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




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tokenizer
       _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer.bos_token_id
       _SCREAMING_SNAKE_CASE   :				List[str]							    = dataset
       _SCREAMING_SNAKE_CASE   :				int							    = seq_length
       _SCREAMING_SNAKE_CASE   :				Dict							    = seq_length * chars_per_token * num_of_sequences




     def __iter__(     self				):




       """simple docstring"""


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





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





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

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


# Setup Accelerator
lowercase_       :						Any     					=	Accelerator()

# Parse configuration
lowercase_       :						List[Any]     					=	HfArgumentParser(EvaluationArguments)
lowercase_       :						List[Any]     					=	parser.parse_args()
set_seed(args.seed)

# Logging
lowercase_       :						Dict     					=	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_       :						Union[str, Any]     					=	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowercase_       :						Optional[int]     					=	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowercase_       :						Optional[Any]     					=	create_dataloader(args)

# Prepare everything with our `accelerator`.
lowercase_					,	lowercase_       :						int     					=	accelerator.prepare(model, eval_dataloader)

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





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





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    "files",						[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tmp_path_factory.mktemp("dset_infos_dir"				)
  if "full:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write("---\ndataset_info:\n  dataset_size: 42\n---"				)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write(""				)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / "dataset_infos.json",						"w"				) as f:
      f.write("{\"default\": {\"dataset_size\": 42}}"				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = DatasetInfosDict.from_directory(lowerCamelCase__				)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2





@pytest.mark.parametrize(
    "dataset_info",						[
        DatasetInfo(),
        DatasetInfo(
            description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							DatasetInfo				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = str(lowerCamelCase__				)
  dataset_info.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = DatasetInfo.from_directory(lowerCamelCase__				)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(lowerCamelCase__,						"dataset_info.json"				)				)





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfo(
      description="foo",						citation="bar",						homepage="https://foo.bar",						license="CC0",						features=Features({"a": Value("int32"				)}				),						post_processed={},						supervised_keys=(),						task_templates=[],						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train", "num_examples": 4_2}],						download_checksums={},						download_size=1_3_3_7,						post_processing_size=4_4_2,						dataset_size=1_2_3_4,						size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4,						)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = dataset_info._to_yaml_dict()
  assert sorted(lowerCamelCase__				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key],						(list, dict, int, str)				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = yaml.safe_dump(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = yaml.safe_load(lowerCamelCase__				)
  assert dataset_info_yaml_dict == reloaded





def    _lowerCAmelCase    (     )  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo()
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    "dataset_infos_dict",						[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}				),
        DatasetInfosDict({"my_config_name": DatasetInfo()}				),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						)
            }				),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=4_2				),
                "v2": DatasetInfo(dataset_size=1_3_3_7				),
            }				),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							DatasetInfosDict				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = str(lowerCamelCase__				)
  dataset_infos_dict.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfosDict.from_directory(lowerCamelCase__				)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(lowerCamelCase__,						"README.md"				)				)



