




'''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       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  List[Any]						)-> int:




     '''simple docstring'''





     __snake_case            =	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}}'''						)
     __snake_case            =	DatasetInfosDict.from_directory(_lowerCamelCase						)
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42







@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=42 ,						),
    ] ,						)
def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  DatasetInfo						)-> Any:




     '''simple docstring'''





     __snake_case            =	str(_lowerCamelCase						)
     dataset_info.write_to_directory(_lowerCamelCase						)
     __snake_case            =	DatasetInfo.from_directory(_lowerCamelCase						)
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(_lowerCamelCase ,						'''dataset_info.json'''						)						)







def       _UpperCamelCase			()-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	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''': 42}] ,						download_checksums={} ,						download_size=13_37 ,						post_processing_size=4_42 ,						dataset_size=12_34 ,						size_in_bytes=13_37 + 4_42 + 12_34 ,						)
     __snake_case            =	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)						)
     __snake_case            =	yaml.safe_dump(_lowerCamelCase						)
     __snake_case            =	yaml.safe_load(_lowerCamelCase						)
     assert dataset_info_yaml_dict == reloaded







def       _UpperCamelCase			()-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	DatasetInfo()
     __snake_case            =	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=42 ,						)
            }						),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42						),
                '''v2''': DatasetInfo(dataset_size=13_37						),
            }						),
    ] ,						)
def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  DatasetInfosDict						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	str(_lowerCamelCase						)
     dataset_infos_dict.write_to_directory(_lowerCamelCase						)
     __snake_case            =	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():
          __snake_case            =	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
          __snake_case            =	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'''						)						)














'''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    (		snake_case      ):




    def __init__(      self	:							Optional[Any]	,  a	:							Dict	,  a	:							int	,  a	:							Any=1_0_2_4	,  a	:							Tuple=1_0_2_4	,  a	:							Optional[int]=3.6      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  tokenizer
       lowercase_						:     Union[str, Any]							     =  tokenizer.bos_token_id
       lowercase_						:     Union[str, Any]							     =  dataset
       lowercase_						:     Optional[Any]							     =  seq_length
       lowercase_						:     Any							     =  seq_length * chars_per_token * num_of_sequences





    def __iter__(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  iter(self.dataset      )
       lowercase_						:     Dict							     =  True
       while more_examples:
          lowercase_     ,		lowercase_						:     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:
                lowercase_						:     int							     =  False
                break
          lowercase_						:     Any							     =  tokenizer(a	,  truncation=a      )["input_ids"]
          lowercase_						:     List[str]							     =  []
          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      ):
             lowercase_						:     List[Any]							     =  all_token_ids[i : i + self.seq_length]
             if len(a      ) == self.seq_length:
                yield torch.tensor(a      )




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




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

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   lowercase_						:     Optional[int]							     =  torch.mean(torch.cat(_UpperCamelCase				)				)
   try:
      lowercase_						:     Dict							     =  torch.exp(_UpperCamelCase				)
   except OverflowError:
      lowercase_						:     Dict							     =  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__,  UpperCamelCase__			=      accelerator.prepare(model, eval_dataloader)

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





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




