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		(			_A					):



 """simple docstring"""


 __a       =  VideoMAEConfig()

 set_architecture_configs(__lowercase							,				__lowercase					)

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

 return config



def   __A		(			_A							,				_A					):



 """simple docstring"""


 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   __A		(			_A					):



 """simple docstring"""


 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   __A		(			_A							,				_A					):



 """simple docstring"""


 for key in orig_state_dict.copy().keys():
  __a       =  orig_state_dict.pop(__lowercase					)

  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   __A		(			):



 """simple docstring"""


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



def   __A		(			_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  get_videomae_config(__lowercase					)

 if "finetuned" in model_name:
  __a       =  VideoMAEForVideoClassification(__lowercase					)
 else:
  __a       =  VideoMAEForPreTraining(__lowercase					)

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

 model.load_state_dict(__lowercase					)
 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(__lowercase							,				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(__lowercase					)

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

  SCREAMING_SNAKE_CASE :	str							     =		parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



from ... import PretrainedConfig


SCREAMING_SNAKE_CASE :	Any							     =		{
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  _SCREAMING_SNAKE_CASE    				=			"""nezha"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=2_11_28     ,							__SCREAMING_SNAKE_CASE :      Dict=7_68     ,							__SCREAMING_SNAKE_CASE :      List[Any]=12     ,							__SCREAMING_SNAKE_CASE :      str=12     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30_72     ,							__SCREAMING_SNAKE_CASE :      Any="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_12     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=64     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-12     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Any=0     ,							__SCREAMING_SNAKE_CASE :      Any=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  max_relative_position
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  classifier_dropout
   __a       =  use_cache


