






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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	Dict					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[Any]=1024					, UpperCAmelCase_       :	Optional[Any]=1024					, UpperCAmelCase_       :	Tuple=3.6      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    tokenizer
       SCREAMING_SNAKE_CASE       :      List[str]							=    tokenizer.bos_token_id
       SCREAMING_SNAKE_CASE       :      str							=    dataset
       SCREAMING_SNAKE_CASE       :      List[Any]							=    seq_length
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    seq_length * chars_per_token * num_of_sequences






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





def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






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


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






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

    if args.max_eval_steps > 0 and step >= args.max_eval_steps:
      break
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.mean(torch.cat(lowercase				)				)
  try:
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.exp(lowercase				)
  except OverflowError:
    SCREAMING_SNAKE_CASE       :      Dict							=    float("inf"				)
  return loss.item(), perplexity.item()


# Setup Accelerator
snake_case									=		Accelerator()

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

# Logging
snake_case									=		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
snake_case									=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
snake_case									=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
snake_case									=		create_dataloader(args)

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

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

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














from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	'''camembert'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Optional[Any]=3_0522					, UpperCAmelCase_       :	Optional[int]=768					, UpperCAmelCase_       :	List[Any]=12					, UpperCAmelCase_       :	Union[str, Any]=12					, UpperCAmelCase_       :	Optional[Any]=3072					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	List[Any]=0.1					, UpperCAmelCase_       :	Dict=0.1					, UpperCAmelCase_       :	List[str]=512					, UpperCAmelCase_       :	List[Any]=2					, UpperCAmelCase_       :	Optional[int]=0.02					, UpperCAmelCase_       :	List[str]=1E-12					, UpperCAmelCase_       :	int=1					, UpperCAmelCase_       :	Union[str, Any]=0					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	int="absolute"					, UpperCAmelCase_       :	Tuple=True					, UpperCAmelCase_       :	Optional[Any]=None					, **UpperCAmelCase_       :	Optional[int]					, ):
       super().__init__(pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    vocab_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Any							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Dict							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Dict							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Dict							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      List[Any]							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      Dict							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[Any]							=    use_cache
       SCREAMING_SNAKE_CASE       :      str							=    classifier_dropout





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	Optional[int]      ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]      )






