
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 the experiment tracking capability,
# 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
#
########################################################################

lowercase  : Any						   =							16
lowercase  : Optional[Any]						   =							32







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = 16    )			->   Optional[Any]:
       lowercase							:					Dict	     =				AutoTokenizer.from_pretrained("""bert-base-cased"""    )
       lowercase							:					Optional[Any]	     =				load_dataset("""glue""" ,     """mrpc"""    )

       def tokenize_function(SCREAMING_SNAKE_CASE__    ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase							:					List[Any]	     =				tokenizer(examples["""sentence1"""] ,     examples["""sentence2"""] ,     truncation=SCREAMING_SNAKE_CASE__ ,     max_length=SCREAMING_SNAKE_CASE__    )
              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							:					Optional[Any]	     =				datasets.map(
                  SCREAMING_SNAKE_CASE__ ,     batched=SCREAMING_SNAKE_CASE__ ,     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							:					Dict	     =				tokenized_datasets.rename_column("""label""" ,     """labels"""    )

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

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

       # Instantiate dataloaders.
       lowercase							:					str	     =				DataLoader(
           tokenized_datasets["""train"""] ,     shuffle=SCREAMING_SNAKE_CASE__ ,     collate_fn=SCREAMING_SNAKE_CASE__ ,     batch_size=SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[str]	     =				DataLoader(
           tokenized_datasets["""validation"""] ,     shuffle=SCREAMING_SNAKE_CASE__ ,     collate_fn=SCREAMING_SNAKE_CASE__ ,     batch_size=SCREAMING_SNAKE_CASE__    )

       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

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,     SCREAMING_SNAKE_CASE__    ) == "1":
              lowercase							:					Any	     =				2
       # Initialize Accelerator

       # New Code #
       # We pass in "all" to `log_with` to grab all available trackers in the environment
       # Note: If using a custom `Tracker` class, should be passed in here such as:
       # >>> log_with = ["all", MyCustomTrackerClassInstance()]
       if args.with_tracking:
              lowercase							:					Any	     =				Accelerator(
                  cpu=args.cpu ,     mixed_precision=args.mixed_precision ,     log_with="""all""" ,     project_dir=args.project_dir    )
       else:
              lowercase							:					List[str]	     =				Accelerator(cpu=args.cpu ,     mixed_precision=args.mixed_precision    )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase							:					List[str]	     =				config["""lr"""]
       lowercase							:					Optional[int]	     =				int(config["""num_epochs"""]    )
       lowercase							:					Optional[Any]	     =				int(config["""seed"""]    )
       lowercase							:					int	     =				int(config["""batch_size"""]    )
       set_seed(SCREAMING_SNAKE_CASE__    )

       lowercase     ,      lowercase							:					Dict	     =				get_dataloaders(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[Any]	     =				evaluate.load("""glue""" ,     """mrpc"""    )

       # If the batch size is too big we use gradient accumulation
       lowercase							:					str	     =				1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              lowercase							:					List[Any]	     =				batch_size // MAX_GPU_BATCH_SIZE
              lowercase							:					List[Any]	     =				MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase							:					Dict	     =				AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,     return_dict=SCREAMING_SNAKE_CASE__    )

       # 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							:					List[str]	     =				model.to(accelerator.device    )

       # Instantiate optimizer
       lowercase							:					str	     =				AdamW(params=model.parameters() ,     lr=SCREAMING_SNAKE_CASE__    )

       # Instantiate scheduler
       lowercase							:					int	     =				get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE__ ,     num_warmup_steps=100 ,     num_training_steps=(len(SCREAMING_SNAKE_CASE__    ) * 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.
       lowercase     ,      lowercase     ,      lowercase     ,      lowercase     ,      lowercase							:					List[str]	     =				accelerator.prepare(
           SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       # New Code #
       # We need to initialize the trackers we use. Overall configurations can also be stored
       if args.with_tracking:
              lowercase							:					Any	     =				os.path.split(SCREAMING_SNAKE_CASE__    )[-1].split("""."""    )[0]
              accelerator.init_trackers(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE__    ):
              model.train()
              # New Code #
              # For our tracking example, we will log the total loss of each epoch
              if args.with_tracking:
                     lowercase							:					Any	     =				0
              for step, batch in enumerate(SCREAMING_SNAKE_CASE__    ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device    )
                     lowercase							:					Optional[Any]	     =				model(**SCREAMING_SNAKE_CASE__    )
                     lowercase							:					Union[str, Any]	     =				outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     lowercase							:					Dict	     =				loss / gradient_accumulation_steps
                     accelerator.backward(SCREAMING_SNAKE_CASE__    )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE__    ):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device    )
                     with torch.no_grad():
                            lowercase							:					List[str]	     =				model(**SCREAMING_SNAKE_CASE__    )
                     lowercase							:					Dict	     =				outputs.logits.argmax(dim=-1    )
                     lowercase     ,      lowercase							:					Union[str, Any]	     =				accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
                     metric.add_batch(
                         predictions=SCREAMING_SNAKE_CASE__ ,     references=SCREAMING_SNAKE_CASE__ ,     )

              lowercase							:					Tuple	     =				metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}:" ,     SCREAMING_SNAKE_CASE__    )

              # New Code #
              # To actually log, we call `Accelerator.log`
              # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
              if args.with_tracking:
                     accelerator.log(
                         {
                             """accuracy""": eval_metric["""accuracy"""],
                             """f1""": eval_metric["""f1"""],
                             """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE__    ),
                             """epoch""": epoch,
                         } ,     step=SCREAMING_SNAKE_CASE__ ,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
       if args.with_tracking:
              accelerator.end_training()







def 						_snake_case(  )			->   Optional[int]:
       lowercase							:					int	     =				argparse.ArgumentParser(description="""Simple example of training script."""    )
       parser.add_argument(
           """--mixed_precision""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=SCREAMING_SNAKE_CASE__ ,     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(
           """--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(
           """--project_dir""" ,     type=SCREAMING_SNAKE_CASE__ ,     default="""logs""" ,     help="""Location on where to store experiment tracking logs` and relevent project information""" ,     )
       lowercase							:					List[Any]	     =				parser.parse_args()
       lowercase							:					List[str]	     =				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )


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


lowercase  : List[Any]						   =							logging.get_logger(__name__)

lowercase  : List[Any]						   =							{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Dict=    "mobilenet_v1"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case="relu6"		,snake_case=True		,snake_case=0.999		,snake_case=0.02		,snake_case=0.001		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

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

           lowercase							:					int	     =				num_channels
           lowercase							:					Union[str, Any]	     =				image_size
           lowercase							:					int	     =				depth_multiplier
           lowercase							:					Tuple	     =				min_depth
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Dict	     =				tf_padding
           lowercase							:					Dict	     =				classifier_dropout_prob
           lowercase							:					int	     =				initializer_range
           lowercase							:					List[str]	     =				layer_norm_eps






class __snake_case		(				lowerCAmelCase					):
    _a		:						int=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4
