


import argparse
import json
from pathlib import Path

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

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase       :      Tuple	=	logging.get_logger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   YolosConfig:





	"""simple docstring"""







	A								=				YolosConfig()

	# size of the architecture
	if "yolos_ti" in yolos_name:
		A								=				1_92
		A								=				7_68
		A								=				12
		A								=				3
		A								=				[8_00, 13_33]
		A								=				False
	elif yolos_name == "yolos_s_dWr":
		A								=				3_30
		A								=				14
		A								=				6
		A								=				13_20
	elif "yolos_s" in yolos_name:
		A								=				3_84
		A								=				15_36
		A								=				12
		A								=				6
	elif "yolos_b" in yolos_name:
		A								=				[8_00, 13_44]

	A								=				91
	A								=				"""huggingface/label-files"""
	A								=				"""coco-detection-id2label.json"""
	A								=				json.load(open(hf_hub_download(UpperCamelCase__							,					UpperCamelCase__							,					repo_type="""dataset"""						)							,					"""r"""						)						)
	A								=				{int(UpperCamelCase__						): v for k, v in idalabel.items()}
	A								=				idalabel
	A								=				{v: k for k, v in idalabel.items()}

	return config





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     YolosConfig							,					UpperCamelCase__:     bool = False						) ->   Any:





	"""simple docstring"""







	for i in range(config.num_hidden_layers						):
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		A								=				state_dict.pop(f'blocks.{i}.attn.qkv.weight'						)
		A								=				state_dict.pop(f'blocks.{i}.attn.qkv.bias'						)
		# next, add query, keys and values (in that order) to the state dict
		A								=				in_proj_weight[: config.hidden_size, :]
		A								=				in_proj_bias[: config.hidden_size]
		A								=				in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A								=				in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A								=				in_proj_weight[-config.hidden_size :, :]
		A								=				in_proj_bias[-config.hidden_size :]





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







	if "backbone" in name:
		A								=				name.replace("""backbone"""							,					"""vit"""						)
	if "cls_token" in name:
		A								=				name.replace("""cls_token"""							,					"""embeddings.cls_token"""						)
	if "det_token" in name:
		A								=				name.replace("""det_token"""							,					"""embeddings.detection_tokens"""						)
	if "mid_pos_embed" in name:
		A								=				name.replace("""mid_pos_embed"""							,					"""encoder.mid_position_embeddings"""						)
	if "pos_embed" in name:
		A								=				name.replace("""pos_embed"""							,					"""embeddings.position_embeddings"""						)
	if "patch_embed.proj" in name:
		A								=				name.replace("""patch_embed.proj"""							,					"""embeddings.patch_embeddings.projection"""						)
	if "blocks" in name:
		A								=				name.replace("""blocks"""							,					"""encoder.layer"""						)
	if "attn.proj" in name:
		A								=				name.replace("""attn.proj"""							,					"""attention.output.dense"""						)
	if "attn" in name:
		A								=				name.replace("""attn"""							,					"""attention.self"""						)
	if "norm1" in name:
		A								=				name.replace("""norm1"""							,					"""layernorm_before"""						)
	if "norm2" in name:
		A								=				name.replace("""norm2"""							,					"""layernorm_after"""						)
	if "mlp.fc1" in name:
		A								=				name.replace("""mlp.fc1"""							,					"""intermediate.dense"""						)
	if "mlp.fc2" in name:
		A								=				name.replace("""mlp.fc2"""							,					"""output.dense"""						)
	if "class_embed" in name:
		A								=				name.replace("""class_embed"""							,					"""class_labels_classifier"""						)
	if "bbox_embed" in name:
		A								=				name.replace("""bbox_embed"""							,					"""bbox_predictor"""						)
	if "vit.norm" in name:
		A								=				name.replace("""vit.norm"""							,					"""vit.layernorm"""						)

	return name





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     YolosForObjectDetection						) ->   dict:





	"""simple docstring"""







	for key in orig_state_dict.copy().keys():
		A								=				orig_state_dict.pop(UpperCamelCase__						)

		if "qkv" in key:
			A								=				key.split("""."""						)
			A								=				int(key_split[2]						)
			A								=				model.vit.encoder.layer[layer_num].attention.attention.all_head_size
			if "weight" in key:
				A								=				val[:dim, :]
				A								=				val[
				    dim : dim * 2, :
				]
				A								=				val[-dim:, :]
			else:
				A								=				val[:dim]
				A								=				val[dim : dim * 2]
				A								=				val[-dim:]
		else:
			A								=				val

	return orig_state_dict





def   _lowerCAmelCase				(   ) ->   torch.Tensor:





	"""simple docstring"""







	A								=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A								=				Image.open(requests.get(UpperCamelCase__							,					stream=UpperCamelCase__						).raw						)
	return im





@torch.no_grad()
def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     str							,					UpperCamelCase__:     str							,					UpperCamelCase__:     bool = False						) ->   Optional[int]:





	"""simple docstring"""







	A								=				get_yolos_config(UpperCamelCase__						)

	# load original state_dict
	A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)["""model"""]

	# load 🤗 model
	A								=				YolosForObjectDetection(UpperCamelCase__						)
	model.eval()
	A								=				convert_state_dict(UpperCamelCase__							,					UpperCamelCase__						)
	model.load_state_dict(UpperCamelCase__						)

