





'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__							:							List[str] ={
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Any =[
        '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''BigBirdPegasusForCausalLM''',
        '''BigBirdPegasusForConditionalGeneration''',
        '''BigBirdPegasusForQuestionAnswering''',
        '''BigBirdPegasusForSequenceClassification''',
        '''BigBirdPegasusModel''',
        '''BigBirdPegasusPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    a__							:							str =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''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}')
