






import torch
from torch import nn


class     _a							(	nn.Module     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=1		,		_UpperCAmelCase=False			)      ->			int:
     super().__init__()

     UpperCamelCase_						=  n_token
     UpperCamelCase_						=  d_embed
     UpperCamelCase_						=  d_proj

     UpperCamelCase_						=  cutoffs + [n_token]
     UpperCamelCase_						=  [0] + self.cutoffs
     UpperCamelCase_						=  div_val

     UpperCamelCase_						=  self.cutoffs[0]
     UpperCamelCase_						=  len(self.cutoffs			) - 1
     UpperCamelCase_						=  self.shortlist_size + self.n_clusters

     if self.n_clusters > 0:
        UpperCamelCase_						=  nn.Parameter(torch.zeros(self.n_clusters		,		self.d_embed			)			)
        UpperCamelCase_						=  nn.Parameter(torch.zeros(self.n_clusters			)			)

     UpperCamelCase_						=  nn.ModuleList()
     UpperCamelCase_						=  nn.ParameterList()

     if div_val == 1:
        for i in range(len(self.cutoffs			)			):
           if d_proj != d_embed:
              self.out_projs.append(nn.Parameter(torch.FloatTensor(_UpperCAmelCase		,		_UpperCAmelCase			)			)			)
           else:
              self.out_projs.append(_UpperCAmelCase			)

        self.out_layers.append(nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase			)			)
     else:
        for i in range(len(self.cutoffs			)			):
           UpperCamelCase_		,   UpperCamelCase_						=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
           UpperCamelCase_						=  d_embed // (div_val**i)

           self.out_projs.append(nn.Parameter(torch.FloatTensor(_UpperCAmelCase		,		_UpperCAmelCase			)			)			)

           self.out_layers.append(nn.Linear(_UpperCAmelCase		,		r_idx - l_idx			)			)

     UpperCamelCase_						=  keep_order




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Any:
     if proj is None:
        UpperCamelCase_						=  nn.functional.linear(_UpperCAmelCase		,		_UpperCAmelCase		,		bias=_UpperCAmelCase			)
     else:
        # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
        UpperCamelCase_						=  nn.functional.linear(_UpperCAmelCase		,		proj.t().contiguous()			)
        UpperCamelCase_						=  nn.functional.linear(_UpperCAmelCase		,		_UpperCAmelCase		,		bias=_UpperCAmelCase			)
        # else:
        #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
        #     if bias is not None:
        #         logit = logit + bias

     return logit




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=None		,		_UpperCAmelCase=False			)      ->			int:

     if labels is not None:
        # Shift so that tokens < n predict n
        UpperCamelCase_						=  hidden[..., :-1, :].contiguous()
        UpperCamelCase_						=  labels[..., 1:].contiguous()
        UpperCamelCase_						=  hidden.view(-1		,		hidden.size(-1			)			)
        UpperCamelCase_						=  labels.view(-1			)
        if hidden.size(0			) != labels.size(0			):
           raise RuntimeError('Input and labels should have the same size in the batch dimension.'			)
     else:
        UpperCamelCase_						=  hidden.view(-1		,		hidden.size(-1			)			)

     if self.n_clusters == 0:
        UpperCamelCase_						=  self._compute_logit(_UpperCAmelCase		,		self.out_layers[0].weight		,		self.out_layers[0].bias		,		self.out_projs[0]			)
        if labels is not None:
           UpperCamelCase_						=  labels != -100
           UpperCamelCase_						=  torch.zeros_like(_UpperCAmelCase		,		dtype=hidden.dtype		,		device=hidden.device			)
           UpperCamelCase_						=  (
               -nn.functional.log_softmax(_UpperCAmelCase		,		dim=-1			)[mask].gather(1		,		labels[mask].unsqueeze(1			)			).squeeze(1			)
           )
        else:
           UpperCamelCase_						=  nn.functional.log_softmax(_UpperCAmelCase		,		dim=-1			)
     else:
        # construct weights and biases
        UpperCamelCase_		,   UpperCamelCase_						=  [], []
        for i in range(len(self.cutoffs			)			):
           if self.div_val == 1:
              UpperCamelCase_		,   UpperCamelCase_						=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
              UpperCamelCase_						=  self.out_layers[0].weight[l_idx:r_idx]
              UpperCamelCase_						=  self.out_layers[0].bias[l_idx:r_idx]
           else:
              UpperCamelCase_						=  self.out_layers[i].weight
              UpperCamelCase_						=  self.out_layers[i].bias

           if i == 0:
              UpperCamelCase_						=  torch.cat([weight_i, self.cluster_weight]		,		dim=0			)
              UpperCamelCase_						=  torch.cat([bias_i, self.cluster_bias]		,		dim=0			)

           weights.append(_UpperCAmelCase			)
           biases.append(_UpperCAmelCase			)

        UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  weights[0], biases[0], self.out_projs[0]

        UpperCamelCase_						=  self._compute_logit(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)
        UpperCamelCase_						=  nn.functional.log_softmax(_UpperCAmelCase		,		dim=1			)

        if labels is None:
           UpperCamelCase_						=  hidden.new_empty((head_logit.size(0			), self.n_token)			)
        else:
           UpperCamelCase_						=  torch.zeros_like(_UpperCAmelCase		,		dtype=hidden.dtype		,		device=hidden.device			)

        UpperCamelCase_						=  0
        UpperCamelCase_						=  [0] + self.cutoffs
        for i in range(len(_UpperCAmelCase			) - 1			):
           UpperCamelCase_		,   UpperCamelCase_						=  cutoff_values[i], cutoff_values[i + 1]

           if labels is not None:
              UpperCamelCase_						=  (labels >= l_idx) & (labels < r_idx)
              UpperCamelCase_						=  mask_i.nonzero().squeeze()

              if indices_i.numel() == 0:
                 continue

              UpperCamelCase_						=  labels.index_select(0		,		_UpperCAmelCase			) - l_idx
              UpperCamelCase_						=  head_logprob.index_select(0		,		_UpperCAmelCase			)
              UpperCamelCase_						=  hidden.index_select(0		,		_UpperCAmelCase			)
           else:
              UpperCamelCase_						=  hidden

           if i == 0:
              if labels is not None:
                 UpperCamelCase_						=  head_logprob_i.gather(1		,		target_i[:, None]			).squeeze(1			)
              else:
                 UpperCamelCase_						=  head_logprob[:, : self.cutoffs[0]]
           else:
              UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  weights[i], biases[i], self.out_projs[i]

              UpperCamelCase_						=  self._compute_logit(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)
              UpperCamelCase_						=  nn.functional.log_softmax(_UpperCAmelCase		,		dim=1			)
              UpperCamelCase_						=  self.cutoffs[0] + i - 1  # No probability for the head cluster
              if labels is not None:
                 UpperCamelCase_						=  head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                     1		,		target_i[:, None]			).squeeze(1			)
              else:
                 UpperCamelCase_						=  head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                 UpperCamelCase_						=  logprob_i

           if labels is not None:
              if (hasattr(self		,		'keep_order'			) and self.keep_order) or keep_order:
                 out.index_copy_(0		,		_UpperCAmelCase		,		-logprob_i			)
              else:
                 out[offset : offset + logprob_i.size(0			)].copy_(-logprob_i			)
              offset += logprob_i.size(0			)

     return out




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[Any]:
     if self.n_clusters == 0:
        UpperCamelCase_						=  self._compute_logit(_UpperCAmelCase		,		self.out_layers[0].weight		,		self.out_layers[0].bias		,		self.out_projs[0]			)
        return nn.functional.log_softmax(_UpperCAmelCase		,		dim=-1			)
     else:
        # construct weights and biases
        UpperCamelCase_		,   UpperCamelCase_						=  [], []
        for i in range(len(self.cutoffs			)			):
           if self.div_val == 1:
              UpperCamelCase_		,   UpperCamelCase_						=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
              UpperCamelCase_						=  self.out_layers[0].weight[l_idx:r_idx]
              UpperCamelCase_						=  self.out_layers[0].bias[l_idx:r_idx]
           else:
              UpperCamelCase_						=  self.out_layers[i].weight
              UpperCamelCase_						=  self.out_layers[i].bias

           if i == 0:
              UpperCamelCase_						=  torch.cat([weight_i, self.cluster_weight]		,		dim=0			)
              UpperCamelCase_						=  torch.cat([bias_i, self.cluster_bias]		,		dim=0			)

           weights.append(_UpperCAmelCase			)
           biases.append(_UpperCAmelCase			)

        UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  weights[0], biases[0], self.out_projs[0]
        UpperCamelCase_						=  self._compute_logit(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)

        UpperCamelCase_						=  hidden.new_empty((head_logit.size(0			), self.n_token)			)
        UpperCamelCase_						=  nn.functional.log_softmax(_UpperCAmelCase		,		dim=1			)

