



"""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__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							VideoMAEConfig()

      set_architecture_configs(lowerCAmelCase__			,    lowerCAmelCase__   )

      if "finetuned" not in model_name:
            UpperCAmelCase_    				=							False

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

      return config
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      if "small" in model_name:
            UpperCAmelCase_    				=							384
            UpperCAmelCase_    				=							1536
            UpperCAmelCase_    				=							12
            UpperCAmelCase_    				=							16
            UpperCAmelCase_    				=							12
            UpperCAmelCase_    				=							3
            UpperCAmelCase_    				=							192
            UpperCAmelCase_    				=							768
      elif "large" in model_name:
            UpperCAmelCase_    				=							1024
            UpperCAmelCase_    				=							4096
            UpperCAmelCase_    				=							24
            UpperCAmelCase_    				=							16
            UpperCAmelCase_    				=							12
            UpperCAmelCase_    				=							8
            UpperCAmelCase_    				=							512
            UpperCAmelCase_    				=							2048
      elif "huge" in model_name:
            UpperCAmelCase_    				=							1280
            UpperCAmelCase_    				=							5120
            UpperCAmelCase_    				=							32
            UpperCAmelCase_    				=							16
            UpperCAmelCase_    				=							12
            UpperCAmelCase_    				=							8
            UpperCAmelCase_    				=							640
            UpperCAmelCase_    				=							2560
      elif "base" not in model_name:
            raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""   )
def      a__			(   lowerCAmelCase__   ):
      if "encoder." in name:
            UpperCAmelCase_    				=							name.replace("encoder."			,    ""   )
      if "cls_token" in name:
            UpperCAmelCase_    				=							name.replace("cls_token"			,    "videomae.embeddings.cls_token"   )
      if "decoder_pos_embed" in name:
            UpperCAmelCase_    				=							name.replace("decoder_pos_embed"			,    "decoder.decoder_pos_embed"   )
      if "pos_embed" in name and "decoder" not in name:
            UpperCAmelCase_    				=							name.replace("pos_embed"			,    "videomae.embeddings.position_embeddings"   )
      if "patch_embed.proj" in name:
            UpperCAmelCase_    				=							name.replace("patch_embed.proj"			,    "videomae.embeddings.patch_embeddings.projection"   )
      if "patch_embed.norm" in name:
            UpperCAmelCase_    				=							name.replace("patch_embed.norm"			,    "videomae.embeddings.norm"   )
      if "decoder.blocks" in name:
            UpperCAmelCase_    				=							name.replace("decoder.blocks"			,    "decoder.decoder_layers"   )
      if "blocks" in name:
            UpperCAmelCase_    				=							name.replace("blocks"			,    "videomae.encoder.layer"   )
      if "attn.proj" in name:
            UpperCAmelCase_    				=							name.replace("attn.proj"			,    "attention.output.dense"   )
      if "attn" in name and "bias" not in name:
            UpperCAmelCase_    				=							name.replace("attn"			,    "attention.self"   )
      if "attn" in name:
            UpperCAmelCase_    				=							name.replace("attn"			,    "attention.attention"   )
      if "norm1" in name:
            UpperCAmelCase_    				=							name.replace("norm1"			,    "layernorm_before"   )
      if "norm2" in name:
            UpperCAmelCase_    				=							name.replace("norm2"			,    "layernorm_after"   )
      if "mlp.fc1" in name:
            UpperCAmelCase_    				=							name.replace("mlp.fc1"			,    "intermediate.dense"   )
      if "mlp.fc2" in name:
            UpperCAmelCase_    				=							name.replace("mlp.fc2"			,    "output.dense"   )
      if "decoder_embed" in name:
            UpperCAmelCase_    				=							name.replace("decoder_embed"			,    "decoder.decoder_embed"   )
      if "decoder_norm" in name:
            UpperCAmelCase_    				=							name.replace("decoder_norm"			,    "decoder.decoder_norm"   )
      if "decoder_pred" in name:
            UpperCAmelCase_    				=							name.replace("decoder_pred"			,    "decoder.decoder_pred"   )
      if "norm.weight" in name and "decoder" not in name and "fc" not in name:
            UpperCAmelCase_    				=							name.replace("norm.weight"			,    "videomae.layernorm.weight"   )
      if "norm.bias" in name and "decoder" not in name and "fc" not in name:
            UpperCAmelCase_    				=							name.replace("norm.bias"			,    "videomae.layernorm.bias"   )
      if "head" in name and "decoder" not in name:
            UpperCAmelCase_    				=							name.replace("head"			,    "classifier"   )

      return name
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_    				=							orig_state_dict.pop(lowerCAmelCase__   )

            if key.startswith("encoder."   ):
                  UpperCAmelCase_    				=							key.replace("encoder."			,    ""   )

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

      return orig_state_dict
def      a__			(   ):
      UpperCAmelCase_    				=							hf_hub_download(
          repo_id="hf-internal-testing/spaghetti-video"			,    filename="eating_spaghetti.npy"			,    repo_type="dataset"   )
      UpperCAmelCase_    				=							np.load(lowerCAmelCase__   )
      return list(lowerCAmelCase__   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							get_videomae_config(lowerCAmelCase__   )

      if "finetuned" in model_name:
            UpperCAmelCase_    				=							VideoMAEForVideoClassification(lowerCAmelCase__   )
      else:
            UpperCAmelCase_    				=							VideoMAEForPreTraining(lowerCAmelCase__   )

