





'''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    _lowerCamelCase  (__lowerCamelCase  :     Dict     )   ->    Any:
			a__     = VideoMAEConfig()

			set_architecture_configs(__lowerCamelCase					,  __lowerCamelCase     )

			if "finetuned" not in model_name:
						a__     = False

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

			return config







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







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]     )   ->    Tuple:
			if "encoder." in name:
						a__     = name.replace("encoder."					,  ""     )
			if "cls_token" in name:
						a__     = name.replace("cls_token"					,  "videomae.embeddings.cls_token"     )
			if "decoder_pos_embed" in name:
						a__     = name.replace("decoder_pos_embed"					,  "decoder.decoder_pos_embed"     )
			if "pos_embed" in name and "decoder" not in name:
						a__     = name.replace("pos_embed"					,  "videomae.embeddings.position_embeddings"     )
			if "patch_embed.proj" in name:
						a__     = name.replace("patch_embed.proj"					,  "videomae.embeddings.patch_embeddings.projection"     )
			if "patch_embed.norm" in name:
						a__     = name.replace("patch_embed.norm"					,  "videomae.embeddings.norm"     )
			if "decoder.blocks" in name:
						a__     = name.replace("decoder.blocks"					,  "decoder.decoder_layers"     )
			if "blocks" in name:
						a__     = name.replace("blocks"					,  "videomae.encoder.layer"     )
			if "attn.proj" in name:
						a__     = name.replace("attn.proj"					,  "attention.output.dense"     )
			if "attn" in name and "bias" not in name:
						a__     = name.replace("attn"					,  "attention.self"     )
			if "attn" in name:
						a__     = name.replace("attn"					,  "attention.attention"     )
			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 "decoder_embed" in name:
						a__     = name.replace("decoder_embed"					,  "decoder.decoder_embed"     )
			if "decoder_norm" in name:
						a__     = name.replace("decoder_norm"					,  "decoder.decoder_norm"     )
			if "decoder_pred" in name:
						a__     = name.replace("decoder_pred"					,  "decoder.decoder_pred"     )
			if "norm.weight" in name and "decoder" not in name and "fc" not in name:
						a__     = name.replace("norm.weight"					,  "videomae.layernorm.weight"     )
			if "norm.bias" in name and "decoder" not in name and "fc" not in name:
						a__     = name.replace("norm.bias"					,  "videomae.layernorm.bias"     )
			if "head" in name and "decoder" not in name:
						a__     = name.replace("head"					,  "classifier"     )

			return name







def    _lowerCamelCase  (__lowerCamelCase  :     str					,  __lowerCamelCase  :     Union[str, Any]     )   ->    Optional[int]:
			for key in orig_state_dict.copy().keys():
						a__     = orig_state_dict.pop(__lowerCamelCase     )

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

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

			return orig_state_dict







def    _lowerCamelCase  ()   ->    List[str]:
			a__     = hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"					,  filename="eating_spaghetti.npy"					,  repo_type="dataset"     )
			a__     = np.load(__lowerCamelCase     )
			return list(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[int]     )   ->    Union[str, Any]:
			a__     = get_videomae_config(__lowerCamelCase     )

			if "finetuned" in model_name:
						a__     = VideoMAEForVideoClassification(__lowerCamelCase     )
			else:
						a__     = VideoMAEForPreTraining(__lowerCamelCase     )

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

			model.load_state_dict(__lowerCamelCase     )
			model.eval()

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

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

			a__     = model(**__lowerCamelCase     )
			a__     = outputs.logits

			a__     = [
			    "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":
						a__     = torch.Size([1, 400]     )
						a__     = torch.tensor([-0.9_291, -0.4_061, -0.9_307]     )
			elif model_name == "videomae-small-finetuned-ssv2":
						a__     = torch.Size([1, 174]     )
						a__     = torch.tensor([0.2_671, -0.4_689, -0.8_235]     )
			elif model_name == "videomae-base":
						a__     = torch.Size([1, 1408, 1536]     )
						a__     = 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":
						a__     = torch.Size([1, 1408, 1536]     )
						a__     = 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
						a__     = torch.tensor([0.5_142]     ) if config.norm_pix_loss else torch.tensor([0.6_469]     )
			elif model_name == "videomae-large":
						a__     = torch.Size([1, 1408, 1536]     )
						a__     = 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":
						a__     = torch.Size([1, 400]     )
						a__     = torch.tensor([0.0_771, 0.0_011, -0.3_625]     )
			elif model_name == "videomae-huge-finetuned-kinetics":
						a__     = torch.Size([1, 400]     )
						a__     = torch.tensor([0.2_433, 0.1_632, -0.4_894]     )
			elif model_name == "videomae-base-short-finetuned-kinetics":
						a__     = torch.Size([1, 400]     )
						a__     = torch.tensor([0.6_588, 0.0_990, -0.2_493]     )
			elif model_name == "videomae-base-finetuned-kinetics":
						a__     = torch.Size([1, 400]     )
						a__     = torch.tensor([0.3_669, -0.0_688, -0.2_421]     )
			elif model_name == "videomae-base-short-ssv2":
						a__     = torch.Size([1, 1408, 1536]     )
						a__     = 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":
						a__     = torch.Size([1, 174]     )
						a__     = torch.tensor([-0.0_537, -0.1_539, -0.3_266]     )
			elif model_name == "videomae-base-ssv2":
						a__     = torch.Size([1, 1408, 1536]     )
						a__     = 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":
						a__     = torch.Size([1, 174]     )
						a__     = 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]					,  __lowerCamelCase					,  atol=1e-4     )
			else:
						print("Logits:"					,  logits[0, :3, :3]     )
						assert torch.allclose(logits[0, :3, :3]					,  __lowerCamelCase					,  atol=1e-4     )
			print("Logits ok!"     )

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

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


if __name__ == "__main__":
		lowerCAmelCase_   :   int       =					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_   :   Optional[int]       =					parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)










'''simple docstring'''


import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script






@require_cpu
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				debug_launcher(test_script.main		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				debug_launcher(test_ops.main		)



