






'''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       __UpperCamelCase					(      lowercase_       :  Dict   ):
       """simple docstring"""




       a_						     =			VideoMAEConfig()

       set_architecture_configs(lowerCamelCase_   ,       lowerCamelCase_   )

       if "finetuned" not in model_name:
              a_						     =			False

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

       return config


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ,       lowercase_       :  int   ):
       """simple docstring"""




       if "small" in model_name:
              a_						     =			384
              a_						     =			1_536
              a_						     =			12
              a_						     =			16
              a_						     =			12
              a_						     =			3
              a_						     =			192
              a_						     =			768
       elif "large" in model_name:
              a_						     =			1_024
              a_						     =			4_096
              a_						     =			24
              a_						     =			16
              a_						     =			12
              a_						     =			8
              a_						     =			512
              a_						     =			2_048
       elif "huge" in model_name:
              a_						     =			1_280
              a_						     =			5_120
              a_						     =			32
              a_						     =			16
              a_						     =			12
              a_						     =			8
              a_						     =			640
              a_						     =			2_560
       elif "base" not in model_name:
              raise ValueError('Model name should include either \"small\", \"base\", \"large\", or \"huge\"'   )


def       __UpperCamelCase					(      lowercase_       :  Tuple   ):
       """simple docstring"""




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

       return name


def       __UpperCamelCase					(      lowercase_       :  Any   ,       lowercase_       :  str   ):
       """simple docstring"""




       for key in orig_state_dict.copy().keys():
              a_						     =			orig_state_dict.pop(lowerCamelCase_   )

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

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

       return orig_state_dict


def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			hf_hub_download(
           repo_id='hf-internal-testing/spaghetti-video'   ,       filename='eating_spaghetti.npy'   ,       repo_type='dataset'   )
       a_						     =			np.load(lowerCamelCase_   )
       return list(lowerCamelCase_   )


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  str   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  Optional[Any]   ):
       """simple docstring"""




       a_						     =			get_videomae_config(lowerCamelCase_   )

       if "finetuned" in model_name:
              a_						     =			VideoMAEForVideoClassification(lowerCamelCase_   )
       else:
              a_						     =			VideoMAEForPreTraining(lowerCamelCase_   )

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

       model.load_state_dict(lowerCamelCase_   )
       model.eval()

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

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

       a_						     =			model(**lowerCamelCase_   )
       a_						     =			outputs.logits

       a_						     =			[
           "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_						     =			torch.Size([1, 400]   )
              a_						     =			torch.tensor([-0.9291, -0.4061, -0.9307]   )
       elif model_name == "videomae-small-finetuned-ssv2":
              a_						     =			torch.Size([1, 174]   )
              a_						     =			torch.tensor([0.2671, -0.4689, -0.8235]   )
       elif model_name == "videomae-base":
              a_						     =			torch.Size([1, 1_408, 1_536]   )
              a_						     =			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":
              a_						     =			torch.Size([1, 1_408, 1_536]   )
              a_						     =			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
              a_						     =			torch.tensor([0.5142]   ) if config.norm_pix_loss else torch.tensor([0.6469]   )
       elif model_name == "videomae-large":
              a_						     =			torch.Size([1, 1_408, 1_536]   )
              a_						     =			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":
              a_						     =			torch.Size([1, 400]   )
              a_						     =			torch.tensor([0.0771, 0.0011, -0.3625]   )
       elif model_name == "videomae-huge-finetuned-kinetics":
              a_						     =			torch.Size([1, 400]   )
              a_						     =			torch.tensor([0.2433, 0.1632, -0.4894]   )
       elif model_name == "videomae-base-short-finetuned-kinetics":
              a_						     =			torch.Size([1, 400]   )
              a_						     =			torch.tensor([0.6588, 0.0990, -0.2493]   )
       elif model_name == "videomae-base-finetuned-kinetics":
              a_						     =			torch.Size([1, 400]   )
              a_						     =			torch.tensor([0.3669, -0.0688, -0.2421]   )
       elif model_name == "videomae-base-short-ssv2":
              a_						     =			torch.Size([1, 1_408, 1_536]   )
              a_						     =			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":
              a_						     =			torch.Size([1, 174]   )
              a_						     =			torch.tensor([-0.0537, -0.1539, -0.3266]   )
       elif model_name == "videomae-base-ssv2":
              a_						     =			torch.Size([1, 1_408, 1_536]   )
              a_						     =			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":
              a_						     =			torch.Size([1, 174]   )
              a_						     =			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":
              a_						     =			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__":
  __lowerCAmelCase        =							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."
  )

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









"""simple docstring"""







import requests

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			List[str]						    = {"Content-Type": "application/json"}
						snake_case   :			int						    = requests.post(lowerCamelCase_     ,		json={"text": message_body}     ,		headers=lowerCamelCase_ )
						if response.status_code != 2_0_0:
												snake_case   :			str						    = (
												    "Request to slack returned an error "
												    f'''{response.status_code}, the response is:\n{response.text}'''
												)
												raise ValueError(lowerCamelCase_ )


if __name__ == "__main__":
			# Set the slack url to the one provided by Slack when you create the webhook at
			# https://my.slack.com/services/new/incoming-webhook/
			send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')



