



'''simple docstring'''





import logging

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.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


UpperCamelCase__    :     Optional[Any]			  =   logging.getLogger(__name__)


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase=None					,    _lowerCamelCase=None  )    ->    Optional[int]:
    if current_layer == 0:
       A_  :		str						=					self.embedding_hidden_mapping_in(_lowerCamelCase  )
    else:
       A_  :		Any						=					hidden_states[0]

    A_  :		Optional[int]						=					int(self.config.num_hidden_layers / self.config.num_hidden_groups  )

    # Index of the hidden group
    A_  :		Optional[Any]						=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)  )

    A_  :		Optional[Any]						=					self.albert_layer_groups[group_idx](
        _lowerCamelCase					,    _lowerCamelCase					,    head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]					,    )
    A_  :		str						=					layer_group_output[0]

    return (hidden_states,)


@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.''',							__A,							)
class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

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

    A_  :		str						=					AlbertTransformerWithPabee(_lowerCamelCase  )

    self.init_weights()
    A_  :		int						=					0
    A_  :		Tuple						=					0
    A_  :		Tuple						=					0

    A_  :		int						=					0







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					threshold







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Dict:
    A_  :		List[str]						=					patience







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_  :		Optional[int]						=					0
    A_  :		str						=					0







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    A_  :		Optional[int]						=					self.inference_layers_num / self.inference_instances_num
    A_  :		Union[str, Any]						=					(
        F"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="
        F" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
    )
    print(_lowerCamelCase  )







 @add_start_docstrings_to_model_forward(_lowerCamelCase  )
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=False					,    )    ->    Optional[Any]:

    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:
       A_  :		int						=					input_ids.size()
    elif inputs_embeds is not None:
       A_  :		int						=					inputs_embeds.size()[:-1]
    else:
       raise ValueError("""You have to specify either input_ids or inputs_embeds"""  )

    A_  :		List[str]						=					input_ids.device if input_ids is not None else inputs_embeds.device

    if attention_mask is None:
       A_  :		int						=					torch.ones(_lowerCamelCase					,    device=_lowerCamelCase  )
    if token_type_ids is None:
       A_  :		Union[str, Any]						=					torch.zeros(_lowerCamelCase					,    dtype=torch.long					,    device=_lowerCamelCase  )

    A_  :		int						=					attention_mask.unsqueeze(1  ).unsqueeze(2  )
    A_  :		Optional[Any]						=					extended_attention_mask.to(dtype=self.dtype  )  # fp16 compatibility
    A_  :		int						=					(1.0 - extended_attention_mask) * -1_0000.0
    A_  :		Union[str, Any]						=					self.get_head_mask(_lowerCamelCase					,    self.config.num_hidden_layers  )

    A_  :		Dict						=					self.embeddings(
        _lowerCamelCase					,    position_ids=_lowerCamelCase					,    token_type_ids=_lowerCamelCase					,    inputs_embeds=_lowerCamelCase  )
    A_  :		Tuple						=					embedding_output

    if self.training:
       A_  :		Dict						=					[]
       for i in range(self.config.num_hidden_layers  ):
          A_  :		str						=					self.encoder.adaptive_forward(
              _lowerCamelCase					,    current_layer=_lowerCamelCase					,    attention_mask=_lowerCamelCase					,    head_mask=_lowerCamelCase					,    )

          A_  :		List[str]						=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
          A_  :		int						=					output_layers[i](output_dropout(_lowerCamelCase  )  )
          res.append(_lowerCamelCase  )
    elif self.patience == 0:  # Use all layers for inference
       A_  :		List[str]						=					self.encoder(_lowerCamelCase					,    _lowerCamelCase					,    head_mask=_lowerCamelCase  )
       A_  :		Dict						=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
       A_  :		int						=					[output_layers[self.config.num_hidden_layers - 1](_lowerCamelCase  )]
    else:
       A_  :		Union[str, Any]						=					0
       A_  :		Tuple						=					None
       A_  :		int						=					0
       for i in range(self.config.num_hidden_layers  ):
          calculated_layer_num += 1
          A_  :		Optional[Any]						=					self.encoder.adaptive_forward(
              _lowerCamelCase					,    current_layer=_lowerCamelCase					,    attention_mask=_lowerCamelCase					,    head_mask=_lowerCamelCase					,    )

