

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_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					VideoMAEConfig()

     set_architecture_configs(_A   ,	_A       )

     if "finetuned" not in model_name:
          SCREAMING_SNAKE_CASE__					=					False

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

     return config
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




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





     '''simple docstring'''




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

     return name
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     for key in orig_state_dict.copy().keys():
          SCREAMING_SNAKE_CASE__					=					orig_state_dict.pop(_A       )

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

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

     return orig_state_dict
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''   ,	filename='''eating_spaghetti.npy'''   ,	repo_type='''dataset'''       )
     SCREAMING_SNAKE_CASE__					=					np.load(_A       )
     return list(_A       )
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_videomae_config(_A       )

     if "finetuned" in model_name:
          SCREAMING_SNAKE_CASE__					=					VideoMAEForVideoClassification(_A       )
     else:
          SCREAMING_SNAKE_CASE__					=					VideoMAEForPreTraining(_A       )

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

     model.load_state_dict(_A       )
     model.eval()

     # verify model on basic input
     SCREAMING_SNAKE_CASE__					=					VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   ,	image_std=[0.5, 0.5, 0.5]       )
     SCREAMING_SNAKE_CASE__					=					prepare_video()
     SCREAMING_SNAKE_CASE__					=					image_processor(_A   ,	return_tensors='''pt'''       )

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

     SCREAMING_SNAKE_CASE__					=					model(**_A       )
     SCREAMING_SNAKE_CASE__					=					outputs.logits

     SCREAMING_SNAKE_CASE__					=					[
         '''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":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]       )
     elif model_name == "videomae-small-finetuned-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 1_74]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]       )
     elif model_name == "videomae-base":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]       )
     elif model_name == "videomae-base-short":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]       )
          # we verified the loss both for normalized and unnormalized targets for this one
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.5_1_4_2]       ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]       )
     elif model_name == "videomae-large":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]       )
     elif model_name == "videomae-large-finetuned-kinetics":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]       )
     elif model_name == "videomae-huge-finetuned-kinetics":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]       )
     elif model_name == "videomae-base-short-finetuned-kinetics":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]       )
     elif model_name == "videomae-base-finetuned-kinetics":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 4_00]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]       )
     elif model_name == "videomae-base-short-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]       )
     elif model_name == "videomae-base-short-finetuned-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 1_74]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]       )
     elif model_name == "videomae-base-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 14_08, 15_36]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]       )
     elif model_name == "videomae-base-finetuned-ssv2":
          SCREAMING_SNAKE_CASE__					=					torch.Size([1, 1_74]       )
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]       )
     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":
          SCREAMING_SNAKE_CASE__					=					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__":
       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    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.'''
       )

       _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    parser.parse_args()
       convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate






@dataclass(frozen=A__ )
class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      field(default="question-answering-extractive"		,  metadata={"include_in_asdict_even_if_is_default": True} )
  a     =      Features({"question": Value("string" ), "context": Value("string" )} )
  a     =      Features(
      {
          "answers": Sequence(
              {
                  "text": Value("string" ),
                  "answer_start": Value("int32" ),
              } )
      } )
  a     =      "question"
  a     =      "context"
  a     =      "answers"






  @property
  def    lowercase_	(							self		:	Dict      )	->							Dict[str, str]:
       return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}