        UpperCamelCase_						=  [0] + self.cutoffs
        for i in range(len(_UpperCAmelCase			) - 1			):
           UpperCamelCase_		,   UpperCamelCase_						=  cutoff_values[i], cutoff_values[i + 1]

           if i == 0:
              UpperCamelCase_						=  head_logprob[:, : self.cutoffs[0]]
           else:
              UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  weights[i], biases[i], self.out_projs[i]

              UpperCamelCase_						=  self._compute_logit(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)
              UpperCamelCase_						=  nn.functional.log_softmax(_UpperCAmelCase		,		dim=1			)

              UpperCamelCase_						=  head_logprob[:, -i] + tail_logprob_i
              UpperCamelCase_						=  logprob_i

        return out













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
#
########################################################################


snake_case__   :						Dict													=   1_6
snake_case__   :						List[str]													=   3_2






def      _snake_case     (__lowercase     ,   __lowercase = 16):
   UpperCamelCase_						=  AutoTokenizer.from_pretrained('bert-base-cased')
   UpperCamelCase_						=  load_dataset('glue'     ,   'mrpc')

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

   def collate_fn(__lowercase):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCamelCase_						=  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":
         UpperCamelCase_						=  16
      elif accelerator.mixed_precision != "no":
         UpperCamelCase_						=  8
      else:
         UpperCamelCase_						=  None

      return tokenizer.pad(
          __lowercase     ,   padding='longest'     ,   max_length=__lowercase     ,   pad_to_multiple_of=__lowercase     ,   return_tensors='pt'     ,   )

   # Instantiate dataloaders.
   UpperCamelCase_						=  DataLoader(
       tokenized_datasets['train']     ,   shuffle=__lowercase     ,   collate_fn=__lowercase     ,   batch_size=__lowercase)
   UpperCamelCase_						=  DataLoader(
       tokenized_datasets['validation']     ,   shuffle=__lowercase     ,   collate_fn=__lowercase     ,   batch_size=__lowercase)

   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

 snake_case__   :						List[str]													=   mocked_dataloaders  # noqa: F811






def      _snake_case     (__lowercase     ,   __lowercase):
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,   __lowercase) == "1":
      UpperCamelCase_						=  2
   # New Code #
   UpperCamelCase_						=  int(args.gradient_accumulation_steps)
   # Initialize accelerator
   UpperCamelCase_						=  Accelerator(
       cpu=args.cpu     ,   mixed_precision=args.mixed_precision     ,   gradient_accumulation_steps=__lowercase)
   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
   UpperCamelCase_						=  config['lr']
   UpperCamelCase_						=  int(config['num_epochs'])
   UpperCamelCase_						=  int(config['seed'])
   UpperCamelCase_						=  int(config['batch_size'])

   UpperCamelCase_						=  evaluate.load('glue'     ,   'mrpc')

   set_seed(__lowercase)
   UpperCamelCase_		,   UpperCamelCase_						=  get_dataloaders(__lowercase     ,   __lowercase)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCamelCase_						=  AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,   return_dict=__lowercase)

   # 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).
   UpperCamelCase_						=  model.to(accelerator.device)

   # Instantiate optimizer
   UpperCamelCase_						=  AdamW(params=model.parameters()     ,   lr=__lowercase)

   # Instantiate scheduler
   UpperCamelCase_						=  get_linear_schedule_with_warmup(
       optimizer=__lowercase     ,   num_warmup_steps=100     ,   num_training_steps=(len(__lowercase) * 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.
   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  accelerator.prepare(
       __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase)

   # Now we train the model
   for epoch in range(__lowercase):
      model.train()
      for step, batch in enumerate(__lowercase):
         # 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(__lowercase):
            UpperCamelCase_						=  model(**__lowercase)
            UpperCamelCase_						=  output.loss
            accelerator.backward(__lowercase)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

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

      UpperCamelCase_						=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"""epoch {epoch}:"""     ,   __lowercase)






def      _snake_case     ():
   UpperCamelCase_						=  argparse.ArgumentParser(description='Simple example of training script.')
   parser.add_argument(
       '--mixed_precision'     ,   type=__lowercase     ,   default=__lowercase     ,   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=__lowercase     ,   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.')
   UpperCamelCase_						=  parser.parse_args()
   UpperCamelCase_						=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(__lowercase     ,   __lowercase)


if __name__ == "__main__":
 main()





