





import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)





def lowerCAmelCase__(__snake_case							)       ->   Dict:







	'''simple docstring'''







	lowerCamelCase__              =	VideoMAEConfig()

	set_architecture_configs(A__ ,A__							)

	if "finetuned" not in model_name:
		lowerCamelCase__              =	False

	if "finetuned" in model_name:
		lowerCamelCase__              =	'''huggingface/label-files'''
		if "kinetics" in model_name:
			lowerCamelCase__              =	400
			lowerCamelCase__              =	'''kinetics400-id2label.json'''
		elif "ssv2" in model_name:
			lowerCamelCase__              =	174
			lowerCamelCase__              =	'''something-something-v2-id2label.json'''
		else:
			raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'''							)
		lowerCamelCase__              =	json.load(open(hf_hub_download(A__ ,A__ ,repo_type='''dataset'''							) ,'''r'''							)							)
		lowerCamelCase__              =	{int(A__							): v for k, v in idalabel.items()}
		lowerCamelCase__              =	idalabel
		lowerCamelCase__              =	{v: k for k, v in idalabel.items()}

	return config





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Dict:







	'''simple docstring'''







	if "small" in model_name:
		lowerCamelCase__              =	384
		lowerCamelCase__              =	1536
		lowerCamelCase__              =	12
		lowerCamelCase__              =	16
		lowerCamelCase__              =	12
		lowerCamelCase__              =	3
		lowerCamelCase__              =	192
		lowerCamelCase__              =	768
	elif "large" in model_name:
		lowerCamelCase__              =	1024
		lowerCamelCase__              =	4096
		lowerCamelCase__              =	24
		lowerCamelCase__              =	16
		lowerCamelCase__              =	12
		lowerCamelCase__              =	8
		lowerCamelCase__              =	512
		lowerCamelCase__              =	2048
	elif "huge" in model_name:
		lowerCamelCase__              =	1280
		lowerCamelCase__              =	5120
		lowerCamelCase__              =	32
		lowerCamelCase__              =	16
		lowerCamelCase__              =	12
		lowerCamelCase__              =	8
		lowerCamelCase__              =	640
		lowerCamelCase__              =	2560
	elif "base" not in model_name:
		raise ValueError('''Model name should include either \"small\", \"base\", \"large\", or \"huge\"'''							)





def lowerCAmelCase__(__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	if "encoder." in name:
		lowerCamelCase__              =	name.replace('''encoder.''' ,''''''							)
	if "cls_token" in name:
		lowerCamelCase__              =	name.replace('''cls_token''' ,'''videomae.embeddings.cls_token'''							)
	if "decoder_pos_embed" in name:
		lowerCamelCase__              =	name.replace('''decoder_pos_embed''' ,'''decoder.decoder_pos_embed'''							)
	if "pos_embed" in name and "decoder" not in name:
		lowerCamelCase__              =	name.replace('''pos_embed''' ,'''videomae.embeddings.position_embeddings'''							)
	if "patch_embed.proj" in name:
		lowerCamelCase__              =	name.replace('''patch_embed.proj''' ,'''videomae.embeddings.patch_embeddings.projection'''							)
	if "patch_embed.norm" in name:
		lowerCamelCase__              =	name.replace('''patch_embed.norm''' ,'''videomae.embeddings.norm'''							)
	if "decoder.blocks" in name:
		lowerCamelCase__              =	name.replace('''decoder.blocks''' ,'''decoder.decoder_layers'''							)
	if "blocks" in name:
		lowerCamelCase__              =	name.replace('''blocks''' ,'''videomae.encoder.layer'''							)
	if "attn.proj" in name:
		lowerCamelCase__              =	name.replace('''attn.proj''' ,'''attention.output.dense'''							)
	if "attn" in name and "bias" not in name:
		lowerCamelCase__              =	name.replace('''attn''' ,'''attention.self'''							)
	if "attn" in name:
		lowerCamelCase__              =	name.replace('''attn''' ,'''attention.attention'''							)
	if "norm1" in name:
		lowerCamelCase__              =	name.replace('''norm1''' ,'''layernorm_before'''							)
	if "norm2" in name:
		lowerCamelCase__              =	name.replace('''norm2''' ,'''layernorm_after'''							)
	if "mlp.fc1" in name:
		lowerCamelCase__              =	name.replace('''mlp.fc1''' ,'''intermediate.dense'''							)
	if "mlp.fc2" in name:
		lowerCamelCase__              =	name.replace('''mlp.fc2''' ,'''output.dense'''							)
	if "decoder_embed" in name:
		lowerCamelCase__              =	name.replace('''decoder_embed''' ,'''decoder.decoder_embed'''							)
	if "decoder_norm" in name:
		lowerCamelCase__              =	name.replace('''decoder_norm''' ,'''decoder.decoder_norm'''							)
	if "decoder_pred" in name:
		lowerCamelCase__              =	name.replace('''decoder_pred''' ,'''decoder.decoder_pred'''							)
	if "norm.weight" in name and "decoder" not in name and "fc" not in name:
		lowerCamelCase__              =	name.replace('''norm.weight''' ,'''videomae.layernorm.weight'''							)
	if "norm.bias" in name and "decoder" not in name and "fc" not in name:
		lowerCamelCase__              =	name.replace('''norm.bias''' ,'''videomae.layernorm.bias'''							)
	if "head" in name and "decoder" not in name:
		lowerCamelCase__              =	name.replace('''head''' ,'''classifier'''							)

