





'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


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

a__							:							List[Any] ={'''tokenizer_file''': '''tokenizer.json'''}

a__							:							Dict ={
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					Any					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=["input_ids", "attention_mask"]
 SCREAMING_SNAKE_CASE_	:					Any					=None






 def __init__(       self       :							Optional[Any]							,	__A       :							Any=None							,	__A       :							Optional[Any]=None							,	__A       :							Optional[Any]=None							,	__A       :							int="<unk>"							,	__A       :							int="<s>"							,	__A       :							Optional[int]="</s>"							,	__A       :							Union[str, Any]="<pad>"							,	__A       :							List[str]=False							,	__A       :							int=False							,	**__A       :							List[str]							,	):
        super().__init__(
            __A							,	__A							,	tokenizer_file=__A							,	unk_token=__A							,	bos_token=__A							,	eos_token=__A							,	pad_token=__A							,	add_prefix_space=__A							,	clean_up_tokenization_spaces=__A							,	**__A							,	)
        __UpperCamelCase       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get('add_prefix_space'							,	__A	) != add_prefix_space:
               __UpperCamelCase       =  getattr(__A							,	pre_tok_state.pop('type'	)	)
               __UpperCamelCase       =  add_prefix_space
               __UpperCamelCase       =  pre_tok_class(**__A	)

        __UpperCamelCase       =  add_prefix_space






 def        _lowerCamelCase					(       self       :							Dict							,	*__A       :							int							,	**__A       :							List[str]	):
        __UpperCamelCase       =  kwargs.get('is_split_into_words'							,	__A	)
        if not (self.add_prefix_space or not is_split_into_words):
               raise Exception(
                   f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
                   ' pretokenized inputs.'	)

        return super()._batch_encode_plus(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Any							,	*__A       :							List[str]							,	**__A       :							List[Any]	):
        __UpperCamelCase       =  kwargs.get('is_split_into_words'							,	__A	)

        if not (self.add_prefix_space or not is_split_into_words):
               raise Exception(
                   f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
                   ' pretokenized inputs.'	)

        return super()._encode_plus(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							str							,	__A       :							Optional[str] = None	):
        __UpperCamelCase       =  self._tokenizer.model.save(__A							,	name=__A	)
        return tuple(__A	)






 def        _lowerCamelCase					(       self       :							str							,	__A       :							"Conversation"	):
        __UpperCamelCase       =  []
        for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__A							,	add_special_tokens=__A	) + [self.eos_token_id]	)

        if len(__A	) > self.model_max_length:
               __UpperCamelCase       =  input_ids[-self.model_max_length :]
        return input_ids







'''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							Any							,	__A       :							Dict							,	__A       :							str							,	__A       :							List[Any]=1_0_2_4							,	__A       :							Tuple=1_0_2_4							,	__A       :							str=3.6	):
        __UpperCamelCase       =  tokenizer
        __UpperCamelCase       =  tokenizer.bos_token_id
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  seq_length
        __UpperCamelCase       =  seq_length * chars_per_token * num_of_sequences






 def __iter__(       self       :							Any	):
        __UpperCamelCase       =  iter(self.dataset	)
        __UpperCamelCase       =  True
        while more_examples:
               __UpperCamelCase       ,						__UpperCamelCase       =  [], 0
               while True:
                      if buffer_len >= self.input_characters:
                             break
                      try:
                             buffer.append(next(__A	)['content']	)
                             buffer_len += len(buffer[-1]	)
                      except StopIteration:
                             __UpperCamelCase       =  False
                             break
               __UpperCamelCase       =  tokenizer(__A							,	truncation=__A	)['input_ids']
               __UpperCamelCase       =  []
               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	):
                      __UpperCamelCase       =  all_token_ids[i : i + self.seq_length]
                      if len(__A	) == self.seq_length:
                             yield torch.tensor(__A	)






def 				lowercase__   (   __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  {'streaming': True}
       __UpperCamelCase       =  load_dataset(args.dataset_name				,  split='train'				,  **__lowercase     )
       __UpperCamelCase       =  ConstantLengthDataset(__lowercase				,  __lowercase				,  seq_length=args.seq_length     )
       __UpperCamelCase       =  DataLoader(__lowercase				,  batch_size=args.batch_size     )
       return eval_dataloader
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      Optional[Any]:



       """simple docstring"""






       model.eval()
       __UpperCamelCase       =  []
       for step, batch in enumerate(__lowercase     ):
              with torch.no_grad():
                     __UpperCamelCase       =  model(__lowercase				,  labels=__lowercase     )
              __UpperCamelCase       =  outputs.loss.repeat(args.batch_size     )
              losses.append(accelerator.gather(__lowercase     )     )

              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       __UpperCamelCase       =  torch.mean(torch.cat(__lowercase     )     )
       try:
              __UpperCamelCase       =  torch.exp(__lowercase     )
       except OverflowError:
              __UpperCamelCase       =  float('inf'     )
       return loss.item(), perplexity.item()


# Setup Accelerator
a__							:							int =Accelerator()

# Parse configuration
a__							:							Dict =HfArgumentParser(EvaluationArguments)
a__							:							Union[str, Any] =parser.parse_args()
set_seed(args.seed)

# Logging
a__							:							List[Any] =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
a__							:							Union[str, Any] =AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a__							:							List[Any] =AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a__							:							Union[str, Any] =create_dataloader(args)

# Prepare everything with our `accelerator`.
a__ , a__							:							List[str] =accelerator.prepare(model, eval_dataloader)

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


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