



'''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 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Any   =       VideoMAEConfig()

			set_architecture_configs(__magic_name__     ,__magic_name__						)

			if "finetuned" not in model_name:
						snake_case_				:					Union[str, Any]   =       False

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

			return config

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    List[Any]:



			"""simple docstring"""



			if "small" in model_name:
						snake_case_				:					str   =       384
						snake_case_				:					str   =       1536
						snake_case_				:					str   =       12
						snake_case_				:					Optional[int]   =       16
						snake_case_				:					List[Any]   =       12
						snake_case_				:					Optional[int]   =       3
						snake_case_				:					Optional[int]   =       192
						snake_case_				:					Any   =       768
			elif "large" in model_name:
						snake_case_				:					Any   =       1024
						snake_case_				:					Union[str, Any]   =       4096
						snake_case_				:					Optional[int]   =       24
						snake_case_				:					str   =       16
						snake_case_				:					Tuple   =       12
						snake_case_				:					int   =       8
						snake_case_				:					Optional[int]   =       512
						snake_case_				:					Any   =       2048
			elif "huge" in model_name:
						snake_case_				:					Tuple   =       1280
						snake_case_				:					Dict   =       5120
						snake_case_				:					int   =       32
						snake_case_				:					Optional[Any]   =       16
						snake_case_				:					List[Any]   =       12
						snake_case_				:					Tuple   =       8
						snake_case_				:					Optional[int]   =       640
						snake_case_				:					List[str]   =       2560
			elif "base" not in model_name:
						raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""						)

def 	__UpperCAmelCase       ( __magic_name__						)->    Dict:



			"""simple docstring"""



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

			return name

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Any:



			"""simple docstring"""



			for key in orig_state_dict.copy().keys():
						snake_case_				:					Optional[Any]   =       orig_state_dict.pop(__magic_name__						)

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

						if "qkv" in key:
									snake_case_				:					List[Any]   =       key.split("."						)
									if key.startswith("decoder.blocks"						):
												snake_case_				:					Any   =       config.decoder_hidden_size
												snake_case_				:					Optional[int]   =       int(key_split[2]						)
												snake_case_				:					Dict   =       "decoder.decoder_layers."
												if "weight" in key:
															snake_case_				:					int   =       val[:dim, :]
															snake_case_				:					Union[str, Any]   =       val[dim : dim * 2, :]
															snake_case_				:					Dict   =       val[-dim:, :]
									else:
												snake_case_				:					Tuple   =       config.hidden_size
												snake_case_				:					List[str]   =       int(key_split[1]						)
												snake_case_				:					str   =       "videomae.encoder.layer."
												if "weight" in key:
															snake_case_				:					List[str]   =       val[:dim, :]
															snake_case_				:					List[Any]   =       val[dim : dim * 2, :]
															snake_case_				:					Any   =       val[-dim:, :]
						else:
									snake_case_				:					Any   =       val

			return orig_state_dict

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"     ,filename="eating_spaghetti.npy"     ,repo_type="dataset"						)
			snake_case_				:					List[Any]   =       np.load(__magic_name__						)
			return list(__magic_name__						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					Tuple   =       get_videomae_config(__magic_name__						)

			if "finetuned" in model_name:
						snake_case_				:					Optional[int]   =       VideoMAEForVideoClassification(__magic_name__						)
			else:
						snake_case_				:					int   =       VideoMAEForPreTraining(__magic_name__						)

			# download original checkpoint, hosted on Google Drive
			snake_case_				:					Any   =       "pytorch_model.bin"
			gdown.cached_download(__magic_name__     ,__magic_name__     ,quiet=__magic_name__						)
			snake_case_				:					List[Any]   =       torch.load(__magic_name__     ,map_location="cpu"						)
			if "model" in files:
						snake_case_				:					Union[str, Any]   =       files["model"]
			else:
						snake_case_				:					int   =       files["module"]
			snake_case_				:					Optional[Any]   =       convert_state_dict(__magic_name__     ,__magic_name__						)

			model.load_state_dict(__magic_name__						)
			model.eval()

