





'''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
#
########################################################################


__UpperCamelCase					    =	16
__UpperCamelCase					    =	32







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = 16 ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[Any]							      =     AutoTokenizer.from_pretrained("""bert-base-cased""" )
			__snake_case					:      Dict							      =     load_dataset("""glue"""    ,	"""mrpc""" )

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

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

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

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

			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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				Tuple:



			"""simple docstring"""


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

			__snake_case					:      int							      =     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=_lowerCamelCase )
			def inner_training_loop(_lowerCamelCase ):
						# 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(_lowerCamelCase )

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

						# 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).
						__snake_case					:      Union[str, Any]							      =     model.to(accelerator.device )

						# Instantiate optimizer
						__snake_case					:      int							      =     AdamW(params=model.parameters()    ,	lr=_lowerCamelCase )
						__snake_case      ,     __snake_case					:      Optional[int]							      =     get_dataloaders(_lowerCamelCase    ,	_lowerCamelCase )

						# Instantiate scheduler
						__snake_case					:      Optional[Any]							      =     get_linear_schedule_with_warmup(
						    optimizer=_lowerCamelCase    ,	num_warmup_steps=100    ,	num_training_steps=(len(_lowerCamelCase ) * 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.
						__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      int							      =     accelerator.prepare(
						    _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )

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

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

									__snake_case					:      Any							      =     metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(F'''epoch {epoch}:'''    ,	_lowerCamelCase )

    # 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						(       ) ->				Optional[int]:



			"""simple docstring"""


			__snake_case					:      Optional[Any]							      =     argparse.ArgumentParser(description="""Simple example of training script.""" )
			parser.add_argument(
			    """--mixed_precision"""    ,	type=_lowerCamelCase    ,	default=_lowerCamelCase    ,	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.""" )
			__snake_case					:      Any							      =     parser.parse_args()
			__snake_case					:      Any							      =     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(_lowerCamelCase    ,	_lowerCamelCase )


if __name__ == "__main__":
	main()









'''simple docstring'''






import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     MobileNetVaConfig(layer_norm_eps=0.0_01 )

			if "_quant" in model_name:
						raise ValueError("""Quantized models are not supported.""" )

			__snake_case					:      List[Any]							      =     re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""    ,	_lowerCamelCase )
			if matches:
						__snake_case					:      Optional[Any]							      =     float(matches[1] )
						__snake_case					:      Union[str, Any]							      =     int(matches[2] )

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			__snake_case					:      Tuple							      =     1001
			__snake_case					:      Any							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      Optional[Any]							      =     """huggingface/label-files"""
			__snake_case					:      List[Any]							      =     json.load(open(hf_hub_download(_lowerCamelCase    ,	_lowerCamelCase    ,	repo_type="""dataset""" )    ,	"""r""" ) )
			__snake_case					:      Dict							      =     {int(_lowerCamelCase ) + 1: v for k, v in idalabel.items()}
			__snake_case					:      List[str]							      =     """background"""
			__snake_case					:      List[str]							      =     idalabel
			__snake_case					:      List[Any]							      =     {v: k for k, v in idalabel.items()}

			return config







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[Any]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )
			return im







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     get_mobilenet_va_config(_lowerCamelCase )

			# Load 🤗 model
			__snake_case					:      Optional[Any]							      =     MobileNetVaForImageClassification(_lowerCamelCase ).eval()

			# Load weights from TensorFlow checkpoint
			load_tf_weights_in_mobilenet_va(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )

			# Check outputs on an image, prepared by MobileNetV1ImageProcessor
			__snake_case					:      Optional[int]							      =     MobileNetVaImageProcessor(
			    crop_size={"""width""": config.image_size, """height""": config.image_size}    ,	size={"""shortest_edge""": config.image_size + 32}    ,	)
			__snake_case					:      Tuple							      =     image_processor(images=prepare_img()    ,	return_tensors="""pt""" )
			__snake_case					:      Optional[Any]							      =     model(**_lowerCamelCase )
			__snake_case					:      List[Any]							      =     outputs.logits

			assert logits.shape == (1, 1001)

			if model_name == "mobilenet_v1_1.0_224":
						__snake_case					:      str							      =     torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
			elif model_name == "mobilenet_v1_0.75_192":
						__snake_case					:      Tuple							      =     torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
			else:
						__snake_case					:      List[Any]							      =     None

			if expected_logits is not None:
						assert torch.allclose(logits[0, :3]    ,	_lowerCamelCase    ,	atol=1E-4 )

			Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
			print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
			model.save_pretrained(_lowerCamelCase )
			print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
			image_processor.save_pretrained(_lowerCamelCase )

			if push_to_hub:
						print("""Pushing to the hub...""" )
						__snake_case					:      Optional[Any]							      =     """google/""" + model_name
						image_processor.push_to_hub(_lowerCamelCase )
						model.push_to_hub(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="mobilenet_v1_1.0_224",
	    type=str,
	    help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
	)
	parser.add_argument(
	    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	__UpperCamelCase					    =	parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