	# Check outputs on an image, prepared by YolosImageProcessor
	A								=				8_00 if yolos_name != """yolos_ti""" else 5_12
	A								=				YolosImageProcessor(format="""coco_detection"""							,					size=UpperCamelCase__						)
	A								=				image_processor(images=prepare_img()							,					return_tensors="""pt"""						)
	A								=				model(**UpperCamelCase__						)
	A					,			A								=				outputs.logits, outputs.pred_boxes

	A					,			A								=				None, None
	if yolos_name == "yolos_ti":
		A								=				torch.tensor(
		    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]						)
		A								=				torch.tensor(
		    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]						)
	elif yolos_name == "yolos_s_200_pre":
		A								=				torch.tensor(
		    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]						)
		A								=				torch.tensor(
		    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]						)
	elif yolos_name == "yolos_s_300_pre":
		A								=				torch.tensor(
		    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]						)
		A								=				torch.tensor(
		    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]						)
	elif yolos_name == "yolos_s_dWr":
		A								=				torch.tensor(
		    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]						)
		A								=				torch.tensor(
		    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]						)
	elif yolos_name == "yolos_base":
		A								=				torch.tensor(
		    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]						)
		A								=				torch.tensor(
		    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]						)
	else:
		raise ValueError(f'Unknown yolos_name: {yolos_name}'						)

	assert torch.allclose(logits[0, :3, :3]							,					UpperCamelCase__							,					atol=1e-4						)
	assert torch.allclose(pred_boxes[0, :3, :3]							,					UpperCamelCase__							,					atol=1e-4						)

	Path(UpperCamelCase__						).mkdir(exist_ok=UpperCamelCase__						)
	print(f'Saving model {yolos_name} to {pytorch_dump_folder_path}'						)
	model.save_pretrained(UpperCamelCase__						)
	print(f'Saving image processor to {pytorch_dump_folder_path}'						)
	image_processor.save_pretrained(UpperCamelCase__						)

	if push_to_hub:
		A								=				{
		    """yolos_ti""": """yolos-tiny""",
		    """yolos_s_200_pre""": """yolos-small""",
		    """yolos_s_300_pre""": """yolos-small-300""",
		    """yolos_s_dWr""": """yolos-small-dwr""",
		    """yolos_base""": """yolos-base""",
		}

		print("""Pushing to the hub..."""						)
		A								=				model_mapping[yolos_name]
		image_processor.push_to_hub(UpperCamelCase__							,					organization="""hustvl"""						)
		model.push_to_hub(UpperCamelCase__							,					organization="""hustvl"""						)


if __name__ == "__main__":
				_lowercase       :      List[Any]	=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--yolos_name",
				    default="yolos_s_200_pre",
				    type=str,
				    help=(
				        "Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
				        " 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
				    ),
				)
				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
				)
				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 or not to push the converted model to the 🤗 hub."
				)

				_lowercase       :      str	=	parser.parse_args()
				convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






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


_lowercase       :      Optional[Any]	=	16
_lowercase       :      List[Any]	=	32





def   _lowerCAmelCase				(   UpperCamelCase__:     Accelerator							,					UpperCamelCase__:     int = 16						) ->   Tuple:





	"""simple docstring"""







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

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

	def collate_fn(UpperCamelCase__:     Union[str, Any]						):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A								=				1_28 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A								=				16
		elif accelerator.mixed_precision != "no":
			A								=				8
		else:
			A								=				None

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

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

	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       :      List[Any]	=	mocked_dataloaders  # noqa: F811





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   int:





	"""simple docstring"""







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

	A								=				evaluate.load("""glue"""							,					"""mrpc"""						)

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

	set_seed(UpperCamelCase__						)
	A					,			A								=				get_dataloaders(UpperCamelCase__							,					UpperCamelCase__						)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A								=				AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,					return_dict=UpperCamelCase__						)

	# 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).
	A								=				model.to(accelerator.device						)

	# Instantiate optimizer
	A								=				AdamW(params=model.parameters()							,					lr=UpperCamelCase__						)

	# Instantiate scheduler
	A								=				get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase__							,					num_warmup_steps=1_00							,					num_training_steps=(len(UpperCamelCase__						) * 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.
	A					,			A					,			A					,			A					,			A								=				accelerator.prepare(
	    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)

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

		model.eval()
		A								=				0
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			with torch.no_grad():
				A								=				model(**UpperCamelCase__						)
			A								=				outputs.logits.argmax(dim=-1						)
			A					,			A								=				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(UpperCamelCase__						) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					A								=				predictions[: len(eval_dataloader.dataset						) - samples_seen]
					A								=				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=UpperCamelCase__							,					references=UpperCamelCase__							,					)

		A								=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,					UpperCamelCase__						)





def   _lowerCAmelCase				(   ) ->   Dict:





	"""simple docstring"""







	A								=				argparse.ArgumentParser(description="""Simple example of training script."""						)
	parser.add_argument(
	    """--mixed_precision"""							,					type=UpperCamelCase__							,					default=UpperCamelCase__							,					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."""						)
	A								=				parser.parse_args()
	A								=				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(UpperCamelCase__							,					UpperCamelCase__						)


if __name__ == "__main__":
				main()



