




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							:      str		):




 """simple docstring"""







 UpperCAmelCase__							       =  VideoMAEConfig()

 set_architecture_configs(A_  ,							A_		)

 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(A_  ,							A_  ,							repo_type="dataset"		)  ,							"r"		)		)
  UpperCAmelCase__							       =  {int(A_		): v for k, v in idalabel.items()}
  UpperCAmelCase__							       =  idalabel
  UpperCAmelCase__							       =  {v: k for k, v in idalabel.items()}

 return config



def   lowerCAmelCase						(						_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      Dict		):




 """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   lowerCAmelCase						(						_lowerCAmelCase							:      Union[str, Any]		):




 """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   lowerCAmelCase						(						_lowerCAmelCase							:      Dict  ,							_lowerCAmelCase							:      Union[str, Any]		):




 """simple docstring"""







 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__							       =  orig_state_dict.pop(A_		)

  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   lowerCAmelCase						(						):




 """simple docstring"""







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



def   lowerCAmelCase						(						_lowerCAmelCase							:      Optional[int]  ,							_lowerCAmelCase							:      List[Any]  ,							_lowerCAmelCase							:      str  ,							_lowerCAmelCase							:      List[Any]		):




 """simple docstring"""







 UpperCAmelCase__							       =  get_videomae_config(A_		)

 if "finetuned" in model_name:
  UpperCAmelCase__							       =  VideoMAEForVideoClassification(A_		)
 else:
  UpperCAmelCase__							       =  VideoMAEForPreTraining(A_		)

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

 model.load_state_dict(A_		)
 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(A_  ,							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(A_		)

 UpperCAmelCase__							       =  model(**A_		)
 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([-0.9_291, -0.4_061, -0.9_307]		)
 elif model_name == "videomae-small-finetuned-ssv2":
  UpperCAmelCase__							       =  torch.Size([1, 174]		)
  UpperCAmelCase__							       =  torch.tensor([0.2_671, -0.4_689, -0.8_235]		)
 elif model_name == "videomae-base":
  UpperCAmelCase__							       =  torch.Size([1, 1408, 1536]		)
  UpperCAmelCase__							       =  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":
  UpperCAmelCase__							       =  torch.Size([1, 1408, 1536]		)
  UpperCAmelCase__							       =  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
  UpperCAmelCase__							       =  torch.tensor([0.5_142]		) if config.norm_pix_loss else torch.tensor([0.6_469]		)
 elif model_name == "videomae-large":
  UpperCAmelCase__							       =  torch.Size([1, 1408, 1536]		)
  UpperCAmelCase__							       =  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":
  UpperCAmelCase__							       =  torch.Size([1, 400]		)
  UpperCAmelCase__							       =  torch.tensor([0.0_771, 0.0_011, -0.3_625]		)
 elif model_name == "videomae-huge-finetuned-kinetics":
  UpperCAmelCase__							       =  torch.Size([1, 400]		)
  UpperCAmelCase__							       =  torch.tensor([0.2_433, 0.1_632, -0.4_894]		)
 elif model_name == "videomae-base-short-finetuned-kinetics":
  UpperCAmelCase__							       =  torch.Size([1, 400]		)
  UpperCAmelCase__							       =  torch.tensor([0.6_588, 0.0_990, -0.2_493]		)
 elif model_name == "videomae-base-finetuned-kinetics":
  UpperCAmelCase__							       =  torch.Size([1, 400]		)
  UpperCAmelCase__							       =  torch.tensor([0.3_669, -0.0_688, -0.2_421]		)
 elif model_name == "videomae-base-short-ssv2":
  UpperCAmelCase__							       =  torch.Size([1, 1408, 1536]		)
  UpperCAmelCase__							       =  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":
  UpperCAmelCase__							       =  torch.Size([1, 174]		)
  UpperCAmelCase__							       =  torch.tensor([-0.0_537, -0.1_539, -0.3_266]		)
 elif model_name == "videomae-base-ssv2":
  UpperCAmelCase__							       =  torch.Size([1, 1408, 1536]		)
  UpperCAmelCase__							       =  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":
  UpperCAmelCase__							       =  torch.Size([1, 174]		)
  UpperCAmelCase__							       =  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]  ,							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":
  UpperCAmelCase__							       =  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__":
  _lowerCAmelCase  :      str						=      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  :      Union[str, Any]						=      parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu




@require_cpu
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       torch.nn.Linear(10			,							10    )
    __magic_name__				     =       torch.optim.SGD(model.parameters()			,							0.1    )
    __magic_name__				     =       Accelerator()
    __magic_name__				     =       accelerator.prepare(UpperCamelCase__    )
    try:
      pickle.loads(pickle.dumps(UpperCamelCase__    )    )
    except Exception as e:
      self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
    AcceleratorState._reset_state()

