



'''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 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      snake_case__  :    Union[str, Any]    						=      VideoMAEConfig()

      set_architecture_configs(_lowerCAmelCase     ,   _lowerCAmelCase		)

      if "finetuned" not in model_name:
            snake_case__  :    Tuple    						=      False

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

      return config





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





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

      return name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[Any]:
      for key in orig_state_dict.copy().keys():
            snake_case__  :    str    						=      orig_state_dict.pop(_lowerCAmelCase		)

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

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

      return orig_state_dict





def 						__snake_case(				)		-> Any:
      snake_case__  :    List[Any]    						=      hf_hub_download(
          repo_id="""hf-internal-testing/spaghetti-video"""     ,   filename="""eating_spaghetti.npy"""     ,   repo_type="""dataset"""		)
      snake_case__  :    List[str]    						=      np.load(_lowerCAmelCase		)
      return list(_lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    Optional[Any]    						=      get_videomae_config(_lowerCAmelCase		)

      if "finetuned" in model_name:
            snake_case__  :    Tuple    						=      VideoMAEForVideoClassification(_lowerCAmelCase		)
      else:
            snake_case__  :    str    						=      VideoMAEForPreTraining(_lowerCAmelCase		)

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

      model.load_state_dict(_lowerCAmelCase		)
      model.eval()

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

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

      snake_case__  :    int    						=      model(**_lowerCAmelCase		)
      snake_case__  :    str    						=      outputs.logits

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





import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def        lowercase_							(A : List[str]      ):
       snake_case__							:	Tuple   	=	[
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(A		,					A      )
def        lowercase_							(A : str      ):
       snake_case__	,				snake_case__							:	Union[str, Any]   	=	emb.weight.shape
       snake_case__							:	str   	=	nn.Linear(A		,					A		,					bias=A      )
       snake_case__							:	str   	=	emb.weight.data
       return lin_layer
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]=None      ):
       snake_case__							:	Any   	=	{}
       for old_key in state_dict.keys():
              snake_case__							:	Tuple   	=	old_key
              if "moe_layer.experts." in key:
                     if expert_idx is not None:
                            snake_case__							:	int   	=	key.replace('moe_layer.experts.0'		,					F'''ffn.experts.expert_{expert_idx}'''      )
                     else:
                            snake_case__							:	Any   	=	key.replace('moe_layer.experts.'		,					'ffn.experts.expert_'      )
              if "gate" in key:
                     snake_case__							:	Dict   	=	key.replace('.moe_layer.gate.wg'		,					'.ffn.router.classifier'      )
              if "fc2" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc2.'		,					'.ffn.fc2.'      )
              if "fc1" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc1.'		,					'.ffn.fc1.'      )
              if ".encoder_attn." in key:
                     snake_case__							:	Tuple   	=	key.replace('.encoder_attn.'		,					'.cross_attention.'      )
              if "encoder_attn_layer_norm" in key:
                     snake_case__							:	Tuple   	=	key.replace('encoder_attn_layer_norm'		,					'cross_attention_layer_norm'      )
              if "final_layer_norm" in key:
                     snake_case__							:	Optional[int]   	=	key.replace('final_layer_norm'		,					'ff_layer_norm'      )
              snake_case__							:	Dict   	=	state_dict[old_key]
       return new_dict
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : List[Any]		,					A : List[str]		,					A : str = WEIGHTS_NAME      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	str   	=	0
       os.makedirs(A		,					exist_ok=A      )

       for expert in range(A      ):
              snake_case__							:	Tuple   	=	switch_checkpoint_path + F'''-rank-{expert}.pt'''
              if os.path.isfile(A      ):
                     snake_case__							:	Optional[Any]   	=	torch.load(A      )['model']
                     remove_ignore_keys_(A      )
                     snake_case__							:	Optional[Any]   	=	rename_fairseq_keys(A		,					A      )
                     snake_case__							:	Dict   	=	os.path.join(
                         A		,					weights_name.replace('.bin'		,					F'''-{len(A      )+1:05d}-of-???.bin'''      )      )
                     torch.save(A		,					A      )
                     sharded_state_dicts.append(expert_state.keys()      )
                     total_size += sum([value.numel() for key, value in expert_state.items()]      ) * dtype_byte_size(
                         expert_state[list(A      )[0]].dtype      )

    # Add the last block
       snake_case__							:	Tuple   	=	os.path.join(A		,					weights_name.replace('.bin'		,					F'''-{len(A      )+1:05d}-of-???.bin'''      )      )
       snake_case__							:	Union[str, Any]   	=	torch.load(switch_checkpoint_path + '-shared.pt'      )['model']
       remove_ignore_keys_(A      )
       snake_case__							:	str   	=	rename_fairseq_keys(A		,					A      )
       snake_case__							:	Any   	=	shared_weights['decoder.embed_tokens.weight']
       sharded_state_dicts.append(shared_weights.keys()      )

       # If we only have the shared weights (dummy model/experts saved on the same file)
       if len(A      ) == 1:
              snake_case__							:	Any   	=	os.path.join(A		,					A      )
              torch.save(A		,					A      )
              return {weights_name: sharded_state_dicts[0]}, None
       else:
              torch.save(A		,					A      )
       # Otherwise, let's build the index
       snake_case__							:	Tuple   	=	{}
       for idx, shard in enumerate(A      ):
              snake_case__							:	Optional[int]   	=	weights_name.replace('.bin'		,					F'''-{idx+1:05d}-of-{len(A      ):05d}.bin'''      )
              snake_case__							:	List[Any]   	=	os.path.join(A		,					weights_name.replace('.bin'		,					F'''-{idx+1:05d}-of-???.bin'''      )      )
              os.rename(A		,					os.path.join(A		,					A      )      )
              for key in shard:
                     snake_case__							:	Any   	=	shard_file

    # Add the metadata
       snake_case__							:	int   	=	{'total_size': total_size}
       snake_case__							:	Dict   	=	{'metadata': metadata, 'weight_map': weight_map}

       with open(os.path.join(A		,					A      )		,					'w'		,					encoding='utf-8'      ) as f:
              snake_case__							:	Any   	=	json.dumps(A		,					indent=2		,					sort_keys=A      ) + '\n'
              f.write(A      )

       return metadata, index


if __name__ == "__main__":
 a_   :int        =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--nllb_moe_checkpoint_path",
     default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
     type=str,
     required=False,
     help="Path to a directory containing a folder per layer. Follows the original Google format.",
 )
 parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
     type=str,
     required=False,
     help="Path to the output pytorch model.",
 )
 a_   :Optional[Any]        =      parser.parse_args()
 a_  ,						a_   :Optional[Any]        =      shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

 a_   :List[str]        =      NllbMoeConfig.from_pretrained(
     "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
 )
 config.save_pretrained(args.pytorch_dump_folder_path)
 a_   :int        =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("Done")
 model.save_pretrained(args.pytorch_dump_folder_path)






