




import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


lowercase__							:	Dict											=		16
lowercase__							:	str											=		32


def 		lowerCamelCase__       (						_A	,      _A = 16	,      _A = "bert-base-cased"				):







	'''simple docstring'''





	snake_case_						       =	AutoTokenizer.from_pretrained(_A				)
	snake_case_						       =	load_dataset("glue"	,      "mrpc"				)

	def tokenize_function(_A				):
		# max_length=None => use the model max length (it's actually the default)
		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
	snake_case_						       =	datasets.map(
	    _A	,      batched=_A	,      remove_columns=["idx", "sentence1", "sentence2"]	,      load_from_cache_file=_A				)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	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.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_A	,      padding="max_length"	,      max_length=128	,      return_tensors="pt"				)
		return tokenizer.pad(_A	,      padding="longest"	,      return_tensors="pt"				)

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

	return train_dataloader, eval_dataloader


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	snake_case_						       =	config["lr"]
	snake_case_						       =	int(config["num_epochs"]				)
	snake_case_						       =	int(config["seed"]				)
	snake_case_						       =	int(config["batch_size"]				)
	snake_case_						       =	args.model_name_or_path

	set_seed(_A				)
	snake_case_       , snake_case_						       =	get_dataloaders(_A	,      _A	,      _A				)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case_						       =	AutoModelForSequenceClassification.from_pretrained(_A	,      return_dict=_A				)

	# Instantiate optimizer
	snake_case_						       =	(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	snake_case_						       =	optimizer_cls(params=model.parameters()	,      lr=_A				)

	if accelerator.state.deepspeed_plugin is not None:
		snake_case_						       =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		snake_case_						       =	1
	snake_case_						       =	(len(_A				) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		snake_case_						       =	get_linear_schedule_with_warmup(
		    optimizer=_A	,      num_warmup_steps=0	,      num_training_steps=_A	,      )
	else:
		snake_case_						       =	DummyScheduler(_A	,      total_num_steps=_A	,      warmup_num_steps=0				)

	# 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_						       =	accelerator.prepare(
	    _A	,      _A	,      _A	,      _A	,      _A				)

	# We need to keep track of how many total steps we have iterated over
	snake_case_						       =	0
	# We also need to keep track of the stating epoch so files are named properly
	snake_case_						       =	0

	# Now we train the model
	snake_case_						       =	evaluate.load("glue"	,      "mrpc"				)
	snake_case_						       =	0
	snake_case_						       =	{}
	for epoch in range(_A	,      _A				):
		model.train()
		for step, batch in enumerate(_A				):
			snake_case_						       =	model(**_A				)
			snake_case_						       =	outputs.loss
			snake_case_						       =	loss / gradient_accumulation_steps
			accelerator.backward(_A				)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		snake_case_						       =	0
		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():
				snake_case_						       =	model(**_A				)
			snake_case_						       =	outputs.logits.argmax(dim=-1				)
			# It is slightly faster to call this once, than multiple times
			snake_case_       , snake_case_						       =	accelerator.gather(
			    (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(_A				) - 1:
					snake_case_						       =	predictions[: len(eval_dataloader.dataset				) - samples_seen]
					snake_case_						       =	references[: len(eval_dataloader.dataset				) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=_A	,      references=_A	,      )

		snake_case_						       =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"epoch {epoch}:"	,      _A				)
		snake_case_						       =	eval_metric["accuracy"]

		if best_performance < eval_metric["accuracy"]:
			snake_case_						       =	eval_metric["accuracy"]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir	,      "all_results.json"				)	,      "w"				) as f:
			json.dump(_A	,      _A				)


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	snake_case_						       =	argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
	parser.add_argument(
	    "--model_name_or_path"	,      type=_A	,      default="bert-base-cased"	,      help="Path to pretrained model or model identifier from huggingface.co/models."	,      required=_A	,      )
	parser.add_argument(
	    "--output_dir"	,      type=_A	,      default="."	,      help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,      )
	parser.add_argument(
	    "--performance_lower_bound"	,      type=_A	,      default=_A	,      help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."	,      )
	parser.add_argument(
	    "--num_epochs"	,      type=_A	,      default=3	,      help="Number of train epochs."	,      )
	snake_case_						       =	parser.parse_args()
	snake_case_						       =	{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
	training_function(_A	,      _A				)


if __name__ == "__main__":
		main()






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	old_name

	if "patch_embed" in old_name:
		snake_case_       , snake_case_       , snake_case_						       =	old_name.split("."				)

		if layer == "0":
			snake_case_						       =	old_name.replace("0"	,      "convolution1"				)
		elif layer == "1":
			snake_case_						       =	old_name.replace("1"	,      "batchnorm_before"				)
		elif layer == "3":
			snake_case_						       =	old_name.replace("3"	,      "convolution2"				)
		else:
			snake_case_						       =	old_name.replace("4"	,      "batchnorm_after"				)

