





'''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   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				VideoMAEConfig()

      set_architecture_configs(lowerCAmelCase				,      lowerCAmelCase    )

      if "finetuned" not in model_name:
            _lowerCAmelCase					=				False

      if "finetuned" in model_name:
            _lowerCAmelCase					=				"""huggingface/label-files"""
            if "kinetics" in model_name:
                  _lowerCAmelCase					=				4_00
                  _lowerCAmelCase					=				"""kinetics400-id2label.json"""
            elif "ssv2" in model_name:
                  _lowerCAmelCase					=				1_74
                  _lowerCAmelCase					=				"""something-something-v2-id2label.json"""
            else:
                  raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""    )
            _lowerCAmelCase					=				json.load(open(hf_hub_download(lowerCAmelCase				,      lowerCAmelCase				,      repo_type="""dataset"""    )				,      """r"""    )    )
            _lowerCAmelCase					=				{int(lowerCAmelCase    ): v for k, v in idalabel.items()}
            _lowerCAmelCase					=				idalabel
            _lowerCAmelCase					=				{v: k for k, v in idalabel.items()}

      return config
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      if "small" in model_name:
            _lowerCAmelCase					=				3_84
            _lowerCAmelCase					=				15_36
            _lowerCAmelCase					=				12
            _lowerCAmelCase					=				16
            _lowerCAmelCase					=				12
            _lowerCAmelCase					=				3
            _lowerCAmelCase					=				1_92
            _lowerCAmelCase					=				7_68
      elif "large" in model_name:
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				40_96
            _lowerCAmelCase					=				24
            _lowerCAmelCase					=				16
            _lowerCAmelCase					=				12
            _lowerCAmelCase					=				8
            _lowerCAmelCase					=				5_12
            _lowerCAmelCase					=				20_48
      elif "huge" in model_name:
            _lowerCAmelCase					=				12_80
            _lowerCAmelCase					=				51_20
            _lowerCAmelCase					=				32
            _lowerCAmelCase					=				16
            _lowerCAmelCase					=				12
            _lowerCAmelCase					=				8
            _lowerCAmelCase					=				6_40
            _lowerCAmelCase					=				25_60
      elif "base" not in model_name:
            raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if "encoder." in name:
            _lowerCAmelCase					=				name.replace("""encoder."""				,      """"""    )
      if "cls_token" in name:
            _lowerCAmelCase					=				name.replace("""cls_token"""				,      """videomae.embeddings.cls_token"""    )
      if "decoder_pos_embed" in name:
            _lowerCAmelCase					=				name.replace("""decoder_pos_embed"""				,      """decoder.decoder_pos_embed"""    )
      if "pos_embed" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""pos_embed"""				,      """videomae.embeddings.position_embeddings"""    )
      if "patch_embed.proj" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed.proj"""				,      """videomae.embeddings.patch_embeddings.projection"""    )
      if "patch_embed.norm" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed.norm"""				,      """videomae.embeddings.norm"""    )
      if "decoder.blocks" in name:
            _lowerCAmelCase					=				name.replace("""decoder.blocks"""				,      """decoder.decoder_layers"""    )
      if "blocks" in name:
            _lowerCAmelCase					=				name.replace("""blocks"""				,      """videomae.encoder.layer"""    )
      if "attn.proj" in name:
            _lowerCAmelCase					=				name.replace("""attn.proj"""				,      """attention.output.dense"""    )
      if "attn" in name and "bias" not in name:
            _lowerCAmelCase					=				name.replace("""attn"""				,      """attention.self"""    )
      if "attn" in name:
            _lowerCAmelCase					=				name.replace("""attn"""				,      """attention.attention"""    )
      if "norm1" in name:
            _lowerCAmelCase					=				name.replace("""norm1"""				,      """layernorm_before"""    )
      if "norm2" in name:
            _lowerCAmelCase					=				name.replace("""norm2"""				,      """layernorm_after"""    )
      if "mlp.fc1" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc1"""				,      """intermediate.dense"""    )
      if "mlp.fc2" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc2"""				,      """output.dense"""    )
      if "decoder_embed" in name:
            _lowerCAmelCase					=				name.replace("""decoder_embed"""				,      """decoder.decoder_embed"""    )
      if "decoder_norm" in name:
            _lowerCAmelCase					=				name.replace("""decoder_norm"""				,      """decoder.decoder_norm"""    )
      if "decoder_pred" in name:
            _lowerCAmelCase					=				name.replace("""decoder_pred"""				,      """decoder.decoder_pred"""    )
      if "norm.weight" in name and "decoder" not in name and "fc" not in name:
            _lowerCAmelCase					=				name.replace("""norm.weight"""				,      """videomae.layernorm.weight"""    )
      if "norm.bias" in name and "decoder" not in name and "fc" not in name:
            _lowerCAmelCase					=				name.replace("""norm.bias"""				,      """videomae.layernorm.bias"""    )
      if "head" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""head"""				,      """classifier"""    )

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      for key in orig_state_dict.copy().keys():
            _lowerCAmelCase					=				orig_state_dict.pop(lowerCAmelCase    )

            if key.startswith("""encoder."""    ):
                  _lowerCAmelCase					=				key.replace("""encoder."""				,      """"""    )

