

"""simple docstring"""





from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


_a				   =							'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'




class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def __init__(    self							:    List[Any],      UpperCAmelCase__							:    UNetaDModel,      UpperCAmelCase__							:    CMStochasticIterativeScheduler			):
    super().__init__()

    self.register_modules(
        unet=UpperCAmelCase__,      scheduler=UpperCAmelCase__,      )

    __lowercase			=		None


 def       _lowercase				(    self							:    Any,      UpperCAmelCase__							:    Tuple=0			):
    if is_accelerate_available() and is_accelerate_version(">=",      "0.14.0"			):
       from accelerate import cpu_offload
    else:
       raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"			)

    __lowercase			=		torch.device(F"""cuda:{gpu_id}"""			)

    if self.device.type != "cpu":
       self.to("cpu",      silence_dtype_warnings=UpperCAmelCase__			)
       torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

    for cpu_offloaded_model in [self.unet]:
       cpu_offload(UpperCAmelCase__,      UpperCAmelCase__			)

    if self.safety_checker is not None:
       cpu_offload(self.safety_checker,      execution_device=UpperCAmelCase__,      offload_buffers=UpperCAmelCase__			)


 def       _lowercase				(    self							:    Tuple,      UpperCAmelCase__							:    str=0			):
    if is_accelerate_available() and is_accelerate_version(">=",      "0.17.0.dev0"			):
       from accelerate import cpu_offload_with_hook
    else:
       raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."			)

    __lowercase			=		torch.device(F"""cuda:{gpu_id}"""			)

    if self.device.type != "cpu":
       self.to("cpu",      silence_dtype_warnings=UpperCAmelCase__			)
       torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

    __lowercase			=		None
    for cpu_offloaded_model in [self.unet]:
       __lowercase	,__lowercase			=		cpu_offload_with_hook(UpperCAmelCase__,      UpperCAmelCase__,      prev_module_hook=UpperCAmelCase__			)

    if self.safety_checker is not None:
       __lowercase	,__lowercase			=		cpu_offload_with_hook(self.safety_checker,      UpperCAmelCase__,      prev_module_hook=UpperCAmelCase__			)

    # We'll offload the last model manually.
    __lowercase			=		hook


 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def       _lowercase				(    self							:    Tuple			):
    if not hasattr(self.unet,      "_hf_hook"			):
       return self.device
    for module in self.unet.modules():
       if (
           hasattr(UpperCAmelCase__,      "_hf_hook"			)
           and hasattr(module._hf_hook,      "execution_device"			)
           and module._hf_hook.execution_device is not None
       ):
          return torch.device(module._hf_hook.execution_device			)
    return self.device


 def       _lowercase				(    self							:    Union[str, Any],      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    Tuple,      UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    List[str],      UpperCAmelCase__							:    Any,      UpperCAmelCase__							:    Any,      UpperCAmelCase__							:    Any=None			):
    __lowercase			=		(batch_size, num_channels, height, width)
    if isinstance(UpperCAmelCase__,      UpperCAmelCase__			) and len(UpperCAmelCase__			) != batch_size:
       raise ValueError(
           F"""You have passed a list of generators of length {len(UpperCAmelCase__			)}, but requested an effective batch"""
           F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""			)

    if latents is None:
       __lowercase			=		randn_tensor(UpperCAmelCase__,      generator=UpperCAmelCase__,      device=UpperCAmelCase__,      dtype=UpperCAmelCase__			)
    else:
       __lowercase			=		latents.to(device=UpperCAmelCase__,      dtype=UpperCAmelCase__			)

    # scale the initial noise by the standard deviation required by the scheduler
    __lowercase			=		latents * self.scheduler.init_noise_sigma
    return latents


 def       _lowercase				(    self							:    int,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    str = "pil"			):
    if output_type not in ["pt", "np", "pil"]:
       raise ValueError(
           F"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""			)

    # Equivalent to diffusers.VaeImageProcessor.denormalize
    __lowercase			=		(sample / 2 + 0.5).clamp(0,      1			)
    if output_type == "pt":
       return sample

    # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
    __lowercase			=		sample.cpu().permute(0,      2,      3,      1			).numpy()
    if output_type == "np":
       return sample

    # Output_type must be 'pil'
    __lowercase			=		self.numpy_to_pil(UpperCAmelCase__			)
    return sample


 def       _lowercase				(    self							:    Tuple,      UpperCAmelCase__							:    Tuple,      UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    Union[str, Any]=None			):
    if self.unet.config.num_class_embeds is not None:
       if isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
          __lowercase			=		torch.tensor(UpperCAmelCase__,      dtype=torch.int			)
       elif isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
          assert batch_size == 1, "Batch size must be 1 if classes is an int"
          __lowercase			=		torch.tensor([class_labels],      dtype=torch.int			)
       elif class_labels is None:
          # Randomly generate batch_size class labels
          # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
          __lowercase			=		torch.randint(0,      self.unet.config.num_class_embeds,      size=(batch_size,)			)
       __lowercase			=		class_labels.to(UpperCAmelCase__			)
    else:
       __lowercase			=		None
    return class_labels


 def       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    Optional[Any],      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    List[Any],      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    List[Any]			):
    if num_inference_steps is None and timesteps is None:
       raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."			)

    if num_inference_steps is not None and timesteps is not None:
       logger.warning(
           F"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
           " `timesteps` will be used over `num_inference_steps`."			)

    if latents is not None:
       __lowercase			=		(batch_size, 3, img_size, img_size)
       if latents.shape != expected_shape:
          raise ValueError(F"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""			)

    if (callback_steps is None) or (
        callback_steps is not None and (not isinstance(UpperCAmelCase__,      UpperCAmelCase__			) or callback_steps <= 0)
    ):
       raise ValueError(
           F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
           F""" {type(UpperCAmelCase__			)}."""			)



