
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      )      ->   str:
   _A:  str					=       VideoMAEConfig()

   set_architecture_configs(a		,		a      )

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

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

   return config






def   lowerCamelCase__							(					a		,		a      )      ->   Union[str, Any]:
   if "small" in model_name:
      _A:  Dict					=       3_84
      _A:  List[str]					=       15_36
      _A:  Union[str, Any]					=       12
      _A:  Any					=       16
      _A:  str					=       12
      _A:  int					=       3
      _A:  Tuple					=       1_92
      _A:  Union[str, Any]					=       7_68
   elif "large" in model_name:
      _A:  Dict					=       10_24
      _A:  int					=       40_96
      _A:  str					=       24
      _A:  str					=       16
      _A:  Any					=       12
      _A:  str					=       8
      _A:  List[Any]					=       5_12
      _A:  Dict					=       20_48
   elif "huge" in model_name:
      _A:  Any					=       12_80
      _A:  Optional[Any]					=       51_20
      _A:  Optional[int]					=       32
      _A:  Any					=       16
      _A:  Union[str, Any]					=       12
      _A:  List[Any]					=       8
      _A:  Tuple					=       6_40
      _A:  Optional[Any]					=       25_60
   elif "base" not in model_name:
      raise ValueError('''Model name should include either "small", "base", "large", or "huge"'''      )






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

   return name






def   lowerCamelCase__							(					a		,		a      )      ->   Any:
   for key in orig_state_dict.copy().keys():
      _A:  Optional[int]					=       orig_state_dict.pop(a      )

      if key.startswith('''encoder.'''      ):
         _A:  str					=       key.replace('''encoder.'''		,		''''''      )

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

   return orig_state_dict






def   lowerCamelCase__							(					)      ->   Dict:
   _A:  List[Any]					=       hf_hub_download(
       repo_id='''hf-internal-testing/spaghetti-video'''		,		filename='''eating_spaghetti.npy'''		,		repo_type='''dataset'''      )
   _A:  Optional[Any]					=       np.load(a      )
   return list(a      )






def   lowerCamelCase__							(					a		,		a		,		a		,		a      )      ->   Optional[Any]:
   _A:  str					=       get_videomae_config(a      )

   if "finetuned" in model_name:
      _A:  Dict					=       VideoMAEForVideoClassification(a      )
   else:
      _A:  Optional[Any]					=       VideoMAEForPreTraining(a      )

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

   model.load_state_dict(a      )
   model.eval()

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

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

   _A:  Any					=       model(**a      )
   _A:  Optional[Any]					=       outputs.logits

   _A:  Tuple					=       [
       '''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:  Optional[Any]					=       torch.Size([1, 4_00]      )
      _A:  Dict					=       torch.tensor([-0.9291, -0.4061, -0.9307]      )
   elif model_name == "videomae-small-finetuned-ssv2":
      _A:  str					=       torch.Size([1, 1_74]      )
      _A:  str					=       torch.tensor([0.2671, -0.4689, -0.8235]      )
   elif model_name == "videomae-base":
      _A:  Optional[int]					=       torch.Size([1, 14_08, 15_36]      )
      _A:  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":
      _A:  List[Any]					=       torch.Size([1, 14_08, 15_36]      )
      _A:  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
      _A:  str					=       torch.tensor([0.5142]      ) if config.norm_pix_loss else torch.tensor([0.6469]      )
   elif model_name == "videomae-large":
      _A:  int					=       torch.Size([1, 14_08, 15_36]      )
      _A:  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":
      _A:  Tuple					=       torch.Size([1, 4_00]      )
      _A:  List[Any]					=       torch.tensor([0.0771, 0.0011, -0.3625]      )
   elif model_name == "videomae-huge-finetuned-kinetics":
      _A:  List[str]					=       torch.Size([1, 4_00]      )
      _A:  List[Any]					=       torch.tensor([0.2433, 0.1632, -0.4894]      )
   elif model_name == "videomae-base-short-finetuned-kinetics":
      _A:  List[Any]					=       torch.Size([1, 4_00]      )
      _A:  List[Any]					=       torch.tensor([0.6588, 0.0990, -0.2493]      )
   elif model_name == "videomae-base-finetuned-kinetics":
      _A:  int					=       torch.Size([1, 4_00]      )
      _A:  int					=       torch.tensor([0.3669, -0.0688, -0.2421]      )
   elif model_name == "videomae-base-short-ssv2":
      _A:  Tuple					=       torch.Size([1, 14_08, 15_36]      )
      _A:  List[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":
      _A:  Tuple					=       torch.Size([1, 1_74]      )
      _A:  int					=       torch.tensor([-0.0537, -0.1539, -0.3266]      )
   elif model_name == "videomae-base-ssv2":
      _A:  str					=       torch.Size([1, 14_08, 15_36]      )
      _A:  int					=       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":
      _A:  Optional[int]					=       torch.Size([1, 1_74]      )
      _A:  Dict					=       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":
      _A:  int					=       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__":
   UpperCAmelCase__		:     Union[str, 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.'
   )

   UpperCAmelCase__		:     Union[str, Any]     			=      parser.parse_args()
   convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





from .imports import is_tqdm_available


if is_tqdm_available():
   from tqdm.auto import tqdm as _tqdm

from ..state import PartialState






def   lowerCamelCase__							(					a = True		,		*a		,		**a      )      ->   Optional[Any]:
   if not is_tqdm_available():
      raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'''      )
   _A:  Optional[Any]					=       False
   if main_process_only:
      _A:  Union[str, Any]					=       PartialState().local_process_index == 0
   return _tqdm(*a		,		**a		,		disable=a      )