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

      return orig_state_dict
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				hf_hub_download(
          repo_id="""hf-internal-testing/spaghetti-video"""				,      filename="""eating_spaghetti.npy"""				,      repo_type="""dataset"""    )
      _lowerCAmelCase					=				np.load(lowerCAmelCase    )
      return list(lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				get_videomae_config(lowerCAmelCase    )

      if "finetuned" in model_name:
            _lowerCAmelCase					=				VideoMAEForVideoClassification(lowerCAmelCase    )
      else:
            _lowerCAmelCase					=				VideoMAEForPreTraining(lowerCAmelCase    )

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

      model.load_state_dict(lowerCAmelCase    )
      model.eval()

      # verify model on basic input
      _lowerCAmelCase					=				VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]				,      image_std=[0.5, 0.5, 0.5]    )
      _lowerCAmelCase					=				prepare_video()
      _lowerCAmelCase					=				image_processor(lowerCAmelCase				,      return_tensors="""pt"""    )

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

      _lowerCAmelCase					=				model(**lowerCAmelCase    )
      _lowerCAmelCase					=				outputs.logits

      _lowerCAmelCase					=				[
          """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					=				torch.Size([1, 4_00]    )
            _lowerCAmelCase					=				torch.tensor([-0.9_291, -0.4_061, -0.9_307]    )
      elif model_name == "videomae-small-finetuned-ssv2":
            _lowerCAmelCase					=				torch.Size([1, 1_74]    )
            _lowerCAmelCase					=				torch.tensor([0.2_671, -0.4_689, -0.8_235]    )
      elif model_name == "videomae-base":
            _lowerCAmelCase					=				torch.Size([1, 14_08, 15_36]    )
            _lowerCAmelCase					=				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":
            _lowerCAmelCase					=				torch.Size([1, 14_08, 15_36]    )
            _lowerCAmelCase					=				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
            _lowerCAmelCase					=				torch.tensor([0.5_142]    ) if config.norm_pix_loss else torch.tensor([0.6_469]    )
      elif model_name == "videomae-large":
            _lowerCAmelCase					=				torch.Size([1, 14_08, 15_36]    )
            _lowerCAmelCase					=				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":
            _lowerCAmelCase					=				torch.Size([1, 4_00]    )
            _lowerCAmelCase					=				torch.tensor([0.0_771, 0.0_011, -0.3_625]    )
      elif model_name == "videomae-huge-finetuned-kinetics":
            _lowerCAmelCase					=				torch.Size([1, 4_00]    )
            _lowerCAmelCase					=				torch.tensor([0.2_433, 0.1_632, -0.4_894]    )
      elif model_name == "videomae-base-short-finetuned-kinetics":
            _lowerCAmelCase					=				torch.Size([1, 4_00]    )
            _lowerCAmelCase					=				torch.tensor([0.6_588, 0.0_990, -0.2_493]    )
      elif model_name == "videomae-base-finetuned-kinetics":
            _lowerCAmelCase					=				torch.Size([1, 4_00]    )
            _lowerCAmelCase					=				torch.tensor([0.3_669, -0.0_688, -0.2_421]    )
      elif model_name == "videomae-base-short-ssv2":
            _lowerCAmelCase					=				torch.Size([1, 14_08, 15_36]    )
            _lowerCAmelCase					=				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":
            _lowerCAmelCase					=				torch.Size([1, 1_74]    )
            _lowerCAmelCase					=				torch.tensor([-0.0_537, -0.1_539, -0.3_266]    )
      elif model_name == "videomae-base-ssv2":
            _lowerCAmelCase					=				torch.Size([1, 14_08, 15_36]    )
            _lowerCAmelCase					=				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":
            _lowerCAmelCase					=				torch.Size([1, 1_74]    )
            _lowerCAmelCase					=				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]				,      lowerCAmelCase				,      atol=1e-4    )
      else:
            print("""Logits:"""				,      logits[0, :3, :3]    )
            assert torch.allclose(logits[0, :3, :3]				,      lowerCAmelCase				,      atol=1e-4    )
      print("""Logits ok!"""    )

      # verify loss, if applicable
      if model_name == "videomae-base-short":
            _lowerCAmelCase					=				outputs.loss
            assert torch.allclose(lowerCAmelCase				,      lowerCAmelCase				,      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(lowerCAmelCase    )
            model.save_pretrained(lowerCAmelCase    )

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


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

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













'''simple docstring'''






from __future__ import annotations

import numpy as np
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				np.shape(lowerCAmelCase    )
      if rows != columns:
            _lowerCAmelCase					=				(
                """'table' has to be of square shaped array but got a """
                f"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(lowerCAmelCase    )

      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      for i in range(lowerCAmelCase    ):
            for j in range(lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""    )
                  _lowerCAmelCase					=				(table[i][j] - total) / upper[j][j]
            _lowerCAmelCase					=				1
            for j in range(lowerCAmelCase				,      lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  _lowerCAmelCase					=				table[i][j] - total
      return lower, upper


if __name__ == "__main__":
      import doctest

      doctest.testmod()