			# verify model on basic input
			snake_case_				:					Union[str, Any]   =       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,image_std=[0.5, 0.5, 0.5]						)
			snake_case_				:					List[str]   =       prepare_video()
			snake_case_				:					Union[str, Any]   =       image_processor(__magic_name__     ,return_tensors="pt"						)

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

			snake_case_				:					Any   =       model(**__magic_name__						)
			snake_case_				:					Tuple   =       outputs.logits

			snake_case_				:					Tuple   =       [
			    "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":
						snake_case_				:					Any   =       torch.Size([1, 400]						)
						snake_case_				:					List[Any]   =       torch.tensor([-0.9_291, -0.4_061, -0.9_307]						)
			elif model_name == "videomae-small-finetuned-ssv2":
						snake_case_				:					List[Any]   =       torch.Size([1, 174]						)
						snake_case_				:					Union[str, Any]   =       torch.tensor([0.2_671, -0.4_689, -0.8_235]						)
			elif model_name == "videomae-base":
						snake_case_				:					Dict   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					Dict   =       torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]						)
			elif model_name == "videomae-base-short":
						snake_case_				:					Union[str, Any]   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					Optional[int]   =       torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]						)
						# we verified the loss both for normalized and unnormalized targets for this one
						snake_case_				:					List[str]   =       torch.tensor([0.5_142]						) if config.norm_pix_loss else torch.tensor([0.6_469]						)
			elif model_name == "videomae-large":
						snake_case_				:					Any   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					List[str]   =       torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]						)
			elif model_name == "videomae-large-finetuned-kinetics":
						snake_case_				:					List[Any]   =       torch.Size([1, 400]						)
						snake_case_				:					Optional[int]   =       torch.tensor([0.0_771, 0.0_011, -0.3_625]						)
			elif model_name == "videomae-huge-finetuned-kinetics":
						snake_case_				:					List[str]   =       torch.Size([1, 400]						)
						snake_case_				:					Union[str, Any]   =       torch.tensor([0.2_433, 0.1_632, -0.4_894]						)
			elif model_name == "videomae-base-short-finetuned-kinetics":
						snake_case_				:					List[Any]   =       torch.Size([1, 400]						)
						snake_case_				:					int   =       torch.tensor([0.6_588, 0.0_990, -0.2_493]						)
			elif model_name == "videomae-base-finetuned-kinetics":
						snake_case_				:					Dict   =       torch.Size([1, 400]						)
						snake_case_				:					str   =       torch.tensor([0.3_669, -0.0_688, -0.2_421]						)
			elif model_name == "videomae-base-short-ssv2":
						snake_case_				:					Optional[Any]   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					Optional[int]   =       torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]						)
			elif model_name == "videomae-base-short-finetuned-ssv2":
						snake_case_				:					Dict   =       torch.Size([1, 174]						)
						snake_case_				:					str   =       torch.tensor([-0.0_537, -0.1_539, -0.3_266]						)
			elif model_name == "videomae-base-ssv2":
						snake_case_				:					Union[str, Any]   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					List[str]   =       torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]						)
			elif model_name == "videomae-base-finetuned-ssv2":
						snake_case_				:					int   =       torch.Size([1, 174]						)
						snake_case_				:					Any   =       torch.tensor([0.1_961, -0.8_337, -0.6_389]						)
			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]     ,__magic_name__     ,atol=1E-4						)
			else:
						print("Logits:"     ,logits[0, :3, :3]						)
						assert torch.allclose(logits[0, :3, :3]     ,__magic_name__     ,atol=1E-4						)
			print("Logits ok!"						)

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

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


if __name__ == "__main__":
		__lowerCamelCase   :					List[Any]   							=					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.'''
		)

		__lowerCamelCase   :					Any   							=					parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)












'''simple docstring'''




def 	__UpperCAmelCase       ( __magic_name__						)->    list[int]:



			"""simple docstring"""



			if length <= 0 or not isinstance(__magic_name__     ,__magic_name__						):
						raise ValueError("Length must be a positive integer."						)
			return [n * (2 * n - 1) for n in range(__magic_name__						)]


if __name__ == "__main__":
		print(hexagonal_numbers(length=5))
		print(hexagonal_numbers(length=10))




