






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	(						__A    :							Any )      ->					Tuple:

    '''simple docstring'''





    _UpperCAmelCase				:      Tuple			  =				VideoMAEConfig()

    set_architecture_configs(__A		,		__A )

    if "finetuned" not in model_name:
        _UpperCAmelCase				:      Union[str, Any]			  =				False

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

    return config

def      _lowerCamelCase	(						__A    :							Any		,		__A    :							List[Any] )      ->					int:

    '''simple docstring'''





    if "small" in model_name:
        _UpperCAmelCase				:      Dict			  =				384
        _UpperCAmelCase				:      Union[str, Any]			  =				1_536
        _UpperCAmelCase				:      str			  =				12
        _UpperCAmelCase				:      List[Any]			  =				16
        _UpperCAmelCase				:      List[Any]			  =				12
        _UpperCAmelCase				:      Dict			  =				3
        _UpperCAmelCase				:      Dict			  =				192
        _UpperCAmelCase				:      List[str]			  =				768
    elif "large" in model_name:
        _UpperCAmelCase				:      Tuple			  =				1_024
        _UpperCAmelCase				:      int			  =				4_096
        _UpperCAmelCase				:      Tuple			  =				24
        _UpperCAmelCase				:      str			  =				16
        _UpperCAmelCase				:      Tuple			  =				12
        _UpperCAmelCase				:      List[Any]			  =				8
        _UpperCAmelCase				:      List[str]			  =				512
        _UpperCAmelCase				:      List[Any]			  =				2_048
    elif "huge" in model_name:
        _UpperCAmelCase				:      List[Any]			  =				1_280
        _UpperCAmelCase				:      Optional[int]			  =				5_120
        _UpperCAmelCase				:      str			  =				32
        _UpperCAmelCase				:      int			  =				16
        _UpperCAmelCase				:      int			  =				12
        _UpperCAmelCase				:      Any			  =				8
        _UpperCAmelCase				:      Optional[Any]			  =				640
        _UpperCAmelCase				:      Optional[int]			  =				2_560
    elif "base" not in model_name:
        raise ValueError('''Model name should include either "small", "base", "large", or "huge"''' )

def      _lowerCamelCase	(						__A    :							Optional[Any] )      ->					Dict:

    '''simple docstring'''





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

    return name

def      _lowerCamelCase	(						__A    :							str		,		__A    :							Optional[Any] )      ->					str:

    '''simple docstring'''





    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase				:      Dict			  =				orig_state_dict.pop(__A )

        if key.startswith('''encoder.''' ):
            _UpperCAmelCase				:      int			  =				key.replace('''encoder.'''		,		'''''' )

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

    return orig_state_dict

def      _lowerCamelCase	(						)      ->					Dict:

    '''simple docstring'''





    _UpperCAmelCase				:      Tuple			  =				hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video'''		,		filename='''eating_spaghetti.npy'''		,		repo_type='''dataset''' )
    _UpperCAmelCase				:      Union[str, Any]			  =				np.load(__A )
    return list(__A )

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							Optional[int]		,		__A    :							Any		,		__A    :							Optional[int] )      ->					Optional[int]:

    '''simple docstring'''





    _UpperCAmelCase				:      Optional[int]			  =				get_videomae_config(__A )

    if "finetuned" in model_name:
        _UpperCAmelCase				:      Dict			  =				VideoMAEForVideoClassification(__A )
    else:
        _UpperCAmelCase				:      Union[str, Any]			  =				VideoMAEForPreTraining(__A )

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

    model.load_state_dict(__A )
    model.eval()

    # verify model on basic input
    _UpperCAmelCase				:      Union[str, Any]			  =				VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]		,		image_std=[0.5, 0.5, 0.5] )
    _UpperCAmelCase				:      List[str]			  =				prepare_video()
    _UpperCAmelCase				:      Tuple			  =				image_processor(__A		,		return_tensors='''pt''' )

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

    _UpperCAmelCase				:      int			  =				model(**__A )
    _UpperCAmelCase				:      List[Any]			  =				outputs.logits

