'''simple docstring'''




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


__lowercase        =    1_6
__lowercase        =    3_2






def 							snake_case__				(     _A:   Accelerator ,  _A:   int = 16					)       ->      List[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    AutoTokenizer.from_pretrained("""bert-base-cased"""					)
		lowerCAmelCase     =    load_dataset("""glue""" ,  """mrpc"""					)

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

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

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

		# Instantiate dataloaders.
		lowerCAmelCase     =    DataLoader(
		    tokenized_datasets["""train"""] ,  shuffle=_A ,  collate_fn=_A ,  batch_size=_A					)
		lowerCAmelCase     =    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

						__lowercase        =    mocked_dataloaders  # noqa: F811






def 							snake_case__				(     _A:   Dict ,  _A:   int					)       ->      List[Any]:

		'''simple docstring'''


		if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,  _A					) == "1":
				lowerCAmelCase     =    2
		# Initialize accelerator
		lowerCAmelCase     =    Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision					)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase     =    config["""lr"""]
		lowerCAmelCase     =    int(config["""num_epochs"""]					)
		lowerCAmelCase     =    int(config["""seed"""]					)
		lowerCAmelCase     =    int(config["""batch_size"""]					)

		lowerCAmelCase     =    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:   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)
				lowerCAmelCase     =    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).
				lowerCAmelCase     =    model.to(accelerator.device					)

				# Instantiate optimizer
				lowerCAmelCase     =    AdamW(params=model.parameters() ,  lr=_A					)
				lowerCAmelCase   ,	lowerCAmelCase     =    get_dataloaders(_A ,  _A					)

				# Instantiate scheduler
				lowerCAmelCase     =    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.
				lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    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					)
								lowerCAmelCase     =    model(**_A					)
								lowerCAmelCase     =    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():
										lowerCAmelCase     =    model(**_A					)
								lowerCAmelCase     =    outputs.logits.argmax(dim=-1					)
								lowerCAmelCase   ,	lowerCAmelCase     =    accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
								metric.add_batch(
								    predictions=_A ,  references=_A ,  )

						lowerCAmelCase     =    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 							snake_case__				(     )       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    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."""					)
		lowerCAmelCase     =    parser.parse_args()
		lowerCAmelCase     =    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(_A ,  _A					)


if __name__ == "__main__":
						main()





'''simple docstring'''




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        =    logging.get_logger(__name__)

__lowercase        =    {
    '''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 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''mobilenet_v1'''



			def __init__(  self			,       __lowerCAmelCase=3			,       __lowerCAmelCase=224			,       __lowerCAmelCase=1.0			,       __lowerCAmelCase=8			,       __lowerCAmelCase="relu6"			,       __lowerCAmelCase=True			,       __lowerCAmelCase=0.999			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=0.001			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)

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

					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    depth_multiplier
					lowerCAmelCase     =    min_depth
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    tf_padding
					lowerCAmelCase     =    classifier_dropout_prob
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    layer_norm_eps







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			version.parse('''1.11'''      )



			@property
			def 				a_			(  self):




					"""simple docstring"""


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



			@property
			def 				a_			(  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 				a_			(  self):




					"""simple docstring"""


					return 1E-4




