

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__			(    SCREAMING_SNAKE_CASE_		:    List[Any]			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       VideoMAEConfig()

     set_architecture_configs(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)

     if "finetuned" not in model_name:
          lowercase__    :   List[Any]      =       False

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

     return config

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    List[Any]			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict			):



     '''simple docstring'''





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



     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          lowercase__    :   int      =       orig_state_dict.pop(SCREAMING_SNAKE_CASE_			)

          if key.startswith('encoder.'			):
               lowercase__    :   Tuple      =       key.replace('encoder.'	,     ''			)

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

     return orig_state_dict

def 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   Any      =       hf_hub_download(
         repo_id='hf-internal-testing/spaghetti-video'	,     filename='eating_spaghetti.npy'	,     repo_type='dataset'			)
     lowercase__    :   Optional[Any]      =       np.load(SCREAMING_SNAKE_CASE_			)
     return list(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





     lowercase__    :   Dict      =       get_videomae_config(SCREAMING_SNAKE_CASE_			)

     if "finetuned" in model_name:
          lowercase__    :   Any      =       VideoMAEForVideoClassification(SCREAMING_SNAKE_CASE_			)
     else:
          lowercase__    :   str      =       VideoMAEForPreTraining(SCREAMING_SNAKE_CASE_			)

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

     model.load_state_dict(SCREAMING_SNAKE_CASE_			)
     model.eval()

     # verify model on basic input
     lowercase__    :   int      =       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,     image_std=[0.5, 0.5, 0.5]			)
     lowercase__    :   Union[str, Any]      =       prepare_video()
     lowercase__    :   List[str]      =       image_processor(SCREAMING_SNAKE_CASE_	,     return_tensors='pt'			)

     if "finetuned" not in model_name:
          lowercase__    :   int      =       hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'	,     filename='bool_masked_pos.pt'			)
          lowercase__    :   Dict      =       torch.load(SCREAMING_SNAKE_CASE_			)

     lowercase__    :   Any      =       model(**SCREAMING_SNAKE_CASE_			)
     lowercase__    :   List[Any]      =       outputs.logits

     lowercase__    :   int      =       [
         '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__    :   Any      =       torch.Size([1, 400]			)
          lowercase__    :   str      =       torch.tensor([-0.9291, -0.4061, -0.9307]			)
     elif model_name == "videomae-small-finetuned-ssv2":
          lowercase__    :   str      =       torch.Size([1, 174]			)
          lowercase__    :   Union[str, Any]      =       torch.tensor([0.2671, -0.4689, -0.8235]			)
     elif model_name == "videomae-base":
          lowercase__    :   Dict      =       torch.Size([1, 1_408, 1_536]			)
          lowercase__    :   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":
          lowercase__    :   List[Any]      =       torch.Size([1, 1_408, 1_536]			)
          lowercase__    :   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
          lowercase__    :   Union[str, Any]      =       torch.tensor([0.5142]			) if config.norm_pix_loss else torch.tensor([0.6469]			)
     elif model_name == "videomae-large":
          lowercase__    :   Any      =       torch.Size([1, 1_408, 1_536]			)
          lowercase__    :   List[str]      =       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":
          lowercase__    :   int      =       torch.Size([1, 400]			)
          lowercase__    :   Union[str, Any]      =       torch.tensor([0.0771, 0.0011, -0.3625]			)
     elif model_name == "videomae-huge-finetuned-kinetics":
          lowercase__    :   Tuple      =       torch.Size([1, 400]			)
          lowercase__    :   Any      =       torch.tensor([0.2433, 0.1632, -0.4894]			)
     elif model_name == "videomae-base-short-finetuned-kinetics":
          lowercase__    :   Optional[int]      =       torch.Size([1, 400]			)
          lowercase__    :   Optional[Any]      =       torch.tensor([0.6588, 0.0990, -0.2493]			)
     elif model_name == "videomae-base-finetuned-kinetics":
          lowercase__    :   Union[str, Any]      =       torch.Size([1, 400]			)
          lowercase__    :   List[str]      =       torch.tensor([0.3669, -0.0688, -0.2421]			)
     elif model_name == "videomae-base-short-ssv2":
          lowercase__    :   List[str]      =       torch.Size([1, 1_408, 1_536]			)
          lowercase__    :   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":
          lowercase__    :   List[str]      =       torch.Size([1, 174]			)
          lowercase__    :   Dict      =       torch.tensor([-0.0537, -0.1539, -0.3266]			)
     elif model_name == "videomae-base-ssv2":
          lowercase__    :   Optional[Any]      =       torch.Size([1, 1_408, 1_536]			)
          lowercase__    :   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":
          lowercase__    :   str      =       torch.Size([1, 174]			)
          lowercase__    :   List[Any]      =       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]	,     SCREAMING_SNAKE_CASE_	,     atol=1E-4			)
     else:
          print('Logits:'	,     logits[0, :3, :3]			)
          assert torch.allclose(logits[0, :3, :3]	,     SCREAMING_SNAKE_CASE_	,     atol=1E-4			)
     print('Logits ok!'			)

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

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


if __name__ == "__main__":
       snake_case_			  = 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_			  = parser.parse_args()
       convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)








from __future__ import annotations



def _a				(	lowerCamelCase:   list[int | str] )	->						None:







      '''simple docstring'''






      create_state_space_tree(lowerCamelCase					,				[]					,				0					,				[0 for i in range(len(lowerCamelCase ) )] )



def _a				(	lowerCamelCase:   list[int | str]					,				lowerCamelCase:   list[int | str]					,				lowerCamelCase:   int					,				lowerCamelCase:   list[int]					,				)	->						None:







      '''simple docstring'''






      if index == len(lowerCamelCase ):
            print(lowerCamelCase )
            return

      for i in range(len(lowerCamelCase ) ):
            if not index_used[i]:
                  current_sequence.append(sequence[i] )
                  __A          =				True
                  create_state_space_tree(lowerCamelCase					,				lowerCamelCase					,				index + 1					,				lowerCamelCase )
                  current_sequence.pop()
                  __A          =				False
snake_case__					:							list[int | str]													=     [3, 1, 2, 4]
generate_all_permutations(sequence)

snake_case__					:							list[int | str]													=     ["A", "B", "C"]







generate_all_permutations(sequence_a)





