




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										=       16
_snake_case										=       32







def 				lowerCAmelCase_	(     snake_case_,snake_case_ = 16     ):
      _A							:     List[Any]			=						AutoTokenizer.from_pretrained("""bert-base-cased"""     )
      _A							:     Any			=						load_dataset("""glue""","""mrpc"""     )

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

      def collate_fn(snake_case_     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            _A							:     Optional[Any]			=						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":
                  _A							:     Any			=						16
            elif accelerator.mixed_precision != "no":
                  _A							:     int			=						8
            else:
                  _A							:     str			=						None

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

      # Instantiate dataloaders.
      _A							:     Dict			=						DataLoader(
          tokenized_datasets["""train"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_     )
      _A							:     str			=						DataLoader(
          tokenized_datasets["""validation"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_     )

      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										=       mocked_dataloaders  # noqa: F811







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS""",snake_case_     ) == "1":
            _A							:     Optional[Any]			=						2
      # New Code #
      _A							:     str			=						int(args.gradient_accumulation_steps     )
      # Initialize accelerator
      _A							:     Dict			=						Accelerator(
          cpu=args.cpu,mixed_precision=args.mixed_precision,gradient_accumulation_steps=snake_case_     )
      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
      _A							:     int			=						config["""lr"""]
      _A							:     str			=						int(config["""num_epochs"""]     )
      _A							:     Tuple			=						int(config["""seed"""]     )
      _A							:     Optional[Any]			=						int(config["""batch_size"""]     )

      _A							:     int			=						evaluate.load("""glue""","""mrpc"""     )

      set_seed(snake_case_     )
      _A			,   _A							:     Dict			=						get_dataloaders(snake_case_,snake_case_     )
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _A							:     Dict			=						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",return_dict=snake_case_     )

      # 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).
      _A							:     List[Any]			=						model.to(accelerator.device     )

      # Instantiate optimizer
      _A							:     Any			=						AdamW(params=model.parameters(),lr=snake_case_     )

      # Instantiate scheduler
      _A							:     Optional[Any]			=						get_linear_schedule_with_warmup(
          optimizer=snake_case_,num_warmup_steps=100,num_training_steps=(len(snake_case_     ) * 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.
      _A			,   _A			,   _A			,   _A			,   _A							:     List[Any]			=						accelerator.prepare(
          snake_case_,snake_case_,snake_case_,snake_case_,snake_case_     )

      # Now we train the model
      for epoch in range(snake_case_     ):
            model.train()
            for step, batch in enumerate(snake_case_     ):
                  # 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(snake_case_     ):
                        _A							:     List[Any]			=						model(**snake_case_     )
                        _A							:     Dict			=						output.loss
                        accelerator.backward(snake_case_     )
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

            _A							:     int			=						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'''epoch {epoch}:''',snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Any			=						argparse.ArgumentParser(description="""Simple example of training script."""     )
      parser.add_argument(
          """--mixed_precision""",type=snake_case_,default=snake_case_,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=snake_case_,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."""     )
      _A							:     Tuple			=						parser.parse_args()
      _A							:     List[str]			=						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(snake_case_,snake_case_     )


if __name__ == "__main__":
  main()







from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy







@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							Optional[int]:
           super().__init__(_a				)

           _A							:     Union[str, Any]			=						RobertaEmbeddings(_a				)
           self.init_weights()







@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							str:
           super().__init__(_a				)
           _A							:     Any			=						config.num_labels
           _A							:     Dict			=						config.num_hidden_layers

           _A							:     List[str]			=						DeeRobertaModel(_a				)
           _A							:     int			=						nn.Dropout(config.hidden_dropout_prob				)
           _A							:     int			=						nn.Linear(config.hidden_size		,     self.config.num_labels				)



     @add_start_docstrings_to_model_forward(_a				)
     def 					a__	(  self		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=-1		,     _a=False		,     )						->							Any:
           _A							:     Optional[int]			=						self.num_layers
           try:
                 _A							:     List[str]			=						self.roberta(
                     _a		,     attention_mask=_a		,     token_type_ids=_a		,     position_ids=_a		,     head_mask=_a		,     inputs_embeds=_a		,     )

                 _A							:     List[str]			=						outputs[1]

                 _A							:     List[str]			=						self.dropout(_a				)
                 _A							:     Optional[Any]			=						self.classifier(_a				)
                 _A							:     List[Any]			=						(logits,) + outputs[2:]  # add hidden states and attention if they are here
           except HighwayException as e:
                 _A							:     List[Any]			=						e.message
                 _A							:     Optional[int]			=						e.exit_layer
                 _A							:     Optional[int]			=						outputs[0]

           if not self.training:
                 _A							:     int			=						entropy(_a				)
                 _A							:     int			=						[]
                 _A							:     int			=						[]
           if labels is not None:
                 if self.num_labels == 1:
                       #  We are doing regression
                       _A							:     Union[str, Any]			=						MSELoss()
                       _A							:     Tuple			=						loss_fct(logits.view(-1				)		,     labels.view(-1				)				)
                 else:
                       _A							:     List[Any]			=						CrossEntropyLoss()
                       _A							:     Dict			=						loss_fct(logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)

                 # work with highway exits
                 _A							:     Optional[Any]			=						[]
                 for highway_exit in outputs[-1]:
                       _A							:     Tuple			=						highway_exit[0]
                       if not self.training:
                             highway_logits_all.append(_a				)
                             highway_entropy.append(highway_exit[2]				)
                       if self.num_labels == 1:
                             #  We are doing regression
                             _A							:     List[str]			=						MSELoss()
                             _A							:     Optional[int]			=						loss_fct(highway_logits.view(-1				)		,     labels.view(-1				)				)
                       else:
                             _A							:     List[Any]			=						CrossEntropyLoss()
                             _A							:     Tuple			=						loss_fct(highway_logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)
                       highway_losses.append(_a				)

                 if train_highway:
                       _A							:     Dict			=						(sum(highway_losses[:-1]				),) + outputs
                       # exclude the final highway, of course
                 else:
                       _A							:     int			=						(loss,) + outputs
           if not self.training:
                 _A							:     Optional[Any]			=						outputs + ((original_entropy, highway_entropy), exit_layer)
                 if output_layer >= 0:
                       _A							:     Union[str, Any]			=						(
                           (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                       )  # use the highway of the last layer

           return outputs  # (loss), logits, (hidden_states), (attentions), entropy

