
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_A  					= 16
_A  					= 32



def 						lowercase_			(   A__ ,		A__		)							->							Optional[Any]:
    """simple docstring"""

    if args.with_tracking:
        snake_case      =					Accelerator(
            cpu=args.cpu ,		mixed_precision=args.mixed_precision ,		log_with="all" ,		project_dir=args.project_dir		)
    else:
        snake_case      =					Accelerator(cpu=args.cpu ,		mixed_precision=args.mixed_precision		)

    if hasattr(args.checkpointing_steps ,		"isdigit"		):
        if args.checkpointing_steps == "epoch":
            snake_case      =					args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            snake_case      =					int(args.checkpointing_steps		)
        else:
            raise ValueError(
                F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'		)
    else:
        snake_case      =					None
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case      =					config["lr"]
    snake_case      =					int(config["num_epochs"]		)
    snake_case      =					int(config["seed"]		)
    snake_case      =					int(config["batch_size"]		)

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        snake_case      =					os.path.split(A__		)[-1].split("."		)[0]
        accelerator.init_trackers(A__ ,		A__		)

    snake_case      =					AutoTokenizer.from_pretrained("bert-base-cased"		)
    snake_case      =					load_dataset("glue" ,		"mrpc"		)
    snake_case      =					evaluate.load("glue" ,		"mrpc"		)

    def tokenize_function(A__		):
        # max_length=None => use the model max length (it's actually the default)
        snake_case      =					tokenizer(examples["sentence1"] ,		examples["sentence2"] ,		truncation=A__ ,		max_length=A__		)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        snake_case      =					datasets.map(
            A__ ,		batched=A__ ,		remove_columns=["idx", "sentence1", "sentence2"] ,		)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case      =					tokenized_datasets.rename_column("label" ,		"labels"		)

    # If the batch size is too big we use gradient accumulation
    snake_case      =					1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        snake_case      =					batch_size // MAX_GPU_BATCH_SIZE
        snake_case      =					MAX_GPU_BATCH_SIZE

    def collate_fn(A__		):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case      =					128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            snake_case      =					16
        elif accelerator.mixed_precision != "no":
            snake_case      =					8
        else:
            snake_case      =					None

        return tokenizer.pad(
            A__ ,		padding="longest" ,		max_length=A__ ,		pad_to_multiple_of=A__ ,		return_tensors="pt" ,		)

    # Instantiate dataloaders.
    snake_case      =					DataLoader(
        tokenized_datasets["train"] ,		shuffle=A__ ,		collate_fn=A__ ,		batch_size=A__		)
    snake_case      =					DataLoader(
        tokenized_datasets["validation"] ,		shuffle=A__ ,		collate_fn=A__ ,		batch_size=A__		)

    set_seed(A__		)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case      =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,		return_dict=A__		)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    snake_case      =					model.to(accelerator.device		)

    # Instantiate optimizer
    snake_case      =					AdamW(params=model.parameters() ,		lr=A__		)

    # Instantiate scheduler
    snake_case      =					get_linear_schedule_with_warmup(
        optimizer=A__ ,		num_warmup_steps=100 ,		num_training_steps=(len(A__		) * num_epochs) // gradient_accumulation_steps ,		)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    snake_case     ,							snake_case     ,							snake_case     ,							snake_case     ,							snake_case      =					accelerator.prepare(
        A__ ,		A__ ,		A__ ,		A__ ,		A__		)

    # We need to keep track of how many total steps we have iterated over
    snake_case      =					0
    # We also need to keep track of the stating epoch so files are named properly
    snake_case      =					0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'		)
            accelerator.load_state(args.resume_from_checkpoint		)
            snake_case      =					os.path.basename(args.resume_from_checkpoint		)
        else:
            # Get the most recent checkpoint
            snake_case      =					[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
            dirs.sort(key=os.path.getctime		)
            snake_case      =					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        snake_case      =					os.path.splitext(A__		)[0]

        if "epoch" in training_difference:
            snake_case      =					int(training_difference.replace("epoch_" ,		""		)		) + 1
            snake_case      =					None
        else:
            snake_case      =					int(training_difference.replace("step_" ,		""		)		)
            snake_case      =					resume_step // len(A__		)
            resume_step -= starting_epoch * len(A__		)

    # Now we train the model
    for epoch in range(A__ ,		A__		):
        model.train()
        if args.with_tracking:
            snake_case      =					0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            snake_case      =					accelerator.skip_first_batches(A__ ,		A__		)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            snake_case      =					train_dataloader
        for step, batch in enumerate(A__		):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device		)
            snake_case      =					model(**A__		)
            snake_case      =					outputs.loss
            snake_case      =					loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(A__		)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(A__ ,		A__		):
                snake_case      =					F'step_{overall_step}'
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        snake_case      =					os.path.join(args.output_dir ,		A__		)
                    accelerator.save_state(A__		)

        model.eval()
        for step, batch in enumerate(A__		):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device		)
            with torch.no_grad():
                snake_case      =					model(**A__		)
            snake_case      =					outputs.logits.argmax(dim=-1		)
            snake_case     ,							snake_case      =					accelerator.gather_for_metrics((predictions, batch["labels"])		)
            metric.add_batch(
                predictions=A__ ,		references=A__ ,		)