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

      model.load_state_dict(lowerCAmelCase__   )
      model.eval()

      # verify model on basic input
      UpperCAmelCase_    				=							VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,    image_std=[0.5, 0.5, 0.5]   )
      UpperCAmelCase_    				=							prepare_video()
      UpperCAmelCase_    				=							image_processor(lowerCAmelCase__			,    return_tensors="pt"   )

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

      UpperCAmelCase_    				=							model(**lowerCAmelCase__   )
      UpperCAmelCase_    				=							outputs.logits

      UpperCAmelCase_    				=							[
          "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":
            UpperCAmelCase_    				=							torch.Size([1, 400]   )
            UpperCAmelCase_    				=							torch.tensor([-0.9291, -0.4061, -0.9307]   )
      elif model_name == "videomae-small-finetuned-ssv2":
            UpperCAmelCase_    				=							torch.Size([1, 174]   )
            UpperCAmelCase_    				=							torch.tensor([0.2671, -0.4689, -0.8235]   )
      elif model_name == "videomae-base":
            UpperCAmelCase_    				=							torch.Size([1, 1408, 1536]   )
            UpperCAmelCase_    				=							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":
            UpperCAmelCase_    				=							torch.Size([1, 1408, 1536]   )
            UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							torch.tensor([0.5142]   ) if config.norm_pix_loss else torch.tensor([0.6469]   )
      elif model_name == "videomae-large":
            UpperCAmelCase_    				=							torch.Size([1, 1408, 1536]   )
            UpperCAmelCase_    				=							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":
            UpperCAmelCase_    				=							torch.Size([1, 400]   )
            UpperCAmelCase_    				=							torch.tensor([0.0771, 0.0011, -0.3625]   )
      elif model_name == "videomae-huge-finetuned-kinetics":
            UpperCAmelCase_    				=							torch.Size([1, 400]   )
            UpperCAmelCase_    				=							torch.tensor([0.2433, 0.1632, -0.4894]   )
      elif model_name == "videomae-base-short-finetuned-kinetics":
            UpperCAmelCase_    				=							torch.Size([1, 400]   )
            UpperCAmelCase_    				=							torch.tensor([0.6588, 0.0990, -0.2493]   )
      elif model_name == "videomae-base-finetuned-kinetics":
            UpperCAmelCase_    				=							torch.Size([1, 400]   )
            UpperCAmelCase_    				=							torch.tensor([0.3669, -0.0688, -0.2421]   )
      elif model_name == "videomae-base-short-ssv2":
            UpperCAmelCase_    				=							torch.Size([1, 1408, 1536]   )
            UpperCAmelCase_    				=							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":
            UpperCAmelCase_    				=							torch.Size([1, 174]   )
            UpperCAmelCase_    				=							torch.tensor([-0.0537, -0.1539, -0.3266]   )
      elif model_name == "videomae-base-ssv2":
            UpperCAmelCase_    				=							torch.Size([1, 1408, 1536]   )
            UpperCAmelCase_    				=							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":
            UpperCAmelCase_    				=							torch.Size([1, 174]   )
            UpperCAmelCase_    				=							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":
            UpperCAmelCase_    				=							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"""





from heapq import heappop, heappush

import numpy as np
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							grid.shape
      UpperCAmelCase_    				=							[-1, 1, 0, 0]
      UpperCAmelCase_    				=							[0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      UpperCAmelCase_				,					UpperCAmelCase_    				=							[(0, source)], set()
      UpperCAmelCase_    				=							np.full((rows, cols)			,    np.inf   )
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							np.empty((rows, cols)			,    dtype=lowerCAmelCase__   )
      UpperCAmelCase_    				=							None

      while queue:
            ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heappop(lowerCAmelCase__   )
            if (x, y) in visited:
                  continue
            visited.add((x, y)   )

            if (x, y) == destination:
                  UpperCAmelCase_    				=							[]
                  while (x, y) != source:
                        path.append((x, y)   )
                        UpperCAmelCase_				,					UpperCAmelCase_    				=							predecessors[x, y]
                  path.append(lowerCAmelCase__   )  # add the source manually
                  path.reverse()
                  return matrix[destination], path

            for i in range(len(lowerCAmelCase__   )   ):
                  UpperCAmelCase_				,					UpperCAmelCase_    				=							x + dx[i], y + dy[i]
                  if 0 <= nx < rows and 0 <= ny < cols:
                        UpperCAmelCase_    				=							grid[nx][ny]
                        if next_node == 1 and matrix[nx, ny] > dist + 1:
                              heappush(lowerCAmelCase__			,    (dist + 1, (nx, ny))   )
                              UpperCAmelCase_    				=							dist + 1
                              UpperCAmelCase_    				=							(x, y)

      return np.inf, []


if __name__ == "__main__":
  import doctest

  doctest.testmod()




