





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 					lowercase__ (			SCREAMING_SNAKE_CASE_       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__,	__magic_name__=1024,	__magic_name__=1024,	__magic_name__=3.6							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   tokenizer
       UpperCamelCase__							:       int      =   tokenizer.bos_token_id
       UpperCamelCase__							:       List[Any]      =   dataset
       UpperCamelCase__							:       Tuple      =   seq_length
       UpperCamelCase__							:       Any      =   seq_length * chars_per_token * num_of_sequences




   def __iter__(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   iter(self.dataset							)
       UpperCamelCase__							:       Tuple      =   True
       while more_examples:
           UpperCamelCase__							:       Optional[Any]      =   [], 0
           while True:
               if buffer_len >= self.input_characters:
                   break
               try:
                   buffer.append(next(UpperCamelCase__							)['''content''']							)
                   buffer_len += len(buffer[-1]							)
               except StopIteration:
                   UpperCamelCase__							:       Dict      =   False
                   break
           UpperCamelCase__							:       str      =   tokenizer(UpperCamelCase__,	truncation=UpperCamelCase__							)['''input_ids''']
           UpperCamelCase__							:       Optional[int]      =   []
           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							):
               UpperCamelCase__							:       Optional[int]      =   all_token_ids[i : i + self.seq_length]
               if len(UpperCamelCase__							) == self.seq_length:
                   yield torch.tensor(UpperCamelCase__							)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[Any]   )   ->			Any:
    UpperCamelCase__							:       Any      =   {'''streaming''': True}
    UpperCamelCase__							:       List[str]      =   load_dataset(args.dataset_name	,     split='''train'''	,     **__UpperCamelCase   )
    UpperCamelCase__							:       List[str]      =   ConstantLengthDataset(__UpperCamelCase	,     __UpperCamelCase	,     seq_length=args.seq_length   )
    UpperCamelCase__							:       int      =   DataLoader(__UpperCamelCase	,     batch_size=args.batch_size   )
    return eval_dataloader




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]   )   ->			Dict:
    model.eval()
    UpperCamelCase__							:       Dict      =   []
    for step, batch in enumerate(__UpperCamelCase   ):
        with torch.no_grad():
            UpperCamelCase__							:       Any      =   model(__UpperCamelCase	,     labels=__UpperCamelCase   )
        UpperCamelCase__							:       Tuple      =   outputs.loss.repeat(args.batch_size   )
        losses.append(accelerator.gather(__UpperCamelCase   )   )

        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    UpperCamelCase__							:       Tuple      =   torch.mean(torch.cat(__UpperCamelCase   )   )
    try:
        UpperCamelCase__							:       Optional[Any]      =   torch.exp(__UpperCamelCase   )
    except OverflowError:
        UpperCamelCase__							:       Union[str, 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 logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCAmelCase_  							=				logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__       )
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							bool     	=	field(default=__lowerCamelCase    ,       metadata={"help": "Whether to use SortishSampler or not."}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[Union[str, Path, GenerationConfig]]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }    ,       )




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   super().to_dict()
       for k, v in d.items():
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Tuple      =   v.to_dict()
       return d

