






"""simple docstring"""







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 _a			(		_snake_case							):







						"""simple docstring"""






						UpperCAmelCase          =	VideoMAEConfig()

						set_architecture_configs(_lowercase				,	_lowercase							)

						if "finetuned" not in model_name:
												UpperCAmelCase          =	False

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

						return config

def _a			(		_snake_case				,	_snake_case							):







						"""simple docstring"""






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

def _a			(		_snake_case							):







						"""simple docstring"""






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

						return name

def _a			(		_snake_case				,	_snake_case							):







						"""simple docstring"""






						for key in orig_state_dict.copy().keys():
												UpperCAmelCase          =	orig_state_dict.pop(_lowercase							)

												if key.startswith("""encoder."""							):
																		UpperCAmelCase          =	key.replace("""encoder."""				,	""""""							)

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

						return orig_state_dict

def _a			(		):







						"""simple docstring"""






						UpperCAmelCase          =	hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video"""				,	filename="""eating_spaghetti.npy"""				,	repo_type="""dataset"""							)
						UpperCAmelCase          =	np.load(_lowercase							)
						return list(_lowercase							)

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







						"""simple docstring"""






						UpperCAmelCase          =	get_videomae_config(_lowercase							)

						if "finetuned" in model_name:
												UpperCAmelCase          =	VideoMAEForVideoClassification(_lowercase							)
						else:
												UpperCAmelCase          =	VideoMAEForPreTraining(_lowercase							)

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

						model.load_state_dict(_lowercase							)
						model.eval()

						# verify model on basic input
						UpperCAmelCase          =	VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]				,	image_std=[0.5, 0.5, 0.5]							)
						UpperCAmelCase          =	prepare_video()
						UpperCAmelCase          =	image_processor(_lowercase				,	return_tensors="""pt"""							)

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

						UpperCAmelCase          =	model(**_lowercase							)
						UpperCAmelCase          =	outputs.logits

						UpperCAmelCase          =	[
						    """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":
												UpperCAmelCase          =	torch.Size([1, 400]							)
												UpperCAmelCase          =	torch.tensor([-0.9291, -0.4061, -0.9307]							)
						elif model_name == "videomae-small-finetuned-ssv2":
												UpperCAmelCase          =	torch.Size([1, 174]							)
												UpperCAmelCase          =	torch.tensor([0.2671, -0.4689, -0.8235]							)
						elif model_name == "videomae-base":
												UpperCAmelCase          =	torch.Size([1, 1408, 1536]							)
												UpperCAmelCase          =	torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]							)
						elif model_name == "videomae-base-short":
												UpperCAmelCase          =	torch.Size([1, 1408, 1536]							)
												UpperCAmelCase          =	torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]							)
												# we verified the loss both for normalized and unnormalized targets for this one
												UpperCAmelCase          =	torch.tensor([0.5142]							) if config.norm_pix_loss else torch.tensor([0.6469]							)
						elif model_name == "videomae-large":
												UpperCAmelCase          =	torch.Size([1, 1408, 1536]							)
												UpperCAmelCase          =	torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]							)
						elif model_name == "videomae-large-finetuned-kinetics":
												UpperCAmelCase          =	torch.Size([1, 400]							)
												UpperCAmelCase          =	torch.tensor([0.0771, 0.0011, -0.3625]							)
						elif model_name == "videomae-huge-finetuned-kinetics":
												UpperCAmelCase          =	torch.Size([1, 400]							)
												UpperCAmelCase          =	torch.tensor([0.2433, 0.1632, -0.4894]							)
						elif model_name == "videomae-base-short-finetuned-kinetics":
												UpperCAmelCase          =	torch.Size([1, 400]							)
												UpperCAmelCase          =	torch.tensor([0.6588, 0.0990, -0.2493]							)
						elif model_name == "videomae-base-finetuned-kinetics":
												UpperCAmelCase          =	torch.Size([1, 400]							)
												UpperCAmelCase          =	torch.tensor([0.3669, -0.0688, -0.2421]							)
						elif model_name == "videomae-base-short-ssv2":
												UpperCAmelCase          =	torch.Size([1, 1408, 1536]							)
												UpperCAmelCase          =	torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]							)
						elif model_name == "videomae-base-short-finetuned-ssv2":
												UpperCAmelCase          =	torch.Size([1, 174]							)
												UpperCAmelCase          =	torch.tensor([-0.0537, -0.1539, -0.3266]							)
						elif model_name == "videomae-base-ssv2":
												UpperCAmelCase          =	torch.Size([1, 1408, 1536]							)
												UpperCAmelCase          =	torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]							)
						elif model_name == "videomae-base-finetuned-ssv2":
												UpperCAmelCase          =	torch.Size([1, 174]							)
												UpperCAmelCase          =	torch.tensor([0.1961, -0.8337, -0.6389]							)
						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]				,	_lowercase				,	atol=1E-4							)
						else:
												print("""Logits:"""				,	logits[0, :3, :3]							)
												assert torch.allclose(logits[0, :3, :3]				,	_lowercase				,	atol=1E-4							)
						print("""Logits ok!"""							)

						# verify loss, if applicable
						if model_name == "videomae-base-short":
												UpperCAmelCase          =	outputs.loss
												assert torch.allclose(_lowercase				,	_lowercase				,	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(_lowercase							)
												model.save_pretrained(_lowercase							)

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


if __name__ == "__main__":
		_UpperCamelCase					      =  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."""
		)

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











"""simple docstring"""







def _a			(		_snake_case = 6008_5147_5143							):







      """simple docstring"""






      try:
            UpperCAmelCase          =	int(_snake_case							)
      except (TypeError, ValueError):
            raise TypeError("""Parameter n must be int or castable to int."""							)
      if n <= 0:
            raise ValueError("""Parameter n must be greater than or equal to one."""							)
      UpperCAmelCase          =	1
      UpperCAmelCase          =	2
      while i * i <= n:
            while n % i == 0:
                  UpperCAmelCase          =	i
                  n //= i
            i += 1
      if n > 1:
            UpperCAmelCase          =	n
      return int(_snake_case							)


if __name__ == "__main__":
  print(F"""{solution() = }""")



