




import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


snake_case_ : Optional[Any]    		=       16
snake_case_ : Optional[int]    		=       32


def    A						(__A   :    Accelerator   ,				__A   :    int = 16       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							AutoTokenizer.from_pretrained('''bert-base-cased'''       )
     UpperCAmelCase_          =							load_dataset('''glue'''   ,				'''mrpc'''       )

     def tokenize_function(__A   :    List[Any]       ):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase_          =							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():
          UpperCAmelCase_          =							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
     UpperCAmelCase_          =							tokenized_datasets.rename_column('''label'''   ,				'''labels'''       )

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

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

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

     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_ : Tuple    		=       mocked_dataloaders  # noqa: F811


def    A						(__A   :    Optional[int]   ,				__A   :    Optional[int]       )   ->      int:







     """simple docstring"""


     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,				__A       ) == "1":
          UpperCAmelCase_          =							2
     # Initialize accelerator
     UpperCAmelCase_          =							Accelerator(cpu=args.cpu   ,				mixed_precision=args.mixed_precision       )
     # 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'''       )

     # New Code #
     # We now can define an inner training loop function. It should take a batch size as the only parameter,
     # and build the dataloaders in there.
     # It also gets our decorator
     @find_executable_batch_size(starting_batch_size=__A       )
     def inner_training_loop(__A   :    List[str]       ):
          # And now just move everything below under this function
          # We need to bring in the Accelerator object from earlier
          nonlocal accelerator
          # And reset all of its attributes that could hold onto any memory:
          accelerator.free_memory()

          # Then we can declare the model, optimizer, and everything else:
          set_seed(__A       )

          # 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=__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).
          UpperCAmelCase_          =							model.to(accelerator.device       )

          # Instantiate optimizer
          UpperCAmelCase_          =							AdamW(params=model.parameters()   ,				lr=__A       )
          UpperCAmelCase_     ,       UpperCAmelCase_          =							get_dataloaders(__A   ,				__A       )

          # Instantiate scheduler
          UpperCAmelCase_          =							get_linear_schedule_with_warmup(
              optimizer=__A   ,				num_warmup_steps=100   ,				num_training_steps=(len(__A       ) * 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(
              __A   ,				__A   ,				__A   ,				__A   ,				__A       )

          # Now we train the model
          for epoch in range(__A       ):
               model.train()
               for step, batch in enumerate(__A       ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device       )
                    UpperCAmelCase_          =							model(**__A       )
                    UpperCAmelCase_          =							outputs.loss
                    accelerator.backward(__A       )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               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():
                         UpperCAmelCase_          =							model(**__A       )
                    UpperCAmelCase_          =							outputs.logits.argmax(dim=-1       )
                    UpperCAmelCase_     ,       UpperCAmelCase_          =							accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
                    metric.add_batch(
                        predictions=__A   ,				references=__A   ,				)

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
     inner_training_loop()


def    A						()   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							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.'''       )
     UpperCAmelCase_          =							parser.parse_args()
     UpperCAmelCase_          =							{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(__A   ,				__A       )


if __name__ == "__main__":
    main()










from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		"mobilenet_v2"






   def __init__(		self						,				__A=3						,				__A=224						,				__A=1.0						,				__A=8						,				__A=8						,				__A=6						,				__A=32						,				__A=True						,				__A=True						,				__A="relu6"						,				__A=True						,				__A=0.8						,				__A=0.02						,				__A=0.001						,				__A=255						,				**__A						,				):






     """simple docstring"""





     super().__init__(**__A )

     if depth_multiplier <= 0:
       raise ValueError("depth_multiplier must be greater than zero." )

     lowerCamelCase :					str    =				num_channels
     lowerCamelCase :					Any    =				image_size
     lowerCamelCase :					Union[str, Any]    =				depth_multiplier
     lowerCamelCase :					Tuple    =				depth_divisible_by
     lowerCamelCase :					Dict    =				min_depth
     lowerCamelCase :					Dict    =				expand_ratio
     lowerCamelCase :					Optional[Any]    =				output_stride
     lowerCamelCase :					int    =				first_layer_is_expansion
     lowerCamelCase :					Union[str, Any]    =				finegrained_output
     lowerCamelCase :					Optional[Any]    =				hidden_act
     lowerCamelCase :					Optional[Any]    =				tf_padding
     lowerCamelCase :					Optional[Any]    =				classifier_dropout_prob
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					str    =				layer_norm_eps
     lowerCamelCase :					Optional[Any]    =				semantic_loss_ignore_index



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		version.parse("1.11"    )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return OrderedDict([("pixel_values", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     if self.task == "image-classification":
       return OrderedDict([("logits", {0: "batch"})] )
     else:
       return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return 1e-4



