
"""simple docstring"""




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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase__				=					16
lowerCAmelCase__				=					32



def 							_lowerCamelCase			(			__a,				__a = 16     ):
		SCREAMING_SNAKE_CASE_    			=			AutoTokenizer.from_pretrained('''bert-base-cased'''     )
		SCREAMING_SNAKE_CASE_    			=			load_dataset('''glue''',				'''mrpc'''     )

		def tokenize_function(__a     ):
				# max_length=None => use the model max length (it's actually the default)
				SCREAMING_SNAKE_CASE_    			=			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():
				SCREAMING_SNAKE_CASE_    			=			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
		SCREAMING_SNAKE_CASE_    			=			tokenized_datasets.rename_column('''label''',				'''labels'''     )

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

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

		# Instantiate dataloaders.
		SCREAMING_SNAKE_CASE_    			=			DataLoader(
		    tokenized_datasets['''train'''],				shuffle=__a,				collate_fn=__a,				batch_size=__a     )
		SCREAMING_SNAKE_CASE_    			=			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

						lowerCAmelCase__				=					mocked_dataloaders  # noqa: F811



def 							_lowerCamelCase			(			__a,				__a     ):
		# For testing only
		if os.environ.get('''TESTING_MOCKED_DATALOADERS''',				__a     ) == "1":
				SCREAMING_SNAKE_CASE_    			=			2
		# New Code #
		SCREAMING_SNAKE_CASE_    			=			int(args.gradient_accumulation_steps     )
		SCREAMING_SNAKE_CASE_    			=			int(args.local_sgd_steps     )
		# Initialize accelerator
		SCREAMING_SNAKE_CASE_    			=			Accelerator(
		    cpu=args.cpu,				mixed_precision=args.mixed_precision,				gradient_accumulation_steps=__a     )
		if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
				raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''     )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		SCREAMING_SNAKE_CASE_    			=			config['''lr''']
		SCREAMING_SNAKE_CASE_    			=			int(config['''num_epochs''']     )
		SCREAMING_SNAKE_CASE_    			=			int(config['''seed''']     )
		SCREAMING_SNAKE_CASE_    			=			int(config['''batch_size''']     )

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

		set_seed(__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			get_dataloaders(__a,				__a     )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		SCREAMING_SNAKE_CASE_    			=			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).
		SCREAMING_SNAKE_CASE_    			=			model.to(accelerator.device     )

		# Instantiate optimizer
		SCREAMING_SNAKE_CASE_    			=			AdamW(params=model.parameters(),				lr=__a     )

		# Instantiate scheduler
		SCREAMING_SNAKE_CASE_    			=			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.
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			accelerator.prepare(
		    __a,				__a,				__a,				__a,				__a     )

		# Now we train the model
		for epoch in range(__a     ):
				model.train()
				with LocalSGD(
				    accelerator=__a,				model=__a,				local_sgd_steps=__a,				enabled=local_sgd_steps is not None     ) as local_sgd:
						for step, batch in enumerate(__a     ):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device     )
								# New code #
								# We use the new `accumulate` context manager to perform gradient accumulation
								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
								with accelerator.accumulate(__a     ):
										SCREAMING_SNAKE_CASE_    			=			model(**__a     )
										SCREAMING_SNAKE_CASE_    			=			output.loss
										accelerator.backward(__a     )
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# LocalSGD-specific line
										local_sgd.step()

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

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



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			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.''',				)
		# New Code #
		parser.add_argument(
		    '''--gradient_accumulation_steps''',				type=__a,				default=1,				help='''The number of minibatches to be ran before gradients are accumulated.''',				)
		parser.add_argument(
		    '''--local_sgd_steps''',				type=__a,				default=8,				help='''Number of local SGD steps or None to disable local SGD'''     )
		parser.add_argument('''--cpu''',				action='''store_true''',				help='''If passed, will train on the CPU.'''     )
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		SCREAMING_SNAKE_CASE_    			=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__a,				__a     )


if __name__ == "__main__":
						main()

