





'''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      (			UpperCamelCase__							):
 def __init__(							self :str		,			lowercase_ :int		,			lowercase_ :str		,			lowercase_ :List[str]=10_24		,			lowercase_ :Optional[int]=10_24		,			lowercase_ :Any=3.6				)->				List[Any]:
     A__							=     tokenizer
     A__							=     tokenizer.bos_token_id
     A__							=     dataset
     A__							=     seq_length
     A__							=     seq_length * chars_per_token * num_of_sequences



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





def 		UpperCamelCase							(				_lowerCamelCase   :   Union[str, Any]			):
    A__							=     {"streaming": True}
    A__							=     load_dataset(args.dataset_name		,			split="train"		,			**_lowerCamelCase			)
    A__							=     ConstantLengthDataset(_lowerCamelCase		,			_lowerCamelCase		,			seq_length=args.seq_length			)
    A__							=     DataLoader(_lowerCamelCase		,			batch_size=args.batch_size			)
    return eval_dataloader





def 		UpperCamelCase							(				_lowerCamelCase   :   Any			):
    model.eval()
    A__							=     []
    for step, batch in enumerate(_lowerCamelCase			):
        with torch.no_grad():
            A__							=     model(_lowerCamelCase		,			labels=_lowerCamelCase			)
        A__							=     outputs.loss.repeat(args.batch_size			)
        losses.append(accelerator.gather(_lowerCamelCase			)			)

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


# Setup Accelerator
__lowerCAmelCase				:      str						      =Accelerator()

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

# Logging
__lowerCAmelCase				:      Optional[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				:      List[str]						      =AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__lowerCAmelCase				:      Optional[Any]						      =AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__lowerCAmelCase				:      List[Any]						      =create_dataloader(args)

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

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
__lowerCAmelCase      ,	__lowerCAmelCase				:      Tuple						      =evaluate(args)



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










'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
  from .pipeline_kandinsky import KandinskyPipeline
  from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
  from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
  from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
  from .text_encoder import MultilingualCLIP