        snake_case      =					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:' ,		A__		)
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(A__		),
                    "epoch": epoch,
                } ,		step=A__ ,		)

        if checkpointing_steps == "epoch":
            snake_case      =					F'epoch_{epoch}'
            if args.output_dir is not None:
                snake_case      =					os.path.join(args.output_dir ,		A__		)
            accelerator.save_state(A__		)

    if args.with_tracking:
        accelerator.end_training()



def 						lowercase_			(   )							->							Any:
    """simple docstring"""

    snake_case      =					argparse.ArgumentParser(description="Simple example of training script."		)
    parser.add_argument(
        "--mixed_precision" ,		type=A__ ,		default=A__ ,		choices=["no", "fp16", "bf16", "fp8"] ,		help="Whether to use mixed precision. Choose"
        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
        "and an Nvidia Ampere GPU." ,		)
    parser.add_argument("--cpu" ,		action="store_true" ,		help="If passed, will train on the CPU."		)
    parser.add_argument(
        "--checkpointing_steps" ,		type=A__ ,		default=A__ ,		help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." ,		)
    parser.add_argument(
        "--resume_from_checkpoint" ,		type=A__ ,		default=A__ ,		help="If the training should continue from a checkpoint folder." ,		)
    parser.add_argument(
        "--with_tracking" ,		action="store_true" ,		help="Whether to load in all available experiment trackers from the environment and use them for logging." ,		)
    parser.add_argument(
        "--output_dir" ,		type=A__ ,		default="." ,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,		)
    parser.add_argument(
        "--project_dir" ,		type=A__ ,		default="logs" ,		help="Location on where to store experiment tracking logs` and relevent project information" ,		)
    snake_case      =					parser.parse_args()
    snake_case      =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(A__ ,		A__		)


if __name__ == "__main__":
   main()



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		(  A_  ):





     def __init__(self      :    Tuple				,    _A      :    UNetaDModel				,    _A      :    CMStochasticIterativeScheduler   )						->						None:
         super().__init__()

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

         snake_case      =					None





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    List[str]=0   )						->						Any:
         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"   )

         snake_case      =					torch.device(f'cuda:{gpu_id}'   )

         if self.device.type != "cpu":
             self.to("cpu"				,    silence_dtype_warnings=_A   )
             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(_A				,    _A   )

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





     def 		UpperCAmelCase(self      :    str				,    _A      :    Optional[Any]=0   )						->						Union[str, Any]:
         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."   )

         snake_case      =					torch.device(f'cuda:{gpu_id}'   )

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

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

         if self.safety_checker is not None:
             snake_case     ,							snake_case      =					cpu_offload_with_hook(self.safety_checker				,    _A				,    prev_module_hook=_A   )

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





     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 		UpperCAmelCase(self      :    Dict   )						->						Any:
         if not hasattr(self.unet				,    "_hf_hook"   ):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(_A				,    "_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 		UpperCAmelCase(self      :    Dict				,    _A      :    List[str]				,    _A      :    Tuple				,    _A      :    List[str]				,    _A      :    List[Any]				,    _A      :    Dict				,    _A      :    List[str]				,    _A      :    List[str]				,    _A      :    Optional[Any]=None   )						->						Tuple:
         snake_case      =					(batch_size, num_channels, height, width)
         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.'   )

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

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





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    torch.FloatTensor				,    _A      :    str = "pil"   )						->						str:
         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
         snake_case      =					(sample / 2 + 0.5).clamp(0				,    1   )
         if output_type == "pt":
             return sample

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

         # Output_type must be 'pil'
         snake_case      =					self.numpy_to_pil(_A   )
         return sample





     def 		UpperCAmelCase(self      :    str				,    _A      :    List[Any]				,    _A      :    Optional[Any]				,    _A      :    List[Any]=None   )						->						List[str]:
         if self.unet.config.num_class_embeds is not None:
             if isinstance(_A				,    _A   ):
                 snake_case      =					torch.tensor(_A				,    dtype=torch.int   )
             elif isinstance(_A				,    _A   ):
                 assert batch_size == 1, "Batch size must be 1 if classes is an int"
                 snake_case      =					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
                 snake_case      =					torch.randint(0				,    self.unet.config.num_class_embeds				,    size=(batch_size,)   )
             snake_case      =					class_labels.to(_A   )
         else:
             snake_case      =					None
         return class_labels





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    Tuple				,    _A      :    Union[str, Any]				,    _A      :    Dict				,    _A      :    Union[str, Any]				,    _A      :    Any				,    _A      :    str   )						->						Optional[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:
             snake_case      =					(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(_A				,    _A   ) or callback_steps <= 0)
         ):
             raise ValueError(
                 f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                 f' {type(_A   )}.'   )





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

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

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

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

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

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

                 snake_case      =					self.scheduler.step(_A				,    _A				,    _A				,    generator=_A   )[0]

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

        # 6. Post-process image sample
         snake_case      =					self.postprocess_image(_A				,    output_type=_A   )

         # 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=_A   )