"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			'''huggingface/label-files'''
		SCREAMING_SNAKE_CASE_    			=			'''imagenet-1k-id2label.json'''
		SCREAMING_SNAKE_CASE_    			=			json.load(open(hf_hub_download(__a,				__a,				repo_type='''dataset'''     ),				'''r'''     )     )
		SCREAMING_SNAKE_CASE_    			=			{int(__a     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_    			=			{v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE_    			=			'''std_conv''' if '''bit''' in model_name else False

		# note that when using BiT as backbone for ViT-hybrid checkpoints,
		# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
		# config.conv_layer = "std_conv_same"
		SCREAMING_SNAKE_CASE_    			=			BitConfig(
		    conv_layer=__a,				num_labels=1_000,				idalabel=__a,				labelaid=__a,				)

		return config



def 							_lowerCamelCase			(			__a     ):
		if "stem.conv" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''stem.conv''',				'''bit.embedder.convolution'''     )
		if "blocks" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''blocks''',				'''layers'''     )
		if "head.fc" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''head.fc''',				'''classifier.1'''     )
		if name.startswith('''norm'''     ):
				SCREAMING_SNAKE_CASE_    			=			'''bit.''' + name
		if "bit" not in name and "classifier" not in name:
				SCREAMING_SNAKE_CASE_    			=			'''bit.encoder.''' + name

		return name



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     )
		return im



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=False     ):
		SCREAMING_SNAKE_CASE_    			=			get_config(__a     )

		# load original model from timm
		SCREAMING_SNAKE_CASE_    			=			create_model(__a,				pretrained=__a     )
		timm_model.eval()

		# load state_dict of original model
		SCREAMING_SNAKE_CASE_    			=			timm_model.state_dict()
		for key in state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			val.squeeze() if '''head''' in key else val

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_    			=			BitForImageClassification(__a     )
		model.eval()
		model.load_state_dict(__a     )

		# create image processor
		SCREAMING_SNAKE_CASE_    			=			create_transform(**resolve_data_config({},				model=__a     )     )
		SCREAMING_SNAKE_CASE_    			=			transform.transforms

		SCREAMING_SNAKE_CASE_    			=			{
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

		SCREAMING_SNAKE_CASE_    			=			BitImageProcessor(
		    do_resize=__a,				size={'''shortest_edge''': timm_transforms[0].size},				resample=pillow_resamplings[timm_transforms[0].interpolation.value],				do_center_crop=__a,				crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]},				do_normalize=__a,				image_mean=timm_transforms[-1].mean.tolist(),				image_std=timm_transforms[-1].std.tolist(),				)

		SCREAMING_SNAKE_CASE_    			=			prepare_img()
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     )
		SCREAMING_SNAKE_CASE_    			=			processor(__a,				return_tensors='''pt'''     ).pixel_values

		# verify pixel values
		assert torch.allclose(__a,				__a     )

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_    			=			model(__a     )
				SCREAMING_SNAKE_CASE_    			=			outputs.logits

		print('''Logits:''',				logits[0, :3]     )
		print('''Predicted class:''',				model.config.idalabel[logits.argmax(-1     ).item()]     )
		SCREAMING_SNAKE_CASE_    			=			timm_model(__a     )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__a,				outputs.logits,				atol=1E-3     )
		print('''Looks ok!'''     )

		if pytorch_dump_folder_path is not None:
				Path(__a     ).mkdir(exist_ok=__a     )
				print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'     )
				model.save_pretrained(__a     )
				processor.save_pretrained(__a     )

		if push_to_hub:
				print(F'Pushing model {model_name} and processor to the hub'     )
				model.push_to_hub(F'ybelkada/{model_name}'     )
				processor.push_to_hub(F'ybelkada/{model_name}'     )


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='resnetv2_50x1_bitm',
						    type=str,
						    help='Name of the BiT timm model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model to the hub.',
						)

						lowerCAmelCase__				=					parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)