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


lowerCamelCase__     :		Tuple        = 1_6
lowerCamelCase__     :		int        = 3_2

def 		UpperCamelCase						(   lowercase_			,			lowercase_ = 16 )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   AutoTokenizer.from_pretrained("""bert-base-cased""" )
  lowercase__	: List[str]     		=   load_dataset("""glue"""			,			"""mrpc""" )

  def tokenize_function(lowercase_ ):
    # max_length=None => use the model max length (it's actually the default)
    lowercase__	: str     		=   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():
    lowercase__	: str     		=   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
  lowercase__	: Optional[Any]     		=   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.
    lowercase__	: int     		=   1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      lowercase__	: List[Any]     		=   16
    elif accelerator.mixed_precision != "no":
      lowercase__	: List[Any]     		=   8
    else:
      lowercase__	: List[str]     		=   None

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

  # Instantiate dataloaders.
  lowercase__	: Tuple     		=   DataLoader(
      tokenized_datasets["""train"""]			,			shuffle=lowercase_			,			collate_fn=lowercase_			,			batch_size=lowercase_ )
  lowercase__	: Dict     		=   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

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Any:


  '''simple docstring'''






  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,			lowercase_ ) == "1":
    lowercase__	: Optional[Any]     		=   2
  # New Code #
  lowercase__	: int     		=   int(args.gradient_accumulation_steps )
  # Initialize accelerator
  lowercase__	: List[str]     		=   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
  lowercase__	: Optional[int]     		=   config["""lr"""]
  lowercase__	: Optional[int]     		=   int(config["""num_epochs"""] )
  lowercase__	: Any     		=   int(config["""seed"""] )
  lowercase__	: str     		=   int(config["""batch_size"""] )

  lowercase__	: Dict     		=   evaluate.load("""glue"""			,			"""mrpc""" )

  set_seed(lowercase_ )
  lowercase__	: Optional[int]     		=   get_dataloaders(lowercase_			,			lowercase_ )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowercase__	: Tuple     		=   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).
  lowercase__	: Any     		=   model.to(accelerator.device )

  # Instantiate optimizer
  lowercase__	: Union[str, Any]     		=   AdamW(params=model.parameters()			,			lr=lowercase_ )

  # Instantiate scheduler
  lowercase__	: Union[str, Any]     		=   get_linear_schedule_with_warmup(
      optimizer=lowercase_			,			num_warmup_steps=1_00			,			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.
  lowercase__	: Tuple     		=   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_ ):
        lowercase__	: Tuple     		=   model(**lowercase_ )
        lowercase__	: Union[str, Any]     		=   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():
        lowercase__	: Optional[int]     		=   model(**lowercase_ )
      lowercase__	: List[str]     		=   outputs.logits.argmax(dim=-1 )
      lowercase__	: List[Any]     		=   accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
      metric.add_batch(
          predictions=lowercase_			,			references=lowercase_			,			)

    lowercase__	: Dict     		=   metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'epoch {epoch}:'			,			lowercase_ )

def 		UpperCamelCase						(   )		->						Dict:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   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.""" )
  lowercase__	: Dict     		=   parser.parse_args()
  lowercase__	: Any     		=   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(lowercase_			,			lowercase_ )


if __name__ == "__main__":
     main()









"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_A							=  logging.get_logger(__name__)

_A							=  {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  'bart'
  SCREAMING_SNAKE_CASE					      =  ['past_key_values']
  SCREAMING_SNAKE_CASE					      =  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



