





'''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 							__lowercase    ( _lowerCAmelCase  ):







 '''simple docstring'''

 def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=1024       ,_lowerCamelCase=1024       ,_lowerCamelCase=3.6	)							->							str:







    '''simple docstring'''





    __lowercase			  =							tokenizer
    __lowercase			  =							tokenizer.bos_token_id
    __lowercase			  =							dataset
    __lowercase			  =							seq_length
    __lowercase			  =							seq_length * chars_per_token * num_of_sequences


 def __iter__(self	)							->							Tuple:







    '''simple docstring'''





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





def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]			):
   __lowercase			  =							{"""streaming""": True}
   __lowercase			  =							load_dataset(args.dataset_name						,    split='''train'''						,    **lowerCAmelCase__			)
   __lowercase			  =							ConstantLengthDataset(lowerCAmelCase__						,    lowerCAmelCase__						,    seq_length=args.seq_length			)
   __lowercase			  =							DataLoader(lowerCAmelCase__						,    batch_size=args.batch_size			)
   return eval_dataloader




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str			):
   model.eval()
   __lowercase			  =							[]
   for step, batch in enumerate(lowerCAmelCase__			):
      with torch.no_grad():
         __lowercase			  =							model(lowerCAmelCase__						,    labels=lowerCAmelCase__			)
      __lowercase			  =							outputs.loss.repeat(args.batch_size			)
      losses.append(accelerator.gather(lowerCAmelCase__			)			)

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


# Setup Accelerator
_SCREAMING_SNAKE_CASE   =      Accelerator()

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

# Logging
_SCREAMING_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
_SCREAMING_SNAKE_CASE   =      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_SCREAMING_SNAKE_CASE   =      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE   =      create_dataloader(args)

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

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

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












'''simple docstring'''



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[List[PIL.Image.Image], np.ndarray]
	a      :			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