 @torch.no_grad()
 @replace_example_docstring(UpperCAmelCase__			)
 def __call__(    self							:    Any,      UpperCAmelCase__							:    int = 1,      UpperCAmelCase__							:    Optional[Union[torch.Tensor, List[int], int]] = None,      UpperCAmelCase__							:    int = 1,      UpperCAmelCase__							:    List[int] = None,      UpperCAmelCase__							:    Optional[Union[torch.Generator, List[torch.Generator]]] = None,      UpperCAmelCase__							:    Optional[torch.FloatTensor] = None,      UpperCAmelCase__							:    Optional[str] = "pil",      UpperCAmelCase__							:    bool = True,      UpperCAmelCase__							:    Optional[Callable[[int, int, torch.FloatTensor], None]] = None,      UpperCAmelCase__							:    int = 1,      ):
    __lowercase			=		self.unet.config.sample_size
    __lowercase			=		self._execution_device

    # 1. Check inputs
    self.check_inputs(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)

    # 2. Prepare image latents
    # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
    __lowercase			=		self.prepare_latents(
        batch_size=UpperCAmelCase__,      num_channels=self.unet.config.in_channels,      height=UpperCAmelCase__,      width=UpperCAmelCase__,      dtype=self.unet.dtype,      device=UpperCAmelCase__,      generator=UpperCAmelCase__,      latents=UpperCAmelCase__,      )

    # 3. Handle class_labels for class-conditional models
    __lowercase			=		self.prepare_class_labels(UpperCAmelCase__,      UpperCAmelCase__,      class_labels=UpperCAmelCase__			)

    # 4. Prepare timesteps
    if timesteps is not None:
       self.scheduler.set_timesteps(timesteps=UpperCAmelCase__,      device=UpperCAmelCase__			)
       __lowercase			=		self.scheduler.timesteps
       __lowercase			=		len(UpperCAmelCase__			)
    else:
       self.scheduler.set_timesteps(UpperCAmelCase__			)
       __lowercase			=		self.scheduler.timesteps

    # 5. Denoising loop
    # Multistep sampling: implements Algorithm 1 in the paper
    with self.progress_bar(total=UpperCAmelCase__			) as progress_bar:
       for i, t in enumerate(UpperCAmelCase__			):
          __lowercase			=		self.scheduler.scale_model_input(UpperCAmelCase__,      UpperCAmelCase__			)
          __lowercase			=		self.unet(UpperCAmelCase__,      UpperCAmelCase__,      class_labels=UpperCAmelCase__,      return_dict=UpperCAmelCase__			)[0]

          __lowercase			=		self.scheduler.step(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      generator=UpperCAmelCase__			)[0]

          # call the callback, if provided
          progress_bar.update()
          if callback is not None and i % callback_steps == 0:
             callback(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)

        # 6. Post-process image sample
    __lowercase			=		self.postprocess_image(UpperCAmelCase__,      output_type=UpperCAmelCase__			)

    # Offload last model to CPU
    if hasattr(self,      "final_offload_hook"			) and self.final_offload_hook is not None:
       self.final_offload_hook.offload()

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=UpperCAmelCase__			)







"""simple docstring"""





import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
   print('#################### AVAILABLE DEVICES ####################')
   print(jax.devices())
   print('###########################################################')

   # setup for wandb sweep
   _a				   =							Args()
   _a				   =							wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _a				   =							dict(logger.config)
   del wandb_args["batch_size"]
   _a				   =							replace(args, **wandb_args)
   _a				   =							args.base_dir + '-' + wandb.run.id
   _a				   =							replace(args, base_dir=base_dir)
   print(args)

   _a				   =							load_dataset('json', data_files=args.tr_data_path)['train']
   _a				   =							load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _a				   =							range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _a				   =							tr_dataset.shuffle().select(indices)
   _a				   =							range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _a				   =							val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _a				   =							tr_dataset.shuffle().select(range(8_00_00))
      _a				   =							val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   _a				   =							FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _a				   =							BigBirdTokenizerFast.from_pretrained(args.model_id)
   _a				   =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   _a				   =							{
       'lr': args.lr,
       'init_lr': args.init_lr,
       'warmup_steps': args.warmup_steps,
       'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
       'weight_decay': args.weight_decay,
   }
   _a   ,		_a				   =							build_tx(**tx_args)

   _a				   =							Trainer(
       args=args,
       data_collator=data_collator,
       model_save_fn=model.save_pretrained,
       train_step_fn=train_step,
       val_step_fn=val_step,
       logger=logger,
       scheduler_fn=lr,
   )

   _a				   =							None
   _a				   =							trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
   try:
      trainer.train(state, tr_dataset, val_dataset)
   except KeyboardInterrupt:
      print('Oooops; TRAINING STOPPED UNFORTUNATELY')

   print('SAVING WEIGHTS IN `final-weights`')
   _a				   =							jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)