    _UpperCAmelCase				:      Optional[int]			  =				[
        '''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				:      List[Any]			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      List[Any]			  =				torch.tensor([-0.9291, -0.4061, -0.9307] )
    elif model_name == "videomae-small-finetuned-ssv2":
        _UpperCAmelCase				:      int			  =				torch.Size([1, 174] )
        _UpperCAmelCase				:      List[str]			  =				torch.tensor([0.2671, -0.4689, -0.8235] )
    elif model_name == "videomae-base":
        _UpperCAmelCase				:      Union[str, Any]			  =				torch.Size([1, 1_408, 1_536] )
        _UpperCAmelCase				:      Tuple			  =				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				:      str			  =				torch.Size([1, 1_408, 1_536] )
        _UpperCAmelCase				:      Union[str, 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[int]			  =				torch.tensor([0.5142] ) if config.norm_pix_loss else torch.tensor([0.6469] )
    elif model_name == "videomae-large":
        _UpperCAmelCase				:      Dict			  =				torch.Size([1, 1_408, 1_536] )
        _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				:      Tuple			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      str			  =				torch.tensor([0.0771, 0.0011, -0.3625] )
    elif model_name == "videomae-huge-finetuned-kinetics":
        _UpperCAmelCase				:      Optional[Any]			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      List[str]			  =				torch.tensor([0.2433, 0.1632, -0.4894] )
    elif model_name == "videomae-base-short-finetuned-kinetics":
        _UpperCAmelCase				:      List[Any]			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      int			  =				torch.tensor([0.6588, 0.0990, -0.2493] )
    elif model_name == "videomae-base-finetuned-kinetics":
        _UpperCAmelCase				:      List[str]			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      Union[str, Any]			  =				torch.tensor([0.3669, -0.0688, -0.2421] )
    elif model_name == "videomae-base-short-ssv2":
        _UpperCAmelCase				:      Optional[Any]			  =				torch.Size([1, 1_408, 1_536] )
        _UpperCAmelCase				:      str			  =				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				:      List[Any]			  =				torch.Size([1, 174] )
        _UpperCAmelCase				:      Any			  =				torch.tensor([-0.0537, -0.1539, -0.3266] )
    elif model_name == "videomae-base-ssv2":
        _UpperCAmelCase				:      Optional[int]			  =				torch.Size([1, 1_408, 1_536] )
        _UpperCAmelCase				:      Optional[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				:      Tuple			  =				torch.Size([1, 174] )
        _UpperCAmelCase				:      str			  =				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":
        _UpperCAmelCase				:      List[str]			  =				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 							=						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 							=						parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)












import argparse
import os
import re


SCREAMING_SNAKE_CASE 							=						'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE 							=						re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE 							=						re.compile(R'\s*\(\s*"(\S[^"]+)"')

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							bool = False )      ->					int:
    with open(__A		,		'''r'''		,		encoding='''utf-8''' ) as f:
        _UpperCAmelCase				:      Union[str, Any]			  =				f.read()

    _UpperCAmelCase				:      Any			  =				content.split('''\n''' )
    _UpperCAmelCase				:      Any			  =				[]
    _UpperCAmelCase				:      Tuple			  =				0
    while line_idx < len(__A ):
        if _re_intro_mapping.search(lines[line_idx] ) is not None:
            _UpperCAmelCase				:      Union[str, Any]			  =				len(re.search(r'''^(\s*)\S'''		,		lines[line_idx] ).groups()[0] ) + 8
            # Start of a new mapping!
            while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
                new_lines.append(lines[line_idx] )
                line_idx += 1

            _UpperCAmelCase				:      str			  =				[]
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    _UpperCAmelCase				:      List[str]			  =				line_idx
                    while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
                        line_idx += 1
                    blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
                else:
                    blocks.append(lines[line_idx] )
                line_idx += 1

            # Sort blocks by their identifiers
            _UpperCAmelCase				:      Tuple			  =				sorted(__A		,		key=lambda __A : _re_identifier.search(__A ).groups()[0] )
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx] )
            line_idx += 1

    if overwrite:
        with open(__A		,		'''w'''		,		encoding='''utf-8''' ) as f:
            f.write('''\n'''.join(__A ) )
    elif "\n".join(__A ) != content:
        return True

def      _lowerCamelCase	(						__A    :							bool = False )      ->					List[str]:
    _UpperCAmelCase				:      List[str]			  =				[os.path.join(__A		,		__A ) for f in os.listdir(__A ) if f.endswith('''.py''' )]
    _UpperCAmelCase				:      List[Any]			  =				[sort_auto_mapping(__A		,		overwrite=__A ) for fname in fnames]

    if not overwrite and any(__A ):
        _UpperCAmelCase				:      Optional[int]			  =				[f for f, d in zip(__A		,		__A ) if d]
        raise ValueError(
            f'''The following files have auto mappings that need sorting: {', '.join(__A )}. Run `make style` to fix'''
            ''' this.''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()

  sort_all_auto_mappings(not args.check_only)




