'''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 							a__( lowerCamelCase__     ):







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Dict=10_24						,      __snake_case			:  Dict=10_24						,      __snake_case			:  Optional[int]=3.6     ):
      a						: int					       =					tokenizer
      a						: Optional[int]					       =					tokenizer.bos_token_id
      a						: Dict					       =					dataset
      a						: Union[str, Any]					       =					seq_length
      a						: Tuple					       =					seq_length * chars_per_token * num_of_sequences







    def __iter__(				self			:  Optional[int]     ):
      a						: Tuple					       =					iter(self.dataset     )
      a						: Union[str, Any]					       =					True
      while more_examples:
        a						: List[Any]					       =					[], 0
        while True:
          if buffer_len >= self.input_characters:
            break
          try:
            buffer.append(next(__snake_case     )['content']     )
            buffer_len += len(buffer[-1]     )
          except StopIteration:
            a						: Dict					       =					False
            break
        a						: Dict					       =					tokenizer(__snake_case						,      truncation=__snake_case     )['input_ids']
        a						: int					       =					[]
        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     ):
          a						: str					       =					all_token_ids[i : i + self.seq_length]
          if len(__snake_case     ) == self.seq_length:
            yield torch.tensor(__snake_case     )
def 				lowerCamelCase__	(						_A    ):
  a						: Union[str, Any]					       =					{'streaming': True}
  a						: List[Any]					       =					load_dataset(args.dataset_name			,      split='train'			,      **_A    )
  a						: Optional[Any]					       =					ConstantLengthDataset(_A			,      _A			,      seq_length=args.seq_length    )
  a						: Union[str, Any]					       =					DataLoader(_A			,      batch_size=args.batch_size    )
  return eval_dataloader
def 				lowerCamelCase__	(						_A    ):
  model.eval()
  a						: str					       =					[]
  for step, batch in enumerate(_A    ):
    with torch.no_grad():
      a						: Optional[int]					       =					model(_A			,      labels=_A    )
    a						: List[Any]					       =					outputs.loss.repeat(args.batch_size    )
    losses.append(accelerator.gather(_A    )    )

    if args.max_eval_steps > 0 and step >= args.max_eval_steps:
      break
  a						: str					       =					torch.mean(torch.cat(_A    )    )
  try:
    a						: Dict					       =					torch.exp(_A    )
  except OverflowError:
    a						: Dict					       =					float('inf'    )
  return loss.item(), perplexity.item()


# Setup Accelerator
lowerCAmelCase: Union[str, Any]							=						Accelerator()

# Parse configuration
lowerCAmelCase: str							=						HfArgumentParser(EvaluationArguments)
lowerCAmelCase: Optional[int]							=						parser.parse_args()
set_seed(args.seed)

# Logging
lowerCAmelCase: 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
lowerCAmelCase: int							=						AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowerCAmelCase: Union[str, Any]							=						AutoTokenizer.from_pretrained(args.model_ckpt)

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

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

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
lowerCAmelCase: Optional[int]							=						evaluate(args)

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




'''simple docstring'''






import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 							a__( lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   CTRLTokenizer
    lowercase__			=   False
    lowercase__			=   False







    def       lowercase_	(				self			:  Dict     ):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a						: Tuple					       =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
      a						: Union[str, Any]					       =					dict(zip(__snake_case						,      range(len(__snake_case     )     )     )     )
      a						: Union[str, Any]					       =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
      a						: Optional[Any]					       =					{'unk_token': '<unk>'}

      a						: Dict					       =					os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['vocab_file']     )
      a						: Optional[Any]					       =					os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['merges_file']     )
      with open(self.vocab_file						,      'w'						,      encoding='utf-8'     ) as fp:
        fp.write(json.dumps(__snake_case     ) + '\n'     )
      with open(self.merges_file						,      'w'						,      encoding='utf-8'     ) as fp:
        fp.write('\n'.join(__snake_case     )     )







    def       lowercase_	(				self			:  int						,      **__snake_case			:  str     ):
      kwargs.update(self.special_tokens_map     )
      return CTRLTokenizer.from_pretrained(self.tmpdirname						,      **__snake_case     )







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Any     ):
      a						: int					       =					'adapt react readapt apt'
      a						: Any					       =					'adapt react readapt apt'
      return input_text, output_text







    def       lowercase_	(				self			:  Dict     ):
      a						: Dict					       =					CTRLTokenizer(self.vocab_file						,      self.merges_file						,      **self.special_tokens_map     )
      a						: List[str]					       =					'adapt react readapt apt'
      a						: Dict					       =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
      a						: Any					       =					tokenizer.tokenize(__snake_case     )
      self.assertListEqual(__snake_case						,      __snake_case     )

      a						: Dict					       =					tokens + [tokenizer.unk_token]

      a						: Optional[Any]					       =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case     )						,      __snake_case     )