"""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 						__a   (	_SCREAMING_SNAKE_CASE   )	->int:
  a__:     Any										=	VideoMAEConfig()

  set_architecture_configs(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  if "finetuned" not in model_name:
    a__:     Optional[Any]										=	False

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

  return config







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  if "small" in model_name:
    a__:     List[Any]										=	384
    a__:     int										=	1536
    a__:     Union[str, Any]										=	12
    a__:     Tuple										=	16
    a__:     Optional[Any]										=	12
    a__:     List[Any]										=	3
    a__:     List[str]										=	192
    a__:     Optional[Any]										=	768
  elif "large" in model_name:
    a__:     Optional[Any]										=	1024
    a__:     Any										=	4096
    a__:     int										=	24
    a__:     int										=	16
    a__:     Dict										=	12
    a__:     Optional[int]										=	8
    a__:     Union[str, Any]										=	512
    a__:     Any										=	2048
  elif "huge" in model_name:
    a__:     Any										=	1280
    a__:     Dict										=	5120
    a__:     Optional[Any]										=	32
    a__:     List[Any]										=	16
    a__:     Optional[int]										=	12
    a__:     Dict										=	8
    a__:     Any										=	640
    a__:     Union[str, Any]										=	2560
  elif "base" not in model_name:
    raise ValueError('Model name should include either "small", "base", "large", or "huge"'   )







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

  return name







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  for key in orig_state_dict.copy().keys():
    a__:     Any										=	orig_state_dict.pop(_SCREAMING_SNAKE_CASE   )

    if key.startswith('encoder.'   ):
      a__:     List[Any]										=	key.replace('encoder.'		,     ''   )

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

  return orig_state_dict







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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  a__:     Tuple										=	get_videomae_config(_SCREAMING_SNAKE_CASE   )

  if "finetuned" in model_name:
    a__:     Union[str, Any]										=	VideoMAEForVideoClassification(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     Optional[Any]										=	VideoMAEForPreTraining(_SCREAMING_SNAKE_CASE   )

  # download original checkpoint, hosted on Google Drive
  a__:     Any										=	'pytorch_model.bin'
  gdown.cached_download(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     quiet=_SCREAMING_SNAKE_CASE   )
  a__:     Optional[Any]										=	torch.load(_SCREAMING_SNAKE_CASE		,     map_location='cpu'   )
  if "model" in files:
    a__:     List[str]										=	files['model']
  else:
    a__:     List[str]										=	files['module']
  a__:     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
  a__:     Union[str, Any]										=	VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]		,     image_std=[0.5, 0.5, 0.5]   )
  a__:     Tuple										=	prepare_video()
  a__:     Dict										=	image_processor(_SCREAMING_SNAKE_CASE		,     return_tensors='pt'   )

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

  a__:     Union[str, Any]										=	model(**_SCREAMING_SNAKE_CASE   )
  a__:     Dict										=	outputs.logits

  a__:     Dict										=	[
      '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__:     Dict										=	torch.Size([1, 400]   )
    a__:     List[str]										=	torch.tensor([-0.9_291, -0.4_061, -0.9_307]   )
  elif model_name == "videomae-small-finetuned-ssv2":
    a__:     Optional[int]										=	torch.Size([1, 174]   )
    a__:     List[Any]										=	torch.tensor([0.2_671, -0.4_689, -0.8_235]   )
  elif model_name == "videomae-base":
    a__:     Tuple										=	torch.Size([1, 1408, 1536]   )
    a__:     List[str]										=	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__:     Optional[int]										=	torch.Size([1, 1408, 1536]   )
    a__:     Union[str, Any]										=	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__:     Optional[Any]										=	torch.tensor([0.5_142]   ) if config.norm_pix_loss else torch.tensor([0.6_469]   )
  elif model_name == "videomae-large":
    a__:     Optional[Any]										=	torch.Size([1, 1408, 1536]   )
    a__:     List[Any]										=	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__:     Dict										=	torch.Size([1, 400]   )
    a__:     Union[str, Any]										=	torch.tensor([0.0_771, 0.0_011, -0.3_625]   )
  elif model_name == "videomae-huge-finetuned-kinetics":
    a__:     Tuple										=	torch.Size([1, 400]   )
    a__:     Optional[int]										=	torch.tensor([0.2_433, 0.1_632, -0.4_894]   )
  elif model_name == "videomae-base-short-finetuned-kinetics":
    a__:     Tuple										=	torch.Size([1, 400]   )
    a__:     Dict										=	torch.tensor([0.6_588, 0.0_990, -0.2_493]   )
  elif model_name == "videomae-base-finetuned-kinetics":
    a__:     List[str]										=	torch.Size([1, 400]   )
    a__:     Union[str, Any]										=	torch.tensor([0.3_669, -0.0_688, -0.2_421]   )
  elif model_name == "videomae-base-short-ssv2":
    a__:     List[str]										=	torch.Size([1, 1408, 1536]   )
    a__:     List[Any]										=	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__:     str										=	torch.Size([1, 174]   )
    a__:     Optional[int]										=	torch.tensor([-0.0_537, -0.1_539, -0.3_266]   )
  elif model_name == "videomae-base-ssv2":
    a__:     str										=	torch.Size([1, 1408, 1536]   )
    a__:     int										=	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__:     Optional[int]										=	torch.Size([1, 174]   )
    a__:     Optional[int]										=	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]		,     _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":
    a__:     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__":
 lowercase__					       =	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.'
 )

 lowercase__					       =	parser.parse_args()
 convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowercase__					       =	{'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
 from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
 import sys

 lowercase__					       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