          A_  :		Dict						=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
          A_  :		List[Any]						=					output_layers[i](_lowerCamelCase  )
          if regression:
             A_  :		List[Any]						=					logits.detach()
             if patient_result is not None:
                A_  :		int						=					patient_result.detach()
             if (patient_result is not None) and torch.abs(patient_result - labels  ) < self.regression_threshold:
                patient_counter += 1
             else:
                A_  :		str						=					0
          else:
             A_  :		Optional[int]						=					logits.detach().argmax(dim=1  )
             if patient_result is not None:
                A_  :		Union[str, Any]						=					patient_result.detach().argmax(dim=1  )
             if (patient_result is not None) and torch.all(labels.eq(_lowerCamelCase  )  ):
                patient_counter += 1
             else:
                A_  :		Any						=					0

          A_  :		List[str]						=					logits
          if patient_counter == self.patience:
             break
       A_  :		Tuple						=					[patient_result]
       self.inference_layers_num += calculated_layer_num
       self.inference_instances_num += 1

    return res


@add_start_docstrings(
    '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. ''',							__A,							)
class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase  )    ->    Optional[int]:
    super().__init__(_lowerCamelCase  )
    A_  :		List[Any]						=					config.num_labels

    A_  :		Dict						=					AlbertModelWithPabee(_lowerCamelCase  )
    A_  :		Optional[Any]						=					nn.Dropout(config.classifier_dropout_prob  )
    A_  :		Dict						=					nn.ModuleList(
        [nn.Linear(config.hidden_size					,    self.config.num_labels  ) for _ in range(config.num_hidden_layers  )]  )

    self.init_weights()







 @add_start_docstrings_to_model_forward(_lowerCamelCase  )
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    _lowerCamelCase=None					,    )    ->    str:
    A_  :		Any						=					self.albert(
        input_ids=_lowerCamelCase					,    attention_mask=_lowerCamelCase					,    token_type_ids=_lowerCamelCase					,    position_ids=_lowerCamelCase					,    head_mask=_lowerCamelCase					,    inputs_embeds=_lowerCamelCase					,    output_dropout=self.dropout					,    output_layers=self.classifiers					,    regression=self.num_labels == 1					,    )

    A_  :		List[Any]						=					(logits[-1],)

    if labels is not None:
       A_  :		int						=					None
       A_  :		str						=					0
       for ix, logits_item in enumerate(_lowerCamelCase  ):
          if self.num_labels == 1:
             #  We are doing regression
             A_  :		str						=					MSELoss()
             A_  :		List[str]						=					loss_fct(logits_item.view(-1  )					,    labels.view(-1  )  )
          else:
             A_  :		int						=					CrossEntropyLoss()
             A_  :		int						=					loss_fct(logits_item.view(-1					,    self.num_labels  )					,    labels.view(-1  )  )
          if total_loss is None:
             A_  :		Dict						=					loss
          else:
             total_loss += loss * (ix + 1)
          total_weights += ix + 1
       A_  :		Optional[int]						=					(total_loss / total_weights,) + outputs

    return outputs





'''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
#
# 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
#
########################################################################


UpperCamelCase__    :     Union[str, Any]			  =   16
UpperCamelCase__    :     Optional[int]			  =   32



def     UpperCAmelCase     (    a_      ,		a_		)   ->       List[Any]:



   """simple docstring"""






   if args.with_tracking:
      A_  :		Union[str, Any]						=					Accelerator(
          cpu=args.cpu      ,		mixed_precision=args.mixed_precision      ,		log_with="""all"""      ,		project_dir=args.project_dir		)
   else:
      A_  :		Optional[Any]						=					Accelerator(cpu=args.cpu      ,		mixed_precision=args.mixed_precision		)