  def __init__(self							,       _lowerCamelCase=50265							,       _lowerCamelCase=1024							,       _lowerCamelCase=12							,       _lowerCamelCase=4096							,       _lowerCamelCase=16							,       _lowerCamelCase=12							,       _lowerCamelCase=4096							,       _lowerCamelCase=16							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.0							,       _lowerCamelCase="gelu"							,       _lowerCamelCase=1024							,       _lowerCamelCase=0.1							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.02							,       _lowerCamelCase=0.0							,       _lowerCamelCase=False							,       _lowerCamelCase=True							,       _lowerCamelCase=3							,       _lowerCamelCase=1							,       _lowerCamelCase=0							,       _lowerCamelCase=2							,       _lowerCamelCase=True							,       _lowerCamelCase=2							,       _lowerCamelCase=2							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    vocab_size
        UpperCAmelCase__			:							Optional[Any]							=    max_position_embeddings
        UpperCAmelCase__			:							int							=    d_model
        UpperCAmelCase__			:							str							=    encoder_ffn_dim
        UpperCAmelCase__			:							List[str]							=    encoder_layers
        UpperCAmelCase__			:							Optional[int]							=    encoder_attention_heads
        UpperCAmelCase__			:							Optional[int]							=    decoder_ffn_dim
        UpperCAmelCase__			:							Dict							=    decoder_layers
        UpperCAmelCase__			:							int							=    decoder_attention_heads
        UpperCAmelCase__			:							Optional[Any]							=    dropout
        UpperCAmelCase__			:							int							=    attention_dropout
        UpperCAmelCase__			:							str							=    activation_dropout
        UpperCAmelCase__			:							Optional[int]							=    activation_function
        UpperCAmelCase__			:							List[str]							=    init_std
        UpperCAmelCase__			:							Dict							=    encoder_layerdrop
        UpperCAmelCase__			:							Any							=    decoder_layerdrop
        UpperCAmelCase__			:							List[str]							=    classifier_dropout
        UpperCAmelCase__			:							Any							=    use_cache
        UpperCAmelCase__			:							Union[str, Any]							=    encoder_layers
        UpperCAmelCase__			:							List[Any]							=    scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            num_labels=_lowerCamelCase							,       pad_token_id=_lowerCamelCase							,       bos_token_id=_lowerCamelCase							,       eos_token_id=_lowerCamelCase							,       is_encoder_decoder=_lowerCamelCase							,       decoder_start_token_id=_lowerCamelCase							,       forced_eos_token_id=_lowerCamelCase							,       **_lowerCamelCase							,       )

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,       _lowerCamelCase	):
              UpperCAmelCase__			:							List[Any]							=    self.bos_token_id
              warnings.warn(
                  F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
                  """The config can simply be saved and uploaded again to be fixed."""	)




class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  @property
  def  _a      (self	):


        """simple docstring"""

        if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase__			:							int							=    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]	)

              if self.use_past:
                    UpperCAmelCase__			:							Dict							=    {0: """batch"""}
                    UpperCAmelCase__			:							Tuple							=    {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                    UpperCAmelCase__			:							Dict							=    {0: """batch""", 1: """decoder_sequence"""}
                    UpperCAmelCase__			:							List[str]							=    {0: """batch""", 1: """decoder_sequence"""}

              if self.use_past:
                    self.fill_with_past_key_values_(_lowerCamelCase							,       direction="""inputs"""	)
        elif self.task == "causal-lm":
              # TODO: figure this case out.
              UpperCAmelCase__			:							str							=    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]	)
              if self.use_past:
                    UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    self.num_layers
                    for i in range(_lowerCamelCase	):
                          UpperCAmelCase__			:							Optional[int]							=    {0: """batch""", 2: """past_sequence + sequence"""}
                          UpperCAmelCase__			:							int							=    {0: """batch""", 2: """past_sequence + sequence"""}
        else:
              UpperCAmelCase__			:							Union[str, Any]							=    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                      ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                  ]	)

        return common_inputs



  @property
  def  _a      (self	):


        """simple docstring"""

        if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase__			:							Any							=    super().outputs
        else:
              UpperCAmelCase__			:							Union[str, Any]							=    super(_lowerCamelCase							,       self	).outputs
              if self.use_past:
                    UpperCAmelCase__					,		UpperCAmelCase__			:							Dict							=    self.num_layers
                    for i in range(_lowerCamelCase	):
                          UpperCAmelCase__			:							List[str]							=    {0: """batch""", 2: """past_sequence + sequence"""}
                          UpperCAmelCase__			:							int							=    {0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = -1							,       _lowerCamelCase = -1							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)

        # Generate decoder inputs
        UpperCAmelCase__			:							int							=    seq_length if not self.use_past else 1
        UpperCAmelCase__			:							str							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							str							=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        UpperCAmelCase__			:							Union[str, Any]							=    dict(**_lowerCamelCase							,       **_lowerCamelCase	)

        if self.use_past:
              if not is_torch_available():
                    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
              else:
                    import torch
              UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    common_inputs["""input_ids"""].shape
              UpperCAmelCase__			:							int							=    common_inputs["""decoder_input_ids"""].shape[1]
              UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    self.num_attention_heads
              UpperCAmelCase__			:							str							=    (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCAmelCase__			:							Optional[int]							=    decoder_seq_length + 3
              UpperCAmelCase__			:							Any							=    (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              UpperCAmelCase__			:							str							=    torch.cat(
                  [common_inputs["""decoder_attention_mask"""], torch.ones(_lowerCamelCase							,       _lowerCamelCase	)]							,       dim=1	)

