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

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = torch.exp(snake_case__     )
       lowerCAmelCase__					     = torch.sum(snake_case__     ,  dim=1     )  # sum of exp(x_i)
       lowerCAmelCase__					     = torch.sum(x * exp_x     ,  dim=1     )  # sum of x_i * exp(x_i)
       return torch.log(snake_case__     ) - B / A
class     a_			(						nn.Module						):






 '''simple docstring'''






 def __init__(      self :  str      ,   lowercase__ :  List[Any]):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__					     = config.output_attentions
        lowerCAmelCase__					     = config.output_hidden_states
        lowerCAmelCase__					     = nn.ModuleList([BertLayer(SCREAMING_SNAKE_CASE_) for _ in range(config.num_hidden_layers)])
        lowerCAmelCase__					     = nn.ModuleList([BertHighway(SCREAMING_SNAKE_CASE_) for _ in range(config.num_hidden_layers)])

        lowerCAmelCase__					     = [-1 for _ in range(config.num_hidden_layers)]




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  List[Any]):







        '''simple docstring'''



        if (type(SCREAMING_SNAKE_CASE_) is float) or (type(SCREAMING_SNAKE_CASE_) is int):
               for i in range(len(self.early_exit_entropy)):
                      lowerCAmelCase__					     = x
        else:
               lowerCAmelCase__					     = x




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = pooler.state_dict()
        for highway in self.highway:
               for name, param in highway.pooler.state_dict().items():
                      param.copy_(loaded_model[name])




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  Union[str, Any]=None      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  List[str]=None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = ()
        lowerCAmelCase__					     = ()
        lowerCAmelCase__					     = ()
        for i, layer_module in enumerate(self.layer):
               if self.output_hidden_states:
                      lowerCAmelCase__					     = all_hidden_states + (hidden_states,)

               lowerCAmelCase__					     = layer_module(
                   SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_      ,   head_mask[i]      ,   SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_)
               lowerCAmelCase__					     = layer_outputs[0]

               if self.output_attentions:
                      lowerCAmelCase__					     = all_attentions + (layer_outputs[1],)

               lowerCAmelCase__					     = (hidden_states,)
               if self.output_hidden_states:
                      lowerCAmelCase__					     = current_outputs + (all_hidden_states,)
               if self.output_attentions:
                      lowerCAmelCase__					     = current_outputs + (all_attentions,)

               lowerCAmelCase__					     = self.highway[i](SCREAMING_SNAKE_CASE_)
               # logits, pooled_output

               if not self.training:
                      lowerCAmelCase__					     = highway_exit[0]
                      lowerCAmelCase__					     = entropy(SCREAMING_SNAKE_CASE_)
                      lowerCAmelCase__					     = highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                      lowerCAmelCase__					     = all_highway_exits + (highway_exit,)

                      if highway_entropy < self.early_exit_entropy[i]:
                             lowerCAmelCase__					     = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                             raise HighwayException(SCREAMING_SNAKE_CASE_      ,   i + 1)
               else:
                      lowerCAmelCase__					     = all_highway_exits + (highway_exit,)

        # Add last layer
        if self.output_hidden_states:
               lowerCAmelCase__					     = all_hidden_states + (hidden_states,)

        lowerCAmelCase__					     = (hidden_states,)
        if self.output_hidden_states:
               lowerCAmelCase__					     = outputs + (all_hidden_states,)
        if self.output_attentions:
               lowerCAmelCase__					     = outputs + (all_attentions,)

        lowerCAmelCase__					     = outputs + (all_highway_exits,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '	,					a__	,					)
class     a_			(						a__						):






 '''simple docstring'''






 def __init__(      self :  Tuple      ,   lowercase__ :  int):







        '''simple docstring'''



        super().__init__(SCREAMING_SNAKE_CASE_)
        lowerCAmelCase__					     = config

        lowerCAmelCase__					     = BertEmbeddings(SCREAMING_SNAKE_CASE_)
        lowerCAmelCase__					     = DeeBertEncoder(SCREAMING_SNAKE_CASE_)
        lowerCAmelCase__					     = BertPooler(SCREAMING_SNAKE_CASE_)

        self.init_weights()




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        self.encoder.init_highway_pooler(self.pooler)




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        return self.embeddings.word_embeddings




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = value




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        for layer, heads in heads_to_prune.items():
               self.encoder.layer[layer].attention.prune_heads(SCREAMING_SNAKE_CASE_)




 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_)
 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Any=None      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Union[str, Any]=None      ,   lowercase__ :  Optional[Any]=None      ,   lowercase__ :  List[str]=None      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  Union[str, Any]=None      ,   lowercase__ :  str=None      ,   ):







