





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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


SCREAMING_SNAKE_CASE_   :      int			=   16
SCREAMING_SNAKE_CASE_   :      Dict			=   32



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = 16							)		->    List[Any]:
		__lowercase											=     AutoTokenizer.from_pretrained('bert-base-cased'							)
		__lowercase											=     load_dataset('glue'						,   'mrpc'							)

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

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

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

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

		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   mocked_dataloaders  # noqa: F811



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Any:
		# For testing only
		if os.environ.get('TESTING_MOCKED_DATALOADERS'						,   snake_case							) == "1":
				__lowercase											=     2
		# Initialize accelerator
		__lowercase											=     Accelerator(cpu=args.cpu						,   mixed_precision=args.mixed_precision							)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__lowercase											=     config['lr']
		__lowercase											=     int(config['num_epochs']							)
		__lowercase											=     int(config['seed']							)
		__lowercase											=     int(config['batch_size']							)

		__lowercase											=     evaluate.load('glue'						,   'mrpc'							)

		# If the batch size is too big we use gradient accumulation
		__lowercase											=     1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				__lowercase											=     batch_size // MAX_GPU_BATCH_SIZE
				__lowercase											=     MAX_GPU_BATCH_SIZE

		set_seed(snake_case							)
		__lowercase						,      __lowercase											=     get_dataloaders(snake_case						,   snake_case							)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__lowercase											=     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'						,   return_dict=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											=     model.to(accelerator.device							)

		# Instantiate optimizer
		__lowercase											=     AdamW(params=model.parameters()						,   lr=snake_case							)

		# Instantiate scheduler
		__lowercase											=     get_linear_schedule_with_warmup(
		    optimizer=snake_case						,   num_warmup_steps=100						,   num_training_steps=(len(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											=     accelerator.prepare(
		    snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case							)

		# Now we train the model
		for epoch in range(snake_case							):
				model.train()
				for step, batch in enumerate(snake_case							):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device							)
						__lowercase											=     model(**snake_case							)
						__lowercase											=     outputs.loss
						__lowercase											=     loss / gradient_accumulation_steps
						accelerator.backward(snake_case							)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				__lowercase											=     0
				for step, batch in enumerate(snake_case							):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device							)
						with torch.no_grad():
								__lowercase											=     model(**snake_case							)
						__lowercase											=     outputs.logits.argmax(dim=-1							)
						__lowercase						,      __lowercase											=     accelerator.gather((predictions, batch['labels'])							)
						# New Code #
						# First we check if it's a distributed system
						if accelerator.use_distributed:
								# Then see if we're on the last batch of our eval dataloader
								if step == len(snake_case							) - 1:
										# Last batch needs to be truncated on distributed systems as it contains additional samples
										__lowercase											=     predictions[: len(eval_dataloader.dataset							) - samples_seen]
										__lowercase											=     references[: len(eval_dataloader.dataset							) - samples_seen]
								else:
										# Otherwise we add the number of samples seen
										samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
						metric.add_batch(
						    predictions=snake_case						,   references=snake_case						,   )

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



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


if __name__ == "__main__":
		main()











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()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

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

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							) + 1: v for k, v in idalabel.items()}
		__lowercase											=     'background'
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

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

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

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

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

		if push_to_hub:
				print('Pushing to the hub...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




