
"""simple docstring"""




from __future__ import annotations




def  _lowerCAmelCase		(		_UpperCamelCase						):






		"""simple docstring"""



		_lowercase:			Dict       =     0.00
		_lowercase:			Tuple       =     0
		for resistor in resistors:
				if resistor <= 0:
						_lowercase:			Dict       =     f'''Resistor at index {index} has a negative or zero value!'''
						raise ValueError(snake_case_						)
				first_sum += 1 / float(snake_case_						)
				index += 1
		return 1 / first_sum




def  _lowerCAmelCase		(		_UpperCamelCase						):






		"""simple docstring"""



		_lowercase:			int       =     0.00
		_lowercase:			Dict       =     0
		for resistor in resistors:
				sum_r += resistor
				if resistor < 0:
						_lowercase:			Dict       =     f'''Resistor at index {index} has a negative value!'''
						raise ValueError(snake_case_						)
				index += 1
		return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()


"""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  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[str]       =     VideoMAEConfig()

  set_architecture_configs(_UpperCamelCase ,				_UpperCamelCase						)

  if "finetuned" not in model_name:
    _lowercase:			Optional[Any]       =     False

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

  return config




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



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

  return name




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  for key in orig_state_dict.copy().keys():
    _lowercase:			Any       =     orig_state_dict.pop(_UpperCamelCase						)

    if key.startswith('''encoder.'''						):
      _lowercase:			Any       =     key.replace('''encoder.''' ,				''''''						)

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

  return orig_state_dict




def  _lowerCAmelCase		(		):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			int       =     get_videomae_config(_UpperCamelCase						)

  if "finetuned" in model_name:
    _lowercase:			Optional[int]       =     VideoMAEForVideoClassification(_UpperCamelCase						)
  else:
    _lowercase:			Union[str, Any]       =     VideoMAEForPreTraining(_UpperCamelCase						)

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

  model.load_state_dict(_UpperCamelCase						)
  model.eval()

  # verify model on basic input
  _lowercase:			Tuple       =     VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,				image_std=[0.5, 0.5, 0.5]						)
  _lowercase:			Optional[Any]       =     prepare_video()
  _lowercase:			int       =     image_processor(_UpperCamelCase ,				return_tensors='''pt'''						)

  if "finetuned" not in model_name:
    _lowercase:			Optional[int]       =     hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' ,				filename='''bool_masked_pos.pt'''						)
    _lowercase:			Union[str, Any]       =     torch.load(_UpperCamelCase						)

  _lowercase:			int       =     model(**_UpperCamelCase						)
  _lowercase:			List[Any]       =     outputs.logits

  _lowercase:			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":
    _lowercase:			Union[str, Any]       =     torch.Size([1, 400]						)
    _lowercase:			Any       =     torch.tensor([-0.9_291, -0.4_061, -0.9_307]						)
  elif model_name == "videomae-small-finetuned-ssv2":
    _lowercase:			Union[str, Any]       =     torch.Size([1, 174]						)
    _lowercase:			Union[str, Any]       =     torch.tensor([0.2_671, -0.4_689, -0.8_235]						)
  elif model_name == "videomae-base":
    _lowercase:			str       =     torch.Size([1, 1_408, 1_536]						)
    _lowercase:			List[str]       =     torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]						)
  elif model_name == "videomae-base-short":
    _lowercase:			Tuple       =     torch.Size([1, 1_408, 1_536]						)
    _lowercase:			Optional[int]       =     torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]						)
    # we verified the loss both for normalized and unnormalized targets for this one
    _lowercase:			str       =     torch.tensor([0.5_142]						) if config.norm_pix_loss else torch.tensor([0.6_469]						)
  elif model_name == "videomae-large":
    _lowercase:			Tuple       =     torch.Size([1, 1_408, 1_536]						)
    _lowercase:			List[str]       =     torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]						)
  elif model_name == "videomae-large-finetuned-kinetics":
    _lowercase:			Dict       =     torch.Size([1, 400]						)
    _lowercase:			Dict       =     torch.tensor([0.0_771, 0.0_011, -0.3_625]						)
  elif model_name == "videomae-huge-finetuned-kinetics":
    _lowercase:			Union[str, Any]       =     torch.Size([1, 400]						)
    _lowercase:			str       =     torch.tensor([0.2_433, 0.1_632, -0.4_894]						)
  elif model_name == "videomae-base-short-finetuned-kinetics":
    _lowercase:			List[str]       =     torch.Size([1, 400]						)
    _lowercase:			Union[str, Any]       =     torch.tensor([0.6_588, 0.0_990, -0.2_493]						)
  elif model_name == "videomae-base-finetuned-kinetics":
    _lowercase:			Tuple       =     torch.Size([1, 400]						)
    _lowercase:			Any       =     torch.tensor([0.3_669, -0.0_688, -0.2_421]						)
  elif model_name == "videomae-base-short-ssv2":
    _lowercase:			int       =     torch.Size([1, 1_408, 1_536]						)
    _lowercase:			str       =     torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]						)
  elif model_name == "videomae-base-short-finetuned-ssv2":
    _lowercase:			Dict       =     torch.Size([1, 174]						)
    _lowercase:			Optional[Any]       =     torch.tensor([-0.0_537, -0.1_539, -0.3_266]						)
  elif model_name == "videomae-base-ssv2":
    _lowercase:			str       =     torch.Size([1, 1_408, 1_536]						)
    _lowercase:			str       =     torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]						)
  elif model_name == "videomae-base-finetuned-ssv2":
    _lowercase:			List[Any]       =     torch.Size([1, 174]						)
    _lowercase:			Tuple       =     torch.tensor([0.1_961, -0.8_337, -0.6_389]						)
  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] ,				_UpperCamelCase ,				atol=1e-4						)
  else:
    print('''Logits:''' ,				logits[0, :3, :3]						)
    assert torch.allclose(logits[0, :3, :3] ,				_UpperCamelCase ,				atol=1e-4						)
  print('''Logits ok!'''						)

  # verify loss, if applicable
  if model_name == "videomae-base-short":
    _lowercase:			List[str]       =     outputs.loss
    assert torch.allclose(_UpperCamelCase ,				_UpperCamelCase ,				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(_UpperCamelCase						)
    model.save_pretrained(_UpperCamelCase						)

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


if __name__ == "__main__":
  A__   :					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.'
  )

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