        '''simple docstring'''



        if input_ids is not None and inputs_embeds is not None:
               raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
        elif input_ids is not None:
               lowerCAmelCase__					     = input_ids.size()
        elif inputs_embeds is not None:
               lowerCAmelCase__					     = inputs_embeds.size()[:-1]
        else:
               raise ValueError('You have to specify either input_ids or inputs_embeds')

        lowerCAmelCase__					     = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
               lowerCAmelCase__					     = torch.ones(SCREAMING_SNAKE_CASE_      ,   device=SCREAMING_SNAKE_CASE_)
        if encoder_attention_mask is None:
               lowerCAmelCase__					     = torch.ones(SCREAMING_SNAKE_CASE_      ,   device=SCREAMING_SNAKE_CASE_)
        if token_type_ids is None:
               lowerCAmelCase__					     = torch.zeros(SCREAMING_SNAKE_CASE_      ,   dtype=torch.long      ,   device=SCREAMING_SNAKE_CASE_)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        lowerCAmelCase__					     = self.get_extended_attention_mask(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_)

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if encoder_attention_mask.dim() == 3:
               lowerCAmelCase__					     = encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
               lowerCAmelCase__					     = encoder_attention_mask[:, None, None, :]

        lowerCAmelCase__					     = encoder_extended_attention_mask.to(
            dtype=next(self.parameters()).dtype)  # fp16 compatibility
        lowerCAmelCase__					     = (1.0 - encoder_extended_attention_mask) * -10_000.0

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        lowerCAmelCase__					     = self.get_head_mask(SCREAMING_SNAKE_CASE_      ,   self.config.num_hidden_layers)

        lowerCAmelCase__					     = self.embeddings(
            input_ids=SCREAMING_SNAKE_CASE_      ,   position_ids=SCREAMING_SNAKE_CASE_      ,   token_type_ids=SCREAMING_SNAKE_CASE_      ,   inputs_embeds=SCREAMING_SNAKE_CASE_)
        lowerCAmelCase__					     = self.encoder(
            SCREAMING_SNAKE_CASE_      ,   attention_mask=SCREAMING_SNAKE_CASE_      ,   head_mask=SCREAMING_SNAKE_CASE_      ,   encoder_hidden_states=SCREAMING_SNAKE_CASE_      ,   encoder_attention_mask=SCREAMING_SNAKE_CASE_      ,   )
        lowerCAmelCase__					     = encoder_outputs[0]
        lowerCAmelCase__					     = self.pooler(SCREAMING_SNAKE_CASE_)

        lowerCAmelCase__					     = (
            sequence_output,
            pooled_output,
        ) + encoder_outputs[
            1:
        ]  # add hidden_states and attentions if they are here
        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class     a_			(						a__						):






 '''simple docstring'''






 def __init__(      self :  List[str]      ,   lowercase__ :  Any      ,   lowercase__ :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = message
        lowerCAmelCase__					     = exit_layer  # start from 1!






class     a_			(						nn.Module						):






 '''simple docstring'''






 def __init__(      self :  List[Any]      ,   lowercase__ :  Dict):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__					     = BertPooler(SCREAMING_SNAKE_CASE_)
        lowerCAmelCase__					     = nn.Dropout(config.hidden_dropout_prob)
        lowerCAmelCase__					     = nn.Linear(config.hidden_size      ,   config.num_labels)




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = encoder_outputs[0]
        lowerCAmelCase__					     = self.pooler(SCREAMING_SNAKE_CASE_)
        # "return" pooler_output

        # BertModel
        lowerCAmelCase__					     = (pooler_input, pooler_output) + encoder_outputs[1:]
        # "return" bmodel_output

        # Dropout and classification
        lowerCAmelCase__					     = bmodel_output[1]

        lowerCAmelCase__					     = self.dropout(SCREAMING_SNAKE_CASE_)
        lowerCAmelCase__					     = self.classifier(SCREAMING_SNAKE_CASE_)

        return logits, pooled_output






@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '	,					a__	,					)
class     a_			(						a__						):






 '''simple docstring'''






 def __init__(      self :  Optional[int]      ,   lowercase__ :  List[Any]):







        '''simple docstring'''



        super().__init__(SCREAMING_SNAKE_CASE_)
        lowerCAmelCase__					     = config.num_labels
        lowerCAmelCase__					     = config.num_hidden_layers

        lowerCAmelCase__					     = DeeBertModel(SCREAMING_SNAKE_CASE_)
        lowerCAmelCase__					     = nn.Dropout(config.hidden_dropout_prob)
        lowerCAmelCase__					     = nn.Linear(config.hidden_size      ,   self.config.num_labels)

        self.init_weights()




