
"""simple docstring"""
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
# and perform gradient accumulation
#
# 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)
#
# 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
#
########################################################################


lowercase_       :						Optional[Any]     					=	16
lowercase_       :						Tuple     					=	32





def    _lowerCAmelCase    (     lowerCamelCase__							:							Accelerator,						lowerCamelCase__							:							int = 1_6				)  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = AutoTokenizer.from_pretrained("bert-base-cased"				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = load_dataset("glue",						"mrpc"				)

  def tokenize_function(lowerCamelCase__							:							Tuple				):
    # max_length=None => use the model max length (it's actually the default)
    _SCREAMING_SNAKE_CASE   :				Any							    = tokenizer(examples["sentence1"],						examples["sentence2"],						truncation=lowerCamelCase__,						max_length=lowerCamelCase__				)
    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():
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = datasets.map(
        lowerCamelCase__,						batched=lowerCamelCase__,						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
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokenized_datasets.rename_column("label",						"labels"				)

  def collate_fn(lowerCamelCase__							:							Optional[Any]				):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 1_6
    elif accelerator.mixed_precision != "no":
      _SCREAMING_SNAKE_CASE   :				List[str]							    = 8
    else:
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = None

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

  # Instantiate dataloaders.
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = DataLoader(
      tokenized_datasets["train"],						shuffle=lowerCamelCase__,						collate_fn=lowerCamelCase__,						batch_size=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = DataLoader(
      tokenized_datasets["validation"],						shuffle=lowerCamelCase__,						collate_fn=lowerCamelCase__,						batch_size=lowerCamelCase__				)

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      lowercase_       :						Any     					=	mocked_dataloaders  # noqa: F811





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							Optional[Any]				)  ->    List[Any]:
  # For testing only
  if os.environ.get("TESTING_MOCKED_DATALOADERS",						lowerCamelCase__				) == "1":
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 2
  # New Code #
  _SCREAMING_SNAKE_CASE   :				Any							    = int(args.gradient_accumulation_steps				)
  # Initialize accelerator
  _SCREAMING_SNAKE_CASE   :				str							    = Accelerator(
      cpu=args.cpu,						mixed_precision=args.mixed_precision,						gradient_accumulation_steps=lowerCamelCase__				)
  if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
    raise NotImplementedError(
        "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"				)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _SCREAMING_SNAKE_CASE   :				Dict							    = config["lr"]
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = int(config["num_epochs"]				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = int(config["seed"]				)
  _SCREAMING_SNAKE_CASE   :				Any							    = int(config["batch_size"]				)

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = evaluate.load("glue",						"mrpc"				)

  set_seed(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = get_dataloaders(lowerCamelCase__,						lowerCamelCase__				)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = AutoModelForSequenceClassification.from_pretrained("bert-base-cased",						return_dict=lowerCamelCase__				)

  # 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).
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model.to(accelerator.device				)

  # Instantiate optimizer
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = AdamW(params=model.parameters(),						lr=lowerCamelCase__				)

  # Instantiate scheduler
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = get_linear_schedule_with_warmup(
      optimizer=lowerCamelCase__,						num_warmup_steps=1_0_0,						num_training_steps=(len(lowerCamelCase__				) * num_epochs),						)

  # 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.
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = accelerator.prepare(
      lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)

  # Now we train the model
  for epoch in range(lowerCamelCase__				):
    model.train()
    for step, batch in enumerate(lowerCamelCase__				):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device				)
      # New code #
      # We use the new `accumulate` context manager to perform gradient accumulation
      # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
      with accelerator.accumulate(lowerCamelCase__				):
        _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model(**lowerCamelCase__				)
        _SCREAMING_SNAKE_CASE   :				List[str]							    = output.loss
        accelerator.backward(lowerCamelCase__				)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

    _SCREAMING_SNAKE_CASE   :				Tuple							    = metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:''',						lowerCamelCase__				)





def    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				Tuple							    = argparse.ArgumentParser(description="Simple example of training script."				)
  parser.add_argument(
      "--mixed_precision",						type=lowerCamelCase__,						default=lowerCamelCase__,						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.",						)
  # New Code #
  parser.add_argument(
      "--gradient_accumulation_steps",						type=lowerCamelCase__,						default=1,						help="The number of minibatches to be ran before gradients are accumulated.",						)
  parser.add_argument("--cpu",						action="store_true",						help="If passed, will train on the CPU."				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = parser.parse_args()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
  training_function(lowerCamelCase__,						lowerCamelCase__				)


if __name__ == "__main__":
      main()





"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			42
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE  ):
     @register_to_config
     def __init__(     self  ,     snake_case__ = 3  ,     snake_case__ = 3  ,     snake_case__ = ("DownEncoderBlock2D",)  ,     snake_case__ = ("UpDecoderBlock2D",)  ,     snake_case__ = (64,)  ,     snake_case__ = 1  ,     snake_case__ = "silu"  ,     snake_case__ = 3  ,     snake_case__ = 32  ,     snake_case__ = 256  ,     snake_case__ = 32  ,     snake_case__ = None  ,     snake_case__ = 0.18_215  ,     snake_case__ = "group"  ,     ):




       """simple docstring"""


       super().__init__()

       # pass init params to Encoder
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = Encoder(
           in_channels=snake_case__  ,     out_channels=snake_case__  ,     down_block_types=snake_case__  ,     block_out_channels=snake_case__  ,     layers_per_block=snake_case__  ,     act_fn=snake_case__  ,     norm_num_groups=snake_case__  ,     double_z=snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				Dict							    = vq_embed_dim if vq_embed_dim is not None else latent_channels

       _SCREAMING_SNAKE_CASE   :				Tuple							    = nn.Convad(snake_case__  ,     snake_case__  ,     1				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = VectorQuantizer(snake_case__  ,     snake_case__  ,     beta=0.25  ,     remap=snake_case__  ,     sane_index_shape=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = nn.Convad(snake_case__  ,     snake_case__  ,     1				)

       # pass init params to Decoder
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Decoder(
           in_channels=snake_case__  ,     out_channels=snake_case__  ,     up_block_types=snake_case__  ,     block_out_channels=snake_case__  ,     layers_per_block=snake_case__  ,     act_fn=snake_case__  ,     norm_num_groups=snake_case__  ,     norm_type=snake_case__  ,     )
     @apply_forward_hook
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = True				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.encoder(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.quant_conv(snake_case__				)

       if not return_dict:
         return (h,)

       return VQEncoderOutput(latents=snake_case__				)
     @apply_forward_hook
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = False  ,     snake_case__ = True				):




       """simple docstring"""


       if not force_not_quantize:
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = self.quantize(snake_case__				)
       else:
         _SCREAMING_SNAKE_CASE   :				int							    = h
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.post_quant_conv(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = self.decoder(snake_case__  ,     quant if self.config.norm_type == "spatial" else None				)

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = True				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = sample
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.encode(snake_case__				).latents
       _SCREAMING_SNAKE_CASE   :				Any							    = self.decode(snake_case__				).sample

       if not return_dict:
         return (dec,)

       return DecoderOutput(sample=snake_case__				)



