		**_UpperCamelCase				)
   lowercase_						:     int							     =  ConstantLengthDataset(_UpperCamelCase    ,		_UpperCamelCase    ,		seq_length=args.seq_length				)
   lowercase_						:     Dict							     =  DataLoader(_UpperCamelCase    ,		batch_size=args.batch_size				)
   return eval_dataloader




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   model.eval()
   lowercase_						:     Optional[Any]							     =  []
   for step, batch in enumerate(_UpperCamelCase				):
      with torch.no_grad():
         lowercase_						:     str							     =  model(_UpperCamelCase    ,		labels=_UpperCamelCase				)
      lowercase_						:     List[Any]							     =  outputs.loss.repeat(args.batch_size				)
      losses.append(accelerator.gather(_UpperCamelCase				)				)

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   lowercase_						:     Optional[int]							     =  torch.mean(torch.cat(_UpperCamelCase				)				)
   try:
      lowercase_						:     Dict							     =  torch.exp(_UpperCamelCase				)
   except OverflowError:
      lowercase_						:     Dict							     =  float("inf"				)
   return loss.item(), perplexity.item()


# Setup Accelerator
UpperCamelCase__			=      Accelerator()

# Parse configuration
UpperCamelCase__			=      HfArgumentParser(EvaluationArguments)
UpperCamelCase__			=      parser.parse_args()
set_seed(args.seed)

# Logging
UpperCamelCase__			=      logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
UpperCamelCase__			=      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCamelCase__			=      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCamelCase__			=      create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCamelCase__,  UpperCamelCase__			=      accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCamelCase__,  UpperCamelCase__			=      evaluate(args)





logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")