	if "network" in old_name and re.search(R"\d\.\d"	,      _A				):
		snake_case_						       =	R"\b\d{2}\b"
		if bool(re.search(_A	,      _A				)				):
			snake_case_						       =	re.search(R"\d\.\d\d."	,      _A				).group()
		else:
			snake_case_						       =	re.search(R"\d\.\d."	,      _A				).group()
		if int(match[0]				) < 6:
			snake_case_						       =	old_name.replace(_A	,      ""				)
			snake_case_						       =	trimmed_name.replace("network"	,      match[0] + ".meta4D_layers.blocks." + match[2:-1]				)
			snake_case_						       =	"intermediate_stages." + trimmed_name
		else:
			snake_case_						       =	old_name.replace(_A	,      ""				)
			if int(match[2]				) < num_meta4D_last_stage:
				snake_case_						       =	trimmed_name.replace("network"	,      "meta4D_layers.blocks." + match[2]				)
			else:
				snake_case_						       =	str(int(match[2]				) - num_meta4D_last_stage				)
				snake_case_						       =	trimmed_name.replace("network"	,      "meta3D_layers.blocks." + layer_index				)
				if "norm1" in old_name:
					snake_case_						       =	trimmed_name.replace("norm1"	,      "layernorm1"				)
				elif "norm2" in old_name:
					snake_case_						       =	trimmed_name.replace("norm2"	,      "layernorm2"				)
				elif "fc1" in old_name:
					snake_case_						       =	trimmed_name.replace("fc1"	,      "linear_in"				)
				elif "fc2" in old_name:
					snake_case_						       =	trimmed_name.replace("fc2"	,      "linear_out"				)

			snake_case_						       =	"last_stage." + trimmed_name

	elif "network" in old_name and re.search(R".\d."	,      _A				):
		snake_case_						       =	old_name.replace("network"	,      "intermediate_stages"				)

	if "fc" in new_name:
		snake_case_						       =	new_name.replace("fc"	,      "convolution"				)
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		snake_case_						       =	new_name.replace("norm1"	,      "batchnorm_before"				)
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		snake_case_						       =	new_name.replace("norm2"	,      "batchnorm_after"				)
	if "proj" in new_name:
		snake_case_						       =	new_name.replace("proj"	,      "projection"				)
	if "dist_head" in new_name:
		snake_case_						       =	new_name.replace("dist_head"	,      "distillation_classifier"				)
	elif "head" in new_name:
		snake_case_						       =	new_name.replace("head"	,      "classifier"				)
	elif "patch_embed" in new_name:
		snake_case_						       =	"efficientformer." + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		snake_case_						       =	new_name.replace("norm"	,      "layernorm"				)
		snake_case_						       =	"efficientformer." + new_name
	else:
		snake_case_						       =	"efficientformer.encoder." + new_name

	return new_name


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	for key in checkpoint.copy().keys():
		snake_case_						       =	checkpoint.pop(_A				)
		snake_case_						       =	val

	return checkpoint


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	snake_case_						       =	"http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case_						       =	Image.open(requests.get(_A	,      stream=_A				).raw				)

	return image


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	torch.load(_A	,      map_location="cpu"				)["model"]
	snake_case_						       =	EfficientFormerConfig.from_json_file(_A				)
	snake_case_						       =	EfficientFormerForImageClassificationWithTeacher(_A				)
	snake_case_						       =	"_".join(checkpoint_path.split("/"				)[-1].split("."				)[0].split("_"				)[:-1]				)

	snake_case_						       =	config.depths[-1] - config.num_metaad_blocks + 1
	snake_case_						       =	convert_torch_checkpoint(_A	,      _A				)

	model.load_state_dict(_A				)
	model.eval()

	snake_case_						       =	{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	# prepare image
	snake_case_						       =	prepare_img()
	snake_case_						       =	256
	snake_case_						       =	224
	snake_case_						       =	EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}	,      crop_size={"height": crop_size, "width": crop_size}	,      resample=pillow_resamplings["bicubic"]	,      )
	snake_case_						       =	processor(images=_A	,      return_tensors="pt"				).pixel_values

	# original processing pipeline
	snake_case_						       =	Compose(
	    [
	        Resize(_A	,      interpolation=pillow_resamplings["bicubic"]				),
	        CenterCrop(_A				),
	        ToTensor(),
	        Normalize(_A	,      _A				),
	    ]				)
	snake_case_						       =	image_transforms(_A				).unsqueeze(0				)

	assert torch.allclose(_A	,      _A				)

	snake_case_						       =	model(_A				)
	snake_case_						       =	outputs.logits

	snake_case_						       =	(1, 1000)

	if "l1" in model_name:
		snake_case_						       =	torch.Tensor(
		    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]				)
		assert torch.allclose(logits[0, :10]	,      _A	,      atol=1E-3				)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		snake_case_						       =	torch.Tensor(
		    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]				)
		assert torch.allclose(logits[0, :10]	,      _A	,      atol=1E-3				)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		snake_case_						       =	torch.Tensor(
		    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]				)
		assert logits.shape == expected_shape
	else:
		raise ValueError(
		    f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"				)

	# Save Checkpoints
	Path(_A				).mkdir(exist_ok=_A				)
	model.save_pretrained(_A				)
	print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"				)
	processor.save_pretrained(_A				)
	print(f"Processor successfuly saved at {pytorch_dump_path}"				)

	if push_to_hub:
		print("Pushing model to the hub..."				)

		model.push_to_hub(
		    repo_id=f"Bearnardd/{pytorch_dump_path}"	,      commit_message="Add model"	,      use_temp_dir=_A	,      )
		processor.push_to_hub(
		    repo_id=f"Bearnardd/{pytorch_dump_path}"	,      commit_message="Add image processor"	,      use_temp_dir=_A	,      )


if __name__ == "__main__":
		lowercase__							:	Tuple											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Path to EfficientFormer pytorch checkpoint.",
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for EfficientFormer model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)

		parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
		parser.add_argument(
		    "--no-push_to_hub",
		    dest="push_to_hub",
		    action="store_false",
		    help="Do not push model and image processor to the hub",
		)
		parser.set_defaults(push_to_hub=True)

		lowercase__							:	Optional[Any]											=		parser.parse_args()
		convert_efficientformer_checkpoint(
		    checkpoint_path=args.pytorch_model_path,
		    efficientformer_config_file=args.config_file,
		    pytorch_dump_path=args.pytorch_dump_path,
		    push_to_hub=args.push_to_hub,
		)