              UpperCAmelCase__			:							List[str]							=    []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCAmelCase__					,		UpperCAmelCase__			:							Dict							=    self.num_layers
              UpperCAmelCase__			:							Union[str, Any]							=    min(_lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							str							=    max(_lowerCamelCase							,       _lowerCamelCase	) - min_num_layers
              UpperCAmelCase__			:							Dict							=    """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

              for _ in range(_lowerCamelCase	):
                    common_inputs["past_key_values"].append(
                        (
                            torch.zeros(_lowerCamelCase	),
                            torch.zeros(_lowerCamelCase	),
                            torch.zeros(_lowerCamelCase	),
                            torch.zeros(_lowerCamelCase	),
                        )	)
              # TODO: test this.
              UpperCAmelCase__			:							Dict							=    encoder_shape if remaining_side_name == """encoder""" else decoder_shape
              for _ in range(_lowerCamelCase							,       _lowerCamelCase	):
                    common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase	), torch.zeros(_lowerCamelCase	))	)
        return common_inputs



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = -1							,       _lowerCamelCase = -1							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)

        if self.use_past:
              if not is_torch_available():
                    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
              else:
                    import torch
              UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[int]							=    common_inputs["""input_ids"""].shape
              # Not using the same length for past_key_values
              UpperCAmelCase__			:							int							=    seqlen + 2
              UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[int]							=    self.num_layers
              UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    self.num_attention_heads
              UpperCAmelCase__			:							Tuple							=    (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              UpperCAmelCase__			:							Optional[int]							=    common_inputs["""attention_mask"""].dtype
              UpperCAmelCase__			:							List[str]							=    torch.cat(
                  [common_inputs["""attention_mask"""], torch.ones(_lowerCamelCase							,       _lowerCamelCase							,       dtype=_lowerCamelCase	)]							,       dim=1	)
              UpperCAmelCase__			:							Tuple							=    [
                  (torch.zeros(_lowerCamelCase	), torch.zeros(_lowerCamelCase	)) for _ in range(_lowerCamelCase	)
              ]
        return common_inputs



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = -1							,       _lowerCamelCase = -1							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    compute_effective_axis_dimension(
            _lowerCamelCase							,       fixed_dimension=OnnxConfig.default_fixed_batch							,       num_token_to_add=0	)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        UpperCAmelCase__			:							Optional[int]							=    tokenizer.num_special_tokens_to_add(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    compute_effective_axis_dimension(
            _lowerCamelCase							,       fixed_dimension=OnnxConfig.default_fixed_sequence							,       num_token_to_add=_lowerCamelCase	)

        # Generate dummy inputs according to compute batch and sequence
        UpperCAmelCase__			:							List[Any]							=    [""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
        UpperCAmelCase__			:							Optional[int]							=    dict(tokenizer(_lowerCamelCase							,       return_tensors=_lowerCamelCase	)	)
        return common_inputs



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = -1							,       _lowerCamelCase = -1							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       ):


        """simple docstring"""

        if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase__			:							Tuple							=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  _lowerCamelCase							,       batch_size=_lowerCamelCase							,       seq_length=_lowerCamelCase							,       is_pair=_lowerCamelCase							,       framework=_lowerCamelCase	)

        elif self.task == "causal-lm":
              UpperCAmelCase__			:							Optional[int]							=    self._generate_dummy_inputs_for_causal_lm(
                  _lowerCamelCase							,       batch_size=_lowerCamelCase							,       seq_length=_lowerCamelCase							,       is_pair=_lowerCamelCase							,       framework=_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Dict							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  _lowerCamelCase							,       batch_size=_lowerCamelCase							,       seq_length=_lowerCamelCase							,       is_pair=_lowerCamelCase							,       framework=_lowerCamelCase	)

        return common_inputs




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase__			:							Union[str, Any]							=    super()._flatten_past_key_values_(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    super(_lowerCamelCase							,       self	)._flatten_past_key_values_(
                  _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)

