






'''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(					UpperCamelCase__			:  str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			int													=VideoMAEConfig()

      set_architecture_configs(UpperCamelCase__,		UpperCamelCase__							)

      if "finetuned" not in model_name:
            SCREAMING_SNAKE_CASE__       :			Any													=False

      if "finetuned" in model_name:
            SCREAMING_SNAKE_CASE__       :			Tuple													='''huggingface/label-files'''
            if "kinetics" in model_name:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=4_0_0
                  SCREAMING_SNAKE_CASE__       :			int													='''kinetics400-id2label.json'''
            elif "ssv2" in model_name:
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=1_7_4
                  SCREAMING_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.'''							)
            SCREAMING_SNAKE_CASE__       :			Any													=json.load(open(hf_hub_download(UpperCamelCase__,		UpperCamelCase__,		repo_type='''dataset'''							),		'''r'''							)							)
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													={int(UpperCamelCase__							): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE__       :			List[Any]													=idalabel
            SCREAMING_SNAKE_CASE__       :			Dict													={v: k for k, v in idalabel.items()}

      return config





def 	_a(					UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






      if "small" in model_name:
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=3_8_4
            SCREAMING_SNAKE_CASE__       :			List[str]													=1_5_3_6
            SCREAMING_SNAKE_CASE__       :			int													=1_2
            SCREAMING_SNAKE_CASE__       :			List[Any]													=1_6
            SCREAMING_SNAKE_CASE__       :			List[Any]													=1_2
            SCREAMING_SNAKE_CASE__       :			List[Any]													=3
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=1_9_2
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=7_6_8
      elif "large" in model_name:
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=1_0_2_4
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=4_0_9_6
            SCREAMING_SNAKE_CASE__       :			List[Any]													=2_4
            SCREAMING_SNAKE_CASE__       :			List[str]													=1_6
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=1_2
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=8
            SCREAMING_SNAKE_CASE__       :			str													=5_1_2
            SCREAMING_SNAKE_CASE__       :			List[str]													=2_0_4_8
      elif "huge" in model_name:
            SCREAMING_SNAKE_CASE__       :			Any													=1_2_8_0
            SCREAMING_SNAKE_CASE__       :			Tuple													=5_1_2_0
            SCREAMING_SNAKE_CASE__       :			List[Any]													=3_2
            SCREAMING_SNAKE_CASE__       :			str													=1_6
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=1_2
            SCREAMING_SNAKE_CASE__       :			List[Any]													=8
            SCREAMING_SNAKE_CASE__       :			int													=6_4_0
            SCREAMING_SNAKE_CASE__       :			int													=2_5_6_0
      elif "base" not in model_name:
            raise ValueError('''Model name should include either "small", "base", "large", or "huge"'''							)





def 	_a(					UpperCamelCase__			:  Optional[int]							):




      '''simple docstring'''






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

      return name





def 	_a(					UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Optional[int]							):




      '''simple docstring'''






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

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

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

      return orig_state_dict





def 	_a(					):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Dict,		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[str]													=get_videomae_config(UpperCamelCase__							)

      if "finetuned" in model_name:
            SCREAMING_SNAKE_CASE__       :			str													=VideoMAEForVideoClassification(UpperCamelCase__							)
      else:
            SCREAMING_SNAKE_CASE__       :			Dict													=VideoMAEForPreTraining(UpperCamelCase__							)

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

      model.load_state_dict(UpperCamelCase__							)
      model.eval()

      # verify model on basic input
      SCREAMING_SNAKE_CASE__       :			Dict													=VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],		image_std=[0.5, 0.5, 0.5]							)
      SCREAMING_SNAKE_CASE__       :			Dict													=prepare_video()
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_processor(UpperCamelCase__,		return_tensors='''pt'''							)

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

      SCREAMING_SNAKE_CASE__       :			List[Any]													=model(**UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=outputs.logits

      SCREAMING_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":
            SCREAMING_SNAKE_CASE__       :			Dict													=torch.Size([1, 4_0_0]							)
            SCREAMING_SNAKE_CASE__       :			Tuple													=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__       :			Tuple													=torch.Size([1, 1_7_4]							)
            SCREAMING_SNAKE_CASE__       :			List[Any]													=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__       :			List[Any]													=torch.Size([1, 1_4_0_8, 1_5_3_6]							)
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=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__       :			Optional[int]													=torch.Size([1, 1_4_0_8, 1_5_3_6]							)
            SCREAMING_SNAKE_CASE__       :			int													=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__       :			Optional[Any]													=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__       :			Any													=torch.Size([1, 1_4_0_8, 1_5_3_6]							)
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=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__       :			Union[str, Any]													=torch.Size([1, 4_0_0]							)
            SCREAMING_SNAKE_CASE__       :			str													=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__       :			Optional[Any]													=torch.Size([1, 4_0_0]							)
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=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__       :			Any													=torch.Size([1, 4_0_0]							)
            SCREAMING_SNAKE_CASE__       :			str													=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__       :			Optional[int]													=torch.Size([1, 4_0_0]							)
            SCREAMING_SNAKE_CASE__       :			List[str]													=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__       :			Tuple													=torch.Size([1, 1_4_0_8, 1_5_3_6]							)
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=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__       :			Optional[int]													=torch.Size([1, 1_7_4]							)
            SCREAMING_SNAKE_CASE__       :			Any													=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__       :			List[Any]													=torch.Size([1, 1_4_0_8, 1_5_3_6]							)
            SCREAMING_SNAKE_CASE__       :			List[str]													=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__       :			Dict													=torch.Size([1, 1_7_4]							)
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=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],		UpperCamelCase__,		atol=1e-4							)
      else:
            print('''Logits:''',		logits[0, :3, :3]							)
            assert torch.allclose(logits[0, :3, :3],		UpperCamelCase__,		atol=1e-4							)
      print('''Logits ok!'''							)

      # verify loss, if applicable
      if model_name == "videomae-base-short":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=outputs.loss
            assert torch.allclose(UpperCamelCase__,		UpperCamelCase__,		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(UpperCamelCase__							)
            model.save_pretrained(UpperCamelCase__							)

      if push_to_hub:
            print('''Pushing to the hub...'''							)
            model.push_to_hub(UpperCamelCase__,		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)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_					=	{
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MegatronBertForCausalLM',
      'MegatronBertForMaskedLM',
      'MegatronBertForMultipleChoice',
      'MegatronBertForNextSentencePrediction',
      'MegatronBertForPreTraining',
      'MegatronBertForQuestionAnswering',
      'MegatronBertForSequenceClassification',
      'MegatronBertForTokenClassification',
      'MegatronBertModel',
      'MegatronBertPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_megatron_bert import (
        MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
        MegatronBertPreTrainedModel,
    )

else:
  import sys

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