
"""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     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    VideoMAEConfig()

   set_architecture_configs(__lowerCamelCase,		__lowerCamelCase  )

   if "finetuned" not in model_name:
      UpperCAmelCase_					:					List[str]			     =    False

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

   return config







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







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

   return name







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   for key in orig_state_dict.copy().keys():
      UpperCAmelCase_					:					List[Any]			     =    orig_state_dict.pop(__lowerCamelCase  )

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

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

   return orig_state_dict







def 	__a     (		):
   UpperCAmelCase_					:					Optional[int]			     =    hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video",		filename="eating_spaghetti.npy",		repo_type="dataset"  )
   UpperCAmelCase_					:					str			     =    np.load(__lowerCamelCase  )
   return list(__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Dict			     =    get_videomae_config(__lowerCamelCase  )

   if "finetuned" in model_name:
      UpperCAmelCase_					:					int			     =    VideoMAEForVideoClassification(__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					Any			     =    VideoMAEForPreTraining(__lowerCamelCase  )

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

   model.load_state_dict(__lowerCamelCase  )
   model.eval()

   # verify model on basic input
   UpperCAmelCase_					:					Optional[int]			     =    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],		image_std=[0.5, 0.5, 0.5]  )
   UpperCAmelCase_					:					Optional[int]			     =    prepare_video()
   UpperCAmelCase_					:					str			     =    image_processor(__lowerCamelCase,		return_tensors="pt"  )

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

   UpperCAmelCase_					:					str			     =    model(**__lowerCamelCase  )
   UpperCAmelCase_					:					Tuple			     =    outputs.logits

   UpperCAmelCase_					:					Any			     =    [
       "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_					:					Tuple			     =    torch.Size([1, 400]  )
      UpperCAmelCase_					:					Optional[Any]			     =    torch.tensor([-0.9291, -0.4061, -0.9307]  )
   elif model_name == "videomae-small-finetuned-ssv2":
      UpperCAmelCase_					:					Any			     =    torch.Size([1, 174]  )
      UpperCAmelCase_					:					List[str]			     =    torch.tensor([0.2671, -0.4689, -0.8235]  )
   elif model_name == "videomae-base":
      UpperCAmelCase_					:					Tuple			     =    torch.Size([1, 1408, 1536]  )
      UpperCAmelCase_					:					str			     =    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_					:					List[str]			     =    torch.Size([1, 1408, 1536]  )
      UpperCAmelCase_					:					Optional[Any]			     =    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_					:					Optional[Any]			     =    torch.tensor([0.5142]  ) if config.norm_pix_loss else torch.tensor([0.6469]  )
   elif model_name == "videomae-large":
      UpperCAmelCase_					:					Dict			     =    torch.Size([1, 1408, 1536]  )
      UpperCAmelCase_					:					List[str]			     =    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_					:					List[Any]			     =    torch.Size([1, 400]  )
      UpperCAmelCase_					:					List[Any]			     =    torch.tensor([0.0771, 0.0011, -0.3625]  )
   elif model_name == "videomae-huge-finetuned-kinetics":
      UpperCAmelCase_					:					Any			     =    torch.Size([1, 400]  )
      UpperCAmelCase_					:					Any			     =    torch.tensor([0.2433, 0.1632, -0.4894]  )
   elif model_name == "videomae-base-short-finetuned-kinetics":
      UpperCAmelCase_					:					int			     =    torch.Size([1, 400]  )
      UpperCAmelCase_					:					int			     =    torch.tensor([0.6588, 0.0990, -0.2493]  )
   elif model_name == "videomae-base-finetuned-kinetics":
      UpperCAmelCase_					:					int			     =    torch.Size([1, 400]  )
      UpperCAmelCase_					:					Any			     =    torch.tensor([0.3669, -0.0688, -0.2421]  )
   elif model_name == "videomae-base-short-ssv2":
      UpperCAmelCase_					:					str			     =    torch.Size([1, 1408, 1536]  )
      UpperCAmelCase_					:					Union[str, Any]			     =    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_					:					Union[str, Any]			     =    torch.Size([1, 174]  )
      UpperCAmelCase_					:					Union[str, Any]			     =    torch.tensor([-0.0537, -0.1539, -0.3266]  )
   elif model_name == "videomae-base-ssv2":
      UpperCAmelCase_					:					List[str]			     =    torch.Size([1, 1408, 1536]  )
      UpperCAmelCase_					:					Union[str, Any]			     =    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_					:					int			     =    torch.Size([1, 174]  )
      UpperCAmelCase_					:					Optional[int]			     =    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],		__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":
      UpperCAmelCase_					:					Any			     =    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__":
    _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)




"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime


@require_onnxruntime
class A_	:




  '''simple docstring'''
  pass