 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_)
 def 		__snake_case						(      self :  str      ,   lowercase__ :  Any=None      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  List[str]=None      ,   lowercase__ :  List[str]=None      ,   lowercase__ :  Any=None      ,   lowercase__ :  List[str]=-1      ,   lowercase__ :  Tuple=False      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self.num_layers
        try:
               lowerCAmelCase__					     = self.bert(
                   SCREAMING_SNAKE_CASE_      ,   attention_mask=SCREAMING_SNAKE_CASE_      ,   token_type_ids=SCREAMING_SNAKE_CASE_      ,   position_ids=SCREAMING_SNAKE_CASE_      ,   head_mask=SCREAMING_SNAKE_CASE_      ,   inputs_embeds=SCREAMING_SNAKE_CASE_      ,   )
               # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

               lowerCAmelCase__					     = outputs[1]

               lowerCAmelCase__					     = self.dropout(SCREAMING_SNAKE_CASE_)
               lowerCAmelCase__					     = self.classifier(SCREAMING_SNAKE_CASE_)
               lowerCAmelCase__					     = (logits,) + outputs[2:]  # add hidden states and attention if they are here
        except HighwayException as e:
               lowerCAmelCase__					     = e.message
               lowerCAmelCase__					     = e.exit_layer
               lowerCAmelCase__					     = outputs[0]

        if not self.training:
               lowerCAmelCase__					     = entropy(SCREAMING_SNAKE_CASE_)
               lowerCAmelCase__					     = []
               lowerCAmelCase__					     = []
        if labels is not None:
               if self.num_labels == 1:
                      #  We are doing regression
                      lowerCAmelCase__					     = MSELoss()
                      lowerCAmelCase__					     = loss_fct(logits.view(-1)      ,   labels.view(-1))
               else:
                      lowerCAmelCase__					     = CrossEntropyLoss()
                      lowerCAmelCase__					     = loss_fct(logits.view(-1      ,   self.num_labels)      ,   labels.view(-1))

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

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

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

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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


lowerCAmelCase__     						=			16
lowerCAmelCase__     						=			32






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__ = 1_6     ):
       lowerCAmelCase__					     = AutoTokenizer.from_pretrained('bert-base-cased'     )
       lowerCAmelCase__					     = load_dataset('glue'     ,  'mrpc'     )

       def tokenize_function(lowerCAmelCase__     ):
              # max_length=None => use the model max length (it's actually the default)
              lowerCAmelCase__					     = 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():
              lowerCAmelCase__					     = 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
       lowerCAmelCase__					     = tokenized_datasets.rename_column('label'     ,  'labels'     )

       def collate_fn(lowerCAmelCase__     ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowerCAmelCase__					     = 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":
                     lowerCAmelCase__					     = 1_6
              elif accelerator.mixed_precision != "no":
                     lowerCAmelCase__					     = 8
              else:
                     lowerCAmelCase__					     = None

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

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

    lowerCAmelCase__     						=			mocked_dataloaders  # noqa: F811






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       # For testing only
       if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,  lowerCAmelCase__     ) == "1":
              lowerCAmelCase__					     = 2
       # Initialize accelerator
       lowerCAmelCase__					     = Accelerator(cpu=args.cpu     ,  mixed_precision=args.mixed_precision     )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowerCAmelCase__					     = config['lr']
       lowerCAmelCase__					     = int(config['num_epochs']     )
       lowerCAmelCase__					     = int(config['seed']     )
       lowerCAmelCase__					     = int(config['batch_size']     )

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

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

       set_seed(lowerCAmelCase__     )
       lowerCAmelCase__     , lowerCAmelCase__					     = get_dataloaders(lowerCAmelCase__     ,  lowerCAmelCase__     )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowerCAmelCase__					     = 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).
       lowerCAmelCase__					     = model.to(accelerator.device     )

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

       # Instantiate scheduler
       lowerCAmelCase__					     = get_linear_schedule_with_warmup(
           optimizer=lowerCAmelCase__     ,  num_warmup_steps=1_0_0     ,  num_training_steps=(len(lowerCAmelCase__     ) * 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.
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = 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     )
                     lowerCAmelCase__					     = model(**lowerCAmelCase__     )
                     lowerCAmelCase__					     = outputs.loss
                     lowerCAmelCase__					     = loss / gradient_accumulation_steps
                     accelerator.backward(lowerCAmelCase__     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              lowerCAmelCase__					     = 0
              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():
                            lowerCAmelCase__					     = model(**lowerCAmelCase__     )
                     lowerCAmelCase__					     = outputs.logits.argmax(dim=-1     )
                     lowerCAmelCase__     , lowerCAmelCase__					     = accelerator.gather((predictions, batch['labels'])     )
                     # New Code #
                     # First we check if it's a distributed system
                     if accelerator.use_distributed:
                            # Then see if we're on the last batch of our eval dataloader
                            if step == len(lowerCAmelCase__     ) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   lowerCAmelCase__					     = predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                                   lowerCAmelCase__					     = references[: len(eval_dataloader.dataset     ) - samples_seen]
                            else:
                                   # Otherwise we add the number of samples seen
                                   samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                     metric.add_batch(
                         predictions=lowerCAmelCase__     ,  references=lowerCAmelCase__     ,  )

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






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = 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.'     ,  )
       parser.add_argument('--cpu'     ,  action='store_true'     ,  help='If passed, will train on the CPU.'     )
       lowerCAmelCase__					     = parser.parse_args()
       lowerCAmelCase__					     = {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
       training_function(lowerCAmelCase__     ,  lowerCAmelCase__     )


if __name__ == "__main__":
    main()
