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(_A							,				_A					)

 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(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
  __a       =  {int(_A					): 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(_A					)

  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(_A					)
 return list(_A					)



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



 """simple docstring"""


 __a       =  get_videomae_config(_A					)

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

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

 model.load_state_dict(_A					)
 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(_A							,				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(_A					)

 __a       =  model(**_A					)
 __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]							,				_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":
  __a       =  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__":
     SCREAMING_SNAKE_CASE :	Optional[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."""
     )

     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)



import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset



@pytest.mark.parametrize("dataset_size"							,				[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize("input_in_memory_max_size"							,				["default", 0, 100 * 2**20, 900 * 2**20]					)
def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config							,				"IN_MEMORY_MAX_SIZE"							,				_A					)
 __a       =  datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  __a       =  dataset_size < in_memory_max_size
 else:
  __a       =  False
 __a       =  is_small_dataset(_A					)
 assert result == expected