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

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

   A_  :		Union[str, Any]						=					AutoTokenizer.from_pretrained("""bert-base-cased"""		)
   A_  :		Optional[int]						=					load_dataset("""glue"""      ,		"""mrpc"""		)
   A_  :		List[str]						=					evaluate.load("""glue"""      ,		"""mrpc"""		)

   def tokenize_function(a_		):
      # max_length=None => use the model max length (it's actually the default)
      A_  :		Tuple						=					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():
      A_  :		Dict						=					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
   A_  :		int						=					tokenized_datasets.rename_column("""label"""      ,		"""labels"""		)

   # If the batch size is too big we use gradient accumulation
   A_  :		Optional[Any]						=					1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A_  :		List[str]						=					batch_size // MAX_GPU_BATCH_SIZE
      A_  :		List[str]						=					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.
      A_  :		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":
         A_  :		Any						=					1_6
      elif accelerator.mixed_precision != "no":
         A_  :		Optional[int]						=					8
      else:
         A_  :		Optional[Any]						=					None

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

   # Instantiate dataloaders.
   A_  :		str						=					DataLoader(
       tokenized_datasets["""train"""]      ,		shuffle=a_      ,		collate_fn=a_      ,		batch_size=a_		)
   A_  :		Any						=					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)
   A_  :		List[Any]						=					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).
   A_  :		Union[str, Any]						=					model.to(accelerator.device		)

   # Instantiate optimizer
   A_  :		Any						=					AdamW(params=model.parameters()      ,		lr=a_		)

   # Instantiate scheduler
   A_  :		Dict						=					get_linear_schedule_with_warmup(
       optimizer=a_      ,		num_warmup_steps=1_0_0      ,		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.
   A_						,  A_						,  A_						,  A_						,  A_  :		List[str]						=					accelerator.prepare(
       a_      ,		a_      ,		a_      ,		a_      ,		a_		)

   # We need to keep track of how many total steps we have iterated over
   A_  :		Optional[int]						=					0
   # We also need to keep track of the stating epoch so files are named properly
   A_  :		Union[str, Any]						=					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		)
         A_  :		int						=					os.path.basename(args.resume_from_checkpoint		)
      else:
         # Get the most recent checkpoint
         A_  :		int						=					[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
         dirs.sort(key=os.path.getctime		)
         A_  :		Optional[Any]						=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      A_  :		Optional[Any]						=					os.path.splitext(a_		)[0]

      if "epoch" in training_difference:
         A_  :		List[Any]						=					int(training_difference.replace("""epoch_"""      ,		""""""		)		) + 1
         A_  :		List[str]						=					None
      else:
         A_  :		Optional[int]						=					int(training_difference.replace("""step_"""      ,		""""""		)		)
         A_  :		Optional[int]						=					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:
         A_  :		List[str]						=					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
         A_  :		List[Any]						=					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
         A_  :		Tuple						=					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		)
         A_  :		List[Any]						=					model(**a_		)
         A_  :		str						=					outputs.loss
         A_  :		Tuple						=					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_		):
            A_  :		str						=					F"step_{overall_step}"
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  A_  :		Dict						=					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():
            A_  :		str						=					model(**a_		)
         A_  :		Any						=					outputs.logits.argmax(dim=-1		)
         A_						,  A_  :		Dict						=					accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
         metric.add_batch(
             predictions=a_      ,		references=a_      ,		)

      A_  :		Union[str, Any]						=					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":
         A_  :		str						=					F"epoch_{epoch}"
         if args.output_dir is not None:
            A_  :		Dict						=					os.path.join(args.output_dir      ,		a_		)
         accelerator.save_state(a_		)

   if args.with_tracking:
      accelerator.end_training()



def     UpperCAmelCase     (    )   ->       str:



   """simple docstring"""






   A_  :		List[Any]						=					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"""      ,		)
   A_  :		Tuple						=					parser.parse_args()
   A_  :		Union[str, Any]						=					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
   training_function(a_      ,		a_		)


if __name__ == "__main__":
  main()
