




"""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(					__lowercase			):







 '''simple docstring'''





 def __init__(    self:		Union[str, Any],				a_:		Optional[Any],				a_:		int,				a_:		Dict=1_024,				a_:		List[Any]=1_024,				a_:		Optional[Any]=3.6		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  tokenizer
   _snake_case							:		Tuple								=  tokenizer.bos_token_id
   _snake_case							:		Optional[int]								=  dataset
   _snake_case							:		int								=  seq_length
   _snake_case							:		Optional[Any]								=  seq_length * chars_per_token * num_of_sequences


 def __iter__(    self:		Optional[int]		):




   '''simple docstring'''



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







def  UpperCAmelCase__	(snake_case__							: str   ):
  """simple docstring"""

  _snake_case							:		Any								=  {"""streaming""": True}
  _snake_case							:		int								=  load_dataset(args.dataset_name		,       split="""train"""		,       **_lowerCamelCase   )
  _snake_case							:		Any								=  ConstantLengthDataset(_lowerCamelCase		,       _lowerCamelCase		,       seq_length=args.seq_length   )
  _snake_case							:		int								=  DataLoader(_lowerCamelCase		,       batch_size=args.batch_size   )
  return eval_dataloader


def  UpperCAmelCase__	(snake_case__							: List[Any]   ):
  """simple docstring"""

  model.eval()
  _snake_case							:		Dict								=  []
  for step, batch in enumerate(_lowerCamelCase   ):
    with torch.no_grad():
      _snake_case							:		Union[str, Any]								=  model(_lowerCamelCase		,       labels=_lowerCamelCase   )
    _snake_case							:		Tuple								=  outputs.loss.repeat(args.batch_size   )
    losses.append(accelerator.gather(_lowerCamelCase   )   )

    if args.max_eval_steps > 0 and step >= args.max_eval_steps:
      break
  _snake_case							:		Tuple								=  torch.mean(torch.cat(_lowerCamelCase   )   )
  try:
    _snake_case							:		Optional[Any]								=  torch.exp(_lowerCamelCase   )
  except OverflowError:
    _snake_case							:		Dict								=  float("""inf"""   )
  return loss.item(), perplexity.item()


# Setup Accelerator
A_      =    Accelerator()

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

# Logging
A_      =    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
A_      =    AutoModelForCausalLM.from_pretrained(args.model_ckpt)
A_      =    AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
A_      =    create_dataloader(args)

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

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






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








"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


A_      =    logging.get_logger(__name__)  # pylint: disable=invalid-name






class  lowercase(					__a			):







 '''simple docstring'''





 def __init__(    self:		Optional[int],				a_:		str,				a_:		Optional[Any]		):




   '''simple docstring'''



   super().__init__()
   self.register_modules(unet=a_,				scheduler=a_		)


 @torch.no_grad()
 def __call__(    self:		Any,				a_:		int = 1,				a_:		int = 100,				a_:		Optional[Union[torch.Generator, List[torch.Generator]]] = None,				a_:		Optional[float] = None,				a_:		bool = True,				):




   '''simple docstring'''



   if audio_length_in_s is None:
     _snake_case							:		Dict								=  self.unet.config.sample_size / self.unet.config.sample_rate

   _snake_case							:		Optional[int]								=  audio_length_in_s * self.unet.config.sample_rate

   _snake_case							:		int								=  2 ** len(self.unet.up_blocks		)
   if sample_size < 3 * down_scale_factor:
     raise ValueError(
         f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
         f" {3 * down_scale_factor / self.unet.config.sample_rate}."		)

   _snake_case							:		Union[str, Any]								=  int(a_		)
   if sample_size % down_scale_factor != 0:
     _snake_case							:		Optional[Any]								=  (
         (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
     ) * down_scale_factor
     logger.info(
         f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
         f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
         """ process."""		)
   _snake_case							:		str								=  int(a_		)

   _snake_case							:		int								=  next(iter(self.unet.parameters()		)		).dtype
   _snake_case							:		Optional[Any]								=  (batch_size, self.unet.config.in_channels, sample_size)
   if isinstance(a_,				a_		) and len(a_		) != batch_size:
     raise ValueError(
         f"You have passed a list of generators of length {len(a_		)}, but requested an effective batch"
         f" size of {batch_size}. Make sure the batch size matches the length of the generators."		)

   _snake_case							:		Optional[Any]								=  randn_tensor(a_,				generator=a_,				device=self.device,				dtype=a_		)

   # set step values
   self.scheduler.set_timesteps(a_,				device=audio.device		)
   _snake_case							:		Optional[int]								=  self.scheduler.timesteps.to(a_		)

   for t in self.progress_bar(self.scheduler.timesteps		):
     # 1. predict noise model_output
     _snake_case							:		str								=  self.unet(a_,				a_		).sample

     # 2. compute previous image: x_t -> t_t-1
     _snake_case							:		Optional[Any]								=  self.scheduler.step(a_,				a_,				a_		).prev_sample

   _snake_case							:		Tuple								=  audio.clamp(-1,				1		).float().cpu().numpy()

   _snake_case							:		Dict								=  audio[:, :, :original_sample_size]

   if not return_dict:
     return (audio,)

   return AudioPipelineOutput(audios=a_		)