	return name





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Optional[int]:







	'''simple docstring'''







	for key in orig_state_dict.copy().keys():
		lowerCamelCase__              =	orig_state_dict.pop(A__							)

		if key.startswith('''encoder.'''							):
			lowerCamelCase__              =	key.replace('''encoder.''' ,''''''							)

		if "qkv" in key:
			lowerCamelCase__              =	key.split('''.'''							)
			if key.startswith('''decoder.blocks'''							):
				lowerCamelCase__              =	config.decoder_hidden_size
				lowerCamelCase__              =	int(key_split[2]							)
				lowerCamelCase__              =	'''decoder.decoder_layers.'''
				if "weight" in key:
					lowerCamelCase__              =	val[:dim, :]
					lowerCamelCase__              =	val[dim : dim * 2, :]
					lowerCamelCase__              =	val[-dim:, :]
			else:
				lowerCamelCase__              =	config.hidden_size
				lowerCamelCase__              =	int(key_split[1]							)
				lowerCamelCase__              =	'''videomae.encoder.layer.'''
				if "weight" in key:
					lowerCamelCase__              =	val[:dim, :]
					lowerCamelCase__              =	val[dim : dim * 2, :]
					lowerCamelCase__              =	val[-dim:, :]
		else:
			lowerCamelCase__              =	val

	return orig_state_dict





def lowerCAmelCase__()       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	hf_hub_download(
	    repo_id='''hf-internal-testing/spaghetti-video''' ,filename='''eating_spaghetti.npy''' ,repo_type='''dataset'''							)
	lowerCamelCase__              =	np.load(A__							)
	return list(A__							)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	get_videomae_config(A__							)

	if "finetuned" in model_name:
		lowerCamelCase__              =	VideoMAEForVideoClassification(A__							)
	else:
		lowerCamelCase__              =	VideoMAEForPreTraining(A__							)

	# download original checkpoint, hosted on Google Drive
	lowerCamelCase__              =	'''pytorch_model.bin'''
	gdown.cached_download(A__ ,A__ ,quiet=A__							)
	lowerCamelCase__              =	torch.load(A__ ,map_location='''cpu'''							)
	if "model" in files:
		lowerCamelCase__              =	files['''model''']
	else:
		lowerCamelCase__              =	files['''module''']
	lowerCamelCase__              =	convert_state_dict(A__ ,A__							)

	model.load_state_dict(A__							)
	model.eval()

	# verify model on basic input
	lowerCamelCase__              =	VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,image_std=[0.5, 0.5, 0.5]							)
	lowerCamelCase__              =	prepare_video()
	lowerCamelCase__              =	image_processor(A__ ,return_tensors='''pt'''							)

	if "finetuned" not in model_name:
		lowerCamelCase__              =	hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' ,filename='''bool_masked_pos.pt'''							)
		lowerCamelCase__              =	torch.load(A__							)

	lowerCamelCase__              =	model(**A__							)
	lowerCamelCase__              =	outputs.logits

	lowerCamelCase__              =	[
	    '''videomae-small-finetuned-kinetics''',
	    '''videomae-small-finetuned-ssv2''',
	    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
	    '''videomae-base-short''',
	    '''videomae-base-short-finetuned-kinetics''',
	    '''videomae-base''',
	    '''videomae-base-finetuned-kinetics''',
	    '''videomae-large''',
	    '''videomae-large-finetuned-kinetics''',
	    '''videomae-huge-finetuned-kinetics''',
	    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
	    '''videomae-base-short-ssv2''',
	    '''videomae-base-short-finetuned-ssv2''',
	    '''videomae-base-ssv2''',
	    '''videomae-base-finetuned-ssv2''',
	]

	# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
	if model_name == "videomae-small-finetuned-kinetics":
		lowerCamelCase__              =	torch.Size([1, 400]							)
		lowerCamelCase__              =	torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]							)
	elif model_name == "videomae-small-finetuned-ssv2":
		lowerCamelCase__              =	torch.Size([1, 174]							)
		lowerCamelCase__              =	torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]							)
	elif model_name == "videomae-base":
		lowerCamelCase__              =	torch.Size([1, 1408, 1536]							)
		lowerCamelCase__              =	torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]							)
	elif model_name == "videomae-base-short":
		lowerCamelCase__              =	torch.Size([1, 1408, 1536]							)
		lowerCamelCase__              =	torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]							)
		# we verified the loss both for normalized and unnormalized targets for this one
		lowerCamelCase__              =	torch.tensor([0.5_1_4_2]							) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]							)
	elif model_name == "videomae-large":
		lowerCamelCase__              =	torch.Size([1, 1408, 1536]							)
		lowerCamelCase__              =	torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]							)
	elif model_name == "videomae-large-finetuned-kinetics":
		lowerCamelCase__              =	torch.Size([1, 400]							)
		lowerCamelCase__              =	torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]							)
	elif model_name == "videomae-huge-finetuned-kinetics":
		lowerCamelCase__              =	torch.Size([1, 400]							)
		lowerCamelCase__              =	torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]							)
	elif model_name == "videomae-base-short-finetuned-kinetics":
		lowerCamelCase__              =	torch.Size([1, 400]							)
		lowerCamelCase__              =	torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]							)
	elif model_name == "videomae-base-finetuned-kinetics":
		lowerCamelCase__              =	torch.Size([1, 400]							)
		lowerCamelCase__              =	torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]							)
	elif model_name == "videomae-base-short-ssv2":
		lowerCamelCase__              =	torch.Size([1, 1408, 1536]							)
		lowerCamelCase__              =	torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]							)
	elif model_name == "videomae-base-short-finetuned-ssv2":
		lowerCamelCase__              =	torch.Size([1, 174]							)
		lowerCamelCase__              =	torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]							)
	elif model_name == "videomae-base-ssv2":
		lowerCamelCase__              =	torch.Size([1, 1408, 1536]							)
		lowerCamelCase__              =	torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]							)
	elif model_name == "videomae-base-finetuned-ssv2":
		lowerCamelCase__              =	torch.Size([1, 174]							)
		lowerCamelCase__              =	torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]							)
	else:
		raise ValueError(F'Model name not supported. Should be one of {model_names}'							)

	# verify logits
	assert logits.shape == expected_shape
	if "finetuned" in model_name:
		assert torch.allclose(logits[0, :3] ,A__ ,atol=1E-4							)
	else:
		print('''Logits:''' ,logits[0, :3, :3]							)
		assert torch.allclose(logits[0, :3, :3] ,A__ ,atol=1E-4							)
	print('''Logits ok!'''							)

	# verify loss, if applicable
	if model_name == "videomae-base-short":
		lowerCamelCase__              =	outputs.loss
		assert torch.allclose(A__ ,A__ ,atol=1E-4							)
		print('''Loss ok!'''							)

	if pytorch_dump_folder_path is not None:
		print(F'Saving model and image processor to {pytorch_dump_folder_path}'							)
		image_processor.save_pretrained(A__							)
		model.save_pretrained(A__							)

	if push_to_hub:
		print('''Pushing to the hub...'''							)
		model.push_to_hub(A__ ,organization='''nielsr'''							)


if __name__ == "__main__":
			_a						=   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--checkpoint_url",
			    default="https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4",
			    type=str,
			    help=(
			        "URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct"
			        " download link."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default="/Users/nielsrogge/Documents/VideoMAE/Test",
			    type=str,
			    help="Path to the output PyTorch model directory.",
			)
			parser.add_argument("--model_name", default="videomae-base", type=str, help="Name of the model.")
			parser.add_argument(
			    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
			)

			_a						=   parser.parse_args()
			convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


_a						=   open  # noqa: we just need to have a builtin inside this module to test it properly



