


"""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      _snake_case						(						_snake_case				:			List[str]			):
      lowerCAmelCase					:	Any       =   VideoMAEConfig()

      set_architecture_configs(_snake_case ,   _snake_case			)

      if "finetuned" not in model_name:
            lowerCAmelCase					:	Optional[int]       =   False

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

      return config


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


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

      return name


def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			Tuple			):
      for key in orig_state_dict.copy().keys():
            lowerCAmelCase					:	Tuple       =   orig_state_dict.pop(_snake_case			)

            if key.startswith('''encoder.'''			):
                  lowerCAmelCase					:	Tuple       =   key.replace('''encoder.''' ,   ''''''			)

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

      return orig_state_dict


def      _snake_case						(						):
      lowerCAmelCase					:	List[Any]       =   hf_hub_download(
          repo_id='''hf-internal-testing/spaghetti-video''' ,   filename='''eating_spaghetti.npy''' ,   repo_type='''dataset'''			)
      lowerCAmelCase					:	List[str]       =   np.load(_snake_case			)
      return list(_snake_case			)


def      _snake_case						(						_snake_case				:			Union[str, Any] ,   _snake_case				:			Optional[int] ,   _snake_case				:			Tuple ,   _snake_case				:			Dict			):
      lowerCAmelCase					:	str       =   get_videomae_config(_snake_case			)

      if "finetuned" in model_name:
            lowerCAmelCase					:	Dict       =   VideoMAEForVideoClassification(_snake_case			)
      else:
            lowerCAmelCase					:	List[str]       =   VideoMAEForPreTraining(_snake_case			)

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

      model.load_state_dict(_snake_case			)
      model.eval()

      # verify model on basic input
      lowerCAmelCase					:	Optional[Any]       =   VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,   image_std=[0.5, 0.5, 0.5]			)
      lowerCAmelCase					:	Optional[Any]       =   prepare_video()
      lowerCAmelCase					:	Dict       =   image_processor(_snake_case ,   return_tensors='''pt'''			)

      if "finetuned" not in model_name:
            lowerCAmelCase					:	Tuple       =   hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' ,   filename='''bool_masked_pos.pt'''			)
            lowerCAmelCase					:	str       =   torch.load(_snake_case			)

      lowerCAmelCase					:	int       =   model(**_snake_case			)
      lowerCAmelCase					:	Optional[Any]       =   outputs.logits

      lowerCAmelCase					:	List[str]       =   [
          '''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":
            lowerCAmelCase					:	Optional[int]       =   torch.Size([1, 400]			)
            lowerCAmelCase					:	Dict       =   torch.tensor([-0.9291, -0.4061, -0.9307]			)
      elif model_name == "videomae-small-finetuned-ssv2":
            lowerCAmelCase					:	Optional[Any]       =   torch.Size([1, 174]			)
            lowerCAmelCase					:	Optional[int]       =   torch.tensor([0.2671, -0.4689, -0.8235]			)
      elif model_name == "videomae-base":
            lowerCAmelCase					:	Optional[int]       =   torch.Size([1, 1408, 1536]			)
            lowerCAmelCase					:	Dict       =   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":
            lowerCAmelCase					:	Optional[Any]       =   torch.Size([1, 1408, 1536]			)
            lowerCAmelCase					:	Dict       =   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
            lowerCAmelCase					:	Optional[int]       =   torch.tensor([0.5142]			) if config.norm_pix_loss else torch.tensor([0.6469]			)
      elif model_name == "videomae-large":
            lowerCAmelCase					:	Any       =   torch.Size([1, 1408, 1536]			)
            lowerCAmelCase					:	Tuple       =   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":
            lowerCAmelCase					:	Dict       =   torch.Size([1, 400]			)
            lowerCAmelCase					:	List[str]       =   torch.tensor([0.0771, 0.0011, -0.3625]			)
      elif model_name == "videomae-huge-finetuned-kinetics":
            lowerCAmelCase					:	Dict       =   torch.Size([1, 400]			)
            lowerCAmelCase					:	List[Any]       =   torch.tensor([0.2433, 0.1632, -0.4894]			)
      elif model_name == "videomae-base-short-finetuned-kinetics":
            lowerCAmelCase					:	str       =   torch.Size([1, 400]			)
            lowerCAmelCase					:	Any       =   torch.tensor([0.6588, 0.0990, -0.2493]			)
      elif model_name == "videomae-base-finetuned-kinetics":
            lowerCAmelCase					:	Dict       =   torch.Size([1, 400]			)
            lowerCAmelCase					:	Optional[int]       =   torch.tensor([0.3669, -0.0688, -0.2421]			)
      elif model_name == "videomae-base-short-ssv2":
            lowerCAmelCase					:	int       =   torch.Size([1, 1408, 1536]			)
            lowerCAmelCase					:	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":
            lowerCAmelCase					:	Union[str, Any]       =   torch.Size([1, 174]			)
            lowerCAmelCase					:	Union[str, Any]       =   torch.tensor([-0.0537, -0.1539, -0.3266]			)
      elif model_name == "videomae-base-ssv2":
            lowerCAmelCase					:	Optional[int]       =   torch.Size([1, 1408, 1536]			)
            lowerCAmelCase					:	str       =   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":
            lowerCAmelCase					:	int       =   torch.Size([1, 174]			)
            lowerCAmelCase					:	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] ,   _snake_case ,   atol=1E-4			)
      else:
            print('''Logits:''' ,   logits[0, :3, :3]			)
            assert torch.allclose(logits[0, :3, :3] ,   _snake_case ,   atol=1E-4			)
      print('''Logits ok!'''			)

