





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				(	lowerCamelCase:   List[Any] )	->						List[Any]:







      '''simple docstring'''






      __A          =				VideoMAEConfig()

      set_architecture_configs(lowerCamelCase					,				lowerCamelCase )

      if "finetuned" not in model_name:
            __A          =				False

      if "finetuned" in model_name:
            __A          =				'''huggingface/label-files'''
            if "kinetics" in model_name:
                  __A          =				4_00
                  __A          =				'''kinetics400-id2label.json'''
            elif "ssv2" in model_name:
                  __A          =				1_74
                  __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(lowerCamelCase					,				lowerCamelCase					,				repo_type='''dataset''' )					,				'''r''' ) )
            __A          =				{int(lowerCamelCase ): v for k, v in idalabel.items()}
            __A          =				idalabel
            __A          =				{v: k for k, v in idalabel.items()}

      return config



def _a				(	lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   List[Any] )	->						Optional[Any]:







      '''simple docstring'''






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



def _a				(	lowerCamelCase:   Dict )	->						Union[str, Any]:







      '''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				(	lowerCamelCase:   Dict					,				lowerCamelCase:   str )	->						Union[str, Any]:







      '''simple docstring'''






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

            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				(	)	->						str:







      '''simple docstring'''






      __A          =				hf_hub_download(
          repo_id='''hf-internal-testing/spaghetti-video'''					,				filename='''eating_spaghetti.npy'''					,				repo_type='''dataset''' )
      __A          =				np.load(lowerCamelCase )
      return list(lowerCamelCase )



def _a				(	lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   int					,				lowerCamelCase:   Union[str, Any] )	->						List[Any]:







      '''simple docstring'''






      __A          =				get_videomae_config(lowerCamelCase )

      if "finetuned" in model_name:
            __A          =				VideoMAEForVideoClassification(lowerCamelCase )
      else:
            __A          =				VideoMAEForPreTraining(lowerCamelCase )

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

      model.load_state_dict(lowerCamelCase )
      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(lowerCamelCase					,				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(lowerCamelCase )

      __A          =				model(**lowerCamelCase )
      __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, 4_00] )
            __A          =				torch.tensor([-0.9_291, -0.4_061, -0.9_307] )
      elif model_name == "videomae-small-finetuned-ssv2":
            __A          =				torch.Size([1, 1_74] )
            __A          =				torch.tensor([0.2_671, -0.4_689, -0.8_235] )
      elif model_name == "videomae-base":
            __A          =				torch.Size([1, 14_08, 15_36] )
            __A          =				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":
            __A          =				torch.Size([1, 14_08, 15_36] )
            __A          =				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
            __A          =				torch.tensor([0.5_142] ) if config.norm_pix_loss else torch.tensor([0.6_469] )
      elif model_name == "videomae-large":
            __A          =				torch.Size([1, 14_08, 15_36] )
            __A          =				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":
            __A          =				torch.Size([1, 4_00] )
            __A          =				torch.tensor([0.0_771, 0.0_011, -0.3_625] )
      elif model_name == "videomae-huge-finetuned-kinetics":
            __A          =				torch.Size([1, 4_00] )
            __A          =				torch.tensor([0.2_433, 0.1_632, -0.4_894] )
      elif model_name == "videomae-base-short-finetuned-kinetics":
            __A          =				torch.Size([1, 4_00] )
            __A          =				torch.tensor([0.6_588, 0.0_990, -0.2_493] )
      elif model_name == "videomae-base-finetuned-kinetics":
            __A          =				torch.Size([1, 4_00] )
            __A          =				torch.tensor([0.3_669, -0.0_688, -0.2_421] )
      elif model_name == "videomae-base-short-ssv2":
            __A          =				torch.Size([1, 14_08, 15_36] )
            __A          =				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":
            __A          =				torch.Size([1, 1_74] )
            __A          =				torch.tensor([-0.0_537, -0.1_539, -0.3_266] )
      elif model_name == "videomae-base-ssv2":
            __A          =				torch.Size([1, 14_08, 15_36] )
            __A          =				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":
            __A          =				torch.Size([1, 1_74] )
            __A          =				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":
            __A          =				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__":
       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__					:							List[str]													=     parser.parse_args()
       convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)












# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case__					:							List[str]													=     {
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							Tuple													=     ['VivitImageProcessor']


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							Any													=     [
           'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VivitModel',
           'VivitPreTrainedModel',
           'VivitForVideoClassification',
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

       snake_case__					:							List[str]													=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





