"""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
# 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
#
########################################################################


UpperCAmelCase__			=    1_6
UpperCAmelCase__			=    3_2






def      __UpperCAmelCase       (      lowercase ,lowercase = 16		):







  """simple docstring"""
  _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    			=   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":
      _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

    UpperCAmelCase__			=    mocked_dataloaders  # noqa: F811






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  # 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=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.
  _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      __UpperCAmelCase       (      ):







  """simple docstring"""
  _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()

"""simple docstring"""


from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Tuple  				=				'efficientnet'


    def __init__(       self						:	int ,					__lowerCAmelCase						:	int = 3 ,					__lowerCAmelCase						:	int = 600 ,					__lowerCAmelCase						:	float = 2.0 ,					__lowerCAmelCase						:	float = 3.1 ,					__lowerCAmelCase						:	int = 8 ,					__lowerCAmelCase						:	List[int] = [3, 3, 5, 3, 5, 5, 3] ,					__lowerCAmelCase						:	List[int] = [32, 16, 24, 40, 80, 112, 192] ,					__lowerCAmelCase						:	List[int] = [16, 24, 40, 80, 112, 192, 320] ,					__lowerCAmelCase						:	List[int] = [] ,					__lowerCAmelCase						:	List[int] = [1, 2, 2, 2, 1, 2, 1] ,					__lowerCAmelCase						:	List[int] = [1, 2, 2, 3, 3, 4, 1] ,					__lowerCAmelCase						:	List[int] = [1, 6, 6, 6, 6, 6, 6] ,					__lowerCAmelCase						:	float = 0.25 ,					__lowerCAmelCase						:	str = "swish" ,					__lowerCAmelCase						:	int = 2560 ,					__lowerCAmelCase						:	str = "mean" ,					__lowerCAmelCase						:	float = 0.02 ,					__lowerCAmelCase						:	float = 0.001 ,					__lowerCAmelCase						:	float = 0.99 ,					__lowerCAmelCase						:	float = 0.5 ,					__lowerCAmelCase						:	float = 0.2 ,					**__lowerCAmelCase						:	Optional[Any] ,					):
      super().__init__(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   image_size
      _UpperCAmelCase    			=   width_coefficient
      _UpperCAmelCase    			=   depth_coefficient
      _UpperCAmelCase    			=   depth_divisor
      _UpperCAmelCase    			=   kernel_sizes
      _UpperCAmelCase    			=   in_channels
      _UpperCAmelCase    			=   out_channels
      _UpperCAmelCase    			=   depthwise_padding
      _UpperCAmelCase    			=   strides
      _UpperCAmelCase    			=   num_block_repeats
      _UpperCAmelCase    			=   expand_ratios
      _UpperCAmelCase    			=   squeeze_expansion_ratio
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dim
      _UpperCAmelCase    			=   pooling_type
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   batch_norm_eps
      _UpperCAmelCase    			=   batch_norm_momentum
      _UpperCAmelCase    			=   dropout_rate
      _UpperCAmelCase    			=   drop_connect_rate
      _UpperCAmelCase    			=   sum(__lowerCAmelCase       ) * 4



class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Union[str, Any]  				=				version.parse('1.11' )


    @property
    def   lowerCAmelCase_	(       self						:	Any       ):
      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]       )


    @property
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      return 1e-5