      # verify loss, if applicable
      if model_name == "videomae-base-short":
            lowerCAmelCase					:	Union[str, Any]       =   outputs.loss
            assert torch.allclose(_snake_case ,   _snake_case ,   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(_snake_case			)
            model.save_pretrained(_snake_case			)

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


if __name__ == "__main__":
 snake_case__		:     List[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.'''
 )

 snake_case__		:     Dict    				=				parser.parse_args()
 convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)






"""simple docstring"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


snake_case__		:     Optional[Any]    				=				namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			List[str]			):
      return (abs(source - target			) / target) < 0.01






@pytest.mark.integration
def      _snake_case						(						_snake_case				:			Any			):
      lowerCAmelCase					:	Union[str, Any]       =   _TestCommandArgs(dataset=_snake_case ,   all_configs=_snake_case ,   save_infos=_snake_case			)
      lowerCAmelCase					:	str       =   TestCommand(*_snake_case			)
      test_command.run()
      lowerCAmelCase					:	str       =   os.path.join(_snake_case ,   '''README.md'''			)
      assert os.path.exists(_snake_case			)
      lowerCAmelCase					:	Tuple       =   DatasetInfosDict.from_directory(_snake_case			)
      lowerCAmelCase					:	List[str]       =   DatasetInfosDict(
          {
              '''default''': DatasetInfo(
                  features=Features(
                      {
                          '''tokens''': Sequence(Value('''string'''			)			),
                          '''ner_tags''': Sequence(
                              ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']			)			),
                          '''langs''': Sequence(Value('''string'''			)			),
                          '''spans''': Sequence(Value('''string'''			)			),
                      }			) ,   splits=[
                      {
                          '''name''': '''train''',
                          '''num_bytes''': 2351563,
                          '''num_examples''': 10000,
                      },
                      {
                          '''name''': '''validation''',
                          '''num_bytes''': 238418,
                          '''num_examples''': 1000,
                      },
                  ] ,   download_size=3940680 ,   dataset_size=2589981 ,   )
          }			)
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   getattr(dataset_infos['''default'''] ,   _snake_case			), getattr(expected_dataset_infos['''default'''] ,   _snake_case			)
            if key == "num_bytes":
                  assert is_apercent_close(_snake_case ,   _snake_case			)
            elif key == "splits":
                  assert list(_snake_case			) == list(_snake_case			)
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes ,   expected[split].num_bytes			)
            else:
                  result == expected


