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 _a  (			lowerCAmelCase__   ):

	'''simple docstring'''



	def __init__(  self				,					__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase=1_024				,					__UpperCAmelCase=1_024				,					__UpperCAmelCase=3.6       ):
			__A				:       Optional[int]			    = tokenizer
			__A				:       List[Any]			    = tokenizer.bos_token_id
			__A				:       Tuple			    = dataset
			__A				:       List[Any]			    = seq_length
			__A				:       int			    = seq_length * chars_per_token * num_of_sequences







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



def        lowerCamelCase_       (    _lowercase							)				->			Any:
		__A				:       Tuple			    = {"streaming": True}
		__A				:       Any			    = load_dataset(args.dataset_name   ,      split="train"   ,      **A__							)
		__A				:       Union[str, Any]			    = ConstantLengthDataset(A__   ,      A__   ,      seq_length=args.seq_length							)
		__A				:       Optional[int]			    = DataLoader(A__   ,      batch_size=args.batch_size							)
		return eval_dataloader



def        lowerCamelCase_       (    _lowercase							)				->			Dict:
		model.eval()
		__A				:       Optional[int]			    = []
		for step, batch in enumerate(A__							):
				with torch.no_grad():
						__A				:       Optional[Any]			    = model(A__   ,      labels=A__							)
				__A				:       Optional[int]			    = outputs.loss.repeat(args.batch_size							)
				losses.append(accelerator.gather(A__							)							)

				if args.max_eval_steps > 0 and step >= args.max_eval_steps:
						break
		__A				:       Tuple			    = torch.mean(torch.cat(A__							)							)
		try:
				__A				:       List[str]			    = torch.exp(A__							)
		except OverflowError:
				__A				:       Optional[Any]			    = 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     					=      accelerator.prepare(model, eval_dataloader)

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





logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')










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


a__   :					Optional[int] 						=      logging.get_logger(__name__)
a__   :					Dict 						=      {"vocab_file": "spiece.model"}

a__   :					str 						=      {
    "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",
    }
}

a__   :					Union[str, Any] 						=      {
    "albert-base-v1": 5_12,
    "albert-large-v1": 5_12,
    "albert-xlarge-v1": 5_12,
    "albert-xxlarge-v1": 5_12,
    "albert-base-v2": 5_12,
    "albert-large-v2": 5_12,
    "albert-xlarge-v2": 5_12,
    "albert-xxlarge-v2": 5_12,
}

a__   :					Optional[int] 						=      "▁"



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Tuple												=     VOCAB_FILES_NAMES
   A     :	Dict												=     PRETRAINED_VOCAB_FILES_MAP
   A     :	str												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


   def __init__(    self	:      List[str]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Optional[int]=True      ,					lowerCAmelCase	:      List[Any]=True      ,					lowerCAmelCase	:      str=False      ,					lowerCAmelCase	:      str="[CLS]"      ,					lowerCAmelCase	:      Optional[int]="[SEP]"      ,					lowerCAmelCase	:      int="<unk>"      ,					lowerCAmelCase	:      str="[SEP]"      ,					lowerCAmelCase	:      Dict="<pad>"      ,					lowerCAmelCase	:      int="[CLS]"      ,					lowerCAmelCase	:      Tuple="[MASK]"      ,					lowerCAmelCase	:      Optional[Dict[str, Any]] = None      ,					**lowerCAmelCase	:      List[str]      ,					)						->					None:




         """simple docstring"""

         lowercase__  =	(
             AddedToken(lowerCAmelCase      ,					lstrip=lowerCAmelCase      ,					rstrip=lowerCAmelCase      ,					normalized=lowerCAmelCase)
             if isinstance(lowerCAmelCase      ,					lowerCAmelCase)
             else mask_token
         )

         lowercase__  =	{} 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      ,					)

         lowercase__  =	do_lower_case
         lowercase__  =	remove_space
         lowercase__  =	keep_accents
         lowercase__  =	vocab_file

         lowercase__  =	spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(lowerCAmelCase)


   @property
   def       UpperCAmelCase		(    self	:      Any)						->					Optional[int]:




         """simple docstring"""

         return len(self.sp_model)


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




         """simple docstring"""

         lowercase__  =	{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	:      int)						->					List[str]:




         """simple docstring"""

         lowercase__  =	self.__dict__.copy()
         lowercase__  =	None
         return state


   def __setstate__(    self	:      int      ,					lowerCAmelCase	:      str)						->					Any:




         """simple docstring"""

         lowercase__  =	d

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

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


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      int)						->					List[str]:




         """simple docstring"""

         if self.remove_space:
               lowercase__  =	' '.join(inputs.strip().split())
         else:
               lowercase__  =	inputs
         lowercase__  =	outputs.replace('``'      ,					'"').replace('\'\''      ,					'"')

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

         return outputs


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      str)						->					List[str]:




         """simple docstring"""

         lowercase__  =	self.preprocess_text(lowerCAmelCase)
         lowercase__  =	self.sp_model.encode(lowerCAmelCase      ,					out_type=lowerCAmelCase)
         lowercase__  =	[]
         for piece in pieces:
               if len(lowerCAmelCase) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
                     lowercase__  =	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:
                                 lowercase__  =	cur_pieces[1:]
                           else:
                                 lowercase__  =	cur_pieces[0][1:]
                     cur_pieces.append(piece[-1])
                     new_pieces.extend(lowerCAmelCase)
               else:
                     new_pieces.append(lowerCAmelCase)

         return new_pieces


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      int)						->					int:




         """simple docstring"""

         return self.sp_model.PieceToId(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Any)						->					Tuple:




         """simple docstring"""

         return self.sp_model.IdToPiece(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Tuple      ,					lowerCAmelCase	:      Optional[Any])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	[]
         lowercase__  =	''
         lowercase__  =	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
                     lowercase__  =	True
                     lowercase__  =	[]
               else:
                     current_sub_tokens.append(lowerCAmelCase)
                     lowercase__  =	False
         out_string += self.sp_model.decode(lowerCAmelCase)
         return out_string.strip()


   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      List[int]      ,					lowerCAmelCase	:      Optional[List[int]] = None)						->					List[int]:




         """simple docstring"""

         lowercase__  =	[self.sep_token_id]
         lowercase__  =	[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       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      List[int]      ,					lowerCAmelCase	:      Optional[List[int]] = None      ,					lowerCAmelCase	:      bool = False)						->					List[int]:




         """simple docstring"""

         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       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      List[int]      ,					lowerCAmelCase	:      Optional[List[int]] = None)						->					List[int]:




         """simple docstring"""

         lowercase__  =	[self.sep_token_id]
         lowercase__  =	[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       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Optional[str] = None)						->					Tuple[str]:




         """simple docstring"""

         if not os.path.isdir(lowerCAmelCase):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
               return
         lowercase__  =	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:
                     lowercase__  =	self.sp_model.serialized_model_proto()
                     fi.write(lowerCAmelCase)

         return (out_vocab_file,)
