
"""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 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __init__(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__=1024  ,     snake_case__=1024  ,     snake_case__=3.6				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = tokenizer
       _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer.bos_token_id
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = dataset
       _SCREAMING_SNAKE_CASE   :				str							    = seq_length
       _SCREAMING_SNAKE_CASE   :				int							    = seq_length * chars_per_token * num_of_sequences




     def __iter__(     self				):




       """simple docstring"""


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





def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any]				)  ->    str:
  _SCREAMING_SNAKE_CASE   :				Tuple							    = {"streaming": True}
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = load_dataset(args.dataset_name,						split="train",						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = ConstantLengthDataset(lowerCamelCase__,						lowerCamelCase__,						seq_length=args.seq_length				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = DataLoader(lowerCamelCase__,						batch_size=args.batch_size				)
  return eval_dataloader





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

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


# Setup Accelerator
lowercase_       :						int     					=	Accelerator()

# Parse configuration
lowercase_       :						str     					=	HfArgumentParser(EvaluationArguments)
lowercase_       :						Dict     					=	parser.parse_args()
set_seed(args.seed)

# Logging
lowercase_       :						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
lowercase_       :						Any     					=	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowercase_       :						Any     					=	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowercase_       :						Dict     					=	create_dataloader(args)

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

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





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





"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be trained."""}  )
     A__      =			field(
         default="""./"""      ,       metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path of training dataset."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for training."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for evaluation."""}  )
     A__      =			field(default=0.1      ,       metadata={"""help""": """Value of weight decay."""}  )
     A__      =			field(
         default=10000      ,       metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}  )
     A__      =			field(default=2e-4      ,       metadata={"""help""": """Learning rate fo training."""}  )
     A__      =			field(default="""cosine"""      ,       metadata={"""help""": """Learning rate."""}  )
     A__      =			field(
         default=750      ,       metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}  )
     A__      =			field(
         default=16      ,       metadata={"""help""": """Number of gradient accumulation steps."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}  )
     A__      =			field(default=50000      ,       metadata={"""help""": """Maximum number of training steps."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Sequence lengths used for training."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Training seed."""}  )
     A__      =			field(
         default=1024      ,       metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True the data is pretokenized."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size used for evaluation."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Length of sequences to be evaluated."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Sample from the language model's output distribution."""}  )
     A__      =			field(default=0.2      ,       metadata={"""help""": """Sampling temperature used for generation."""}  )
     A__      =			field(default=256      ,       metadata={"""help""": """Maximum number of newly generated tokens."""}  )
     A__      =			field(default=0      ,       metadata={"""help""": """Top-k parameter used for generation."""}  )
     A__      =			field(default=0.95      ,       metadata={"""help""": """Top-p parameter used for nucleus sampling."""}  )
     A__      =			field(default=10      ,       metadata={"""help""": """Number of generations to run in parallel."""}  )
     A__      =			field(
         default=200      ,       metadata={"""help""": """Number of completions to generate for each sample."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""eval_results.json"""      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""0"""      ,       metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}  )
     A__      =			field(
         default=-1      ,       metadata={
             """help""": (
                 """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
                 """ number corresponds to which GPU device id to run on."""
             )
         }      ,       )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         }      ,       )
     A__      =			field(
         default="""transformersbook/codeparrot"""      ,       metadata={"""help""": """Folder or name of dataset to process."""}  )
     A__      =			field(
         default="""codeparrot-clean"""      ,       metadata={"""help""": """Folder to save processed processed dataset."""}  )
     A__      =			field(
         default=100000      ,       metadata={"""help""": """Number of files to save per JSON output file."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(
         default=1000      ,       metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=100      ,       metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.25      ,       metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}  )
     A__      =			field(
         default=1.5      ,       metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.7      ,       metadata={"""help""": """Probability for filtering config, test and uncommon files."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True, near-duplicate samples are removed."""}  )
     A__      =			field(
         default=0.85      ,       metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2"""      ,       metadata={"""help""": """Base tokenizer to build new tokenizer from."""}  )
     A__      =			field(
         default="""transformersbook/codeparrot-train"""      ,       metadata={"""help""": """Dataset to train tokenizer on."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(default=200000      ,       metadata={"""help""": """Number of examples to train tokenizer on."""}  )
     A__      =			field(
         default=32768      ,       metadata={"""help""": """Number of examples to train the tokenizer on."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of new tokenizer."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path to the dataset to pretokenize."""}  )
     A__      =			field(
         default="""tokenized-codeparrot-train"""      ,       metadata={"""help""": """Repo name of the pretokenized data."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2-large"""      ,       metadata={"""help""": """Configuration to use for model initialization."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Tokenizer attached to model."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of the created model."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )



