






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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any     )    ->   List[str]:





    """simple docstring"""

    __A       =						VideoMAEConfig()

    set_architecture_configs(__lowercase      ,					__lowercase     )

    if "finetuned" not in model_name:
        __A       =						False

    if "finetuned" in model_name:
        __A       =						"""huggingface/label-files"""
        if "kinetics" in model_name:
            __A       =						4_0_0
            __A       =						"""kinetics400-id2label.json"""
        elif "ssv2" in model_name:
            __A       =						1_7_4
            __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(__lowercase      ,					__lowercase      ,					repo_type="""dataset"""     )      ,					"""r"""     )     )
        __A       =						{int(__lowercase     ): v for k, v in idalabel.items()}
        __A       =						idalabel
        __A       =						{v: k for k, v in idalabel.items()}

    return config



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]      ,					__lowercase :      Tuple     )    ->   Any:





    """simple docstring"""

    if "small" in model_name:
        __A       =						3_8_4
        __A       =						1_5_3_6
        __A       =						1_2
        __A       =						1_6
        __A       =						1_2
        __A       =						3
        __A       =						1_9_2
        __A       =						7_6_8
    elif "large" in model_name:
        __A       =						1_0_2_4
        __A       =						4_0_9_6
        __A       =						2_4
        __A       =						1_6
        __A       =						1_2
        __A       =						8
        __A       =						5_1_2
        __A       =						2_0_4_8
    elif "huge" in model_name:
        __A       =						1_2_8_0
        __A       =						5_1_2_0
        __A       =						3_2
        __A       =						1_6
        __A       =						1_2
        __A       =						8
        __A       =						6_4_0
        __A       =						2_5_6_0
    elif "base" not in model_name:
        raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[int]     )    ->   Any:





    """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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Dict      ,					__lowercase :      Union[str, Any]     )    ->   Union[str, Any]:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        __A       =						orig_state_dict.pop(__lowercase     )

        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       _SCREAMING_SNAKE_CASE    (			)    ->   List[Any]:





    """simple docstring"""

    __A       =						hf_hub_download(
        repo_id="""hf-internal-testing/spaghetti-video"""      ,					filename="""eating_spaghetti.npy"""      ,					repo_type="""dataset"""     )
    __A       =						np.load(__lowercase     )
    return list(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]      ,					__lowercase :      Tuple      ,					__lowercase :      str      ,					__lowercase :      int     )    ->   Optional[int]:





    """simple docstring"""

    __A       =						get_videomae_config(__lowercase     )

    if "finetuned" in model_name:
        __A       =						VideoMAEForVideoClassification(__lowercase     )
    else:
        __A       =						VideoMAEForPreTraining(__lowercase     )

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

    model.load_state_dict(__lowercase     )
    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(__lowercase      ,					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(__lowercase     )

    __A       =						model(**__lowercase     )
    __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, 4_0_0]     )
        __A       =						torch.tensor([-0.9_291, -0.4_061, -0.9_307]     )
    elif model_name == "videomae-small-finetuned-ssv2":
        __A       =						torch.Size([1, 1_7_4]     )
        __A       =						torch.tensor([0.2_671, -0.4_689, -0.8_235]     )
    elif model_name == "videomae-base":
        __A       =						torch.Size([1, 1_4_0_8, 1_5_3_6]     )
        __A       =						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       =						torch.Size([1, 1_4_0_8, 1_5_3_6]     )
        __A       =						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       =						torch.tensor([0.5_142]     ) if config.norm_pix_loss else torch.tensor([0.6_469]     )
    elif model_name == "videomae-large":
        __A       =						torch.Size([1, 1_4_0_8, 1_5_3_6]     )
        __A       =						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       =						torch.Size([1, 4_0_0]     )
        __A       =						torch.tensor([0.0_771, 0.0_011, -0.3_625]     )
    elif model_name == "videomae-huge-finetuned-kinetics":
        __A       =						torch.Size([1, 4_0_0]     )
        __A       =						torch.tensor([0.2_433, 0.1_632, -0.4_894]     )
    elif model_name == "videomae-base-short-finetuned-kinetics":
        __A       =						torch.Size([1, 4_0_0]     )
        __A       =						torch.tensor([0.6_588, 0.0_990, -0.2_493]     )
    elif model_name == "videomae-base-finetuned-kinetics":
        __A       =						torch.Size([1, 4_0_0]     )
        __A       =						torch.tensor([0.3_669, -0.0_688, -0.2_421]     )
    elif model_name == "videomae-base-short-ssv2":
        __A       =						torch.Size([1, 1_4_0_8, 1_5_3_6]     )
        __A       =						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       =						torch.Size([1, 1_7_4]     )
        __A       =						torch.tensor([-0.0_537, -0.1_539, -0.3_266]     )
    elif model_name == "videomae-base-ssv2":
        __A       =						torch.Size([1, 1_4_0_8, 1_5_3_6]     )
        __A       =						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       =						torch.Size([1, 1_7_4]     )
        __A       =						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]      ,					__lowercase      ,					atol=1E-4     )
    else:
        print("""Logits:"""      ,					logits[0, :3, :3]     )
        assert torch.allclose(logits[0, :3, :3]      ,					__lowercase      ,					atol=1E-4     )
    print("""Logits ok!"""     )

    # verify loss, if applicable
    if model_name == "videomae-base-short":
        __A       =						outputs.loss
        assert torch.allclose(__lowercase      ,					__lowercase      ,					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(__lowercase     )
        model.save_pretrained(__lowercase     )

    if push_to_hub:
        print("""Pushing to the hub..."""     )
        model.push_to_hub(__lowercase      ,					organization="""nielsr"""     )


if __name__ == "__main__":
  __a   :							Dict						= 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   :							Tuple						= parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)












from __future__ import annotations

from typing import Generic, TypeVar

__a   :							str						= TypeVar("T")


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Any			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						data
         __A       =						self
         __A       =						0


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						{}




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						DisjointSetTreeNode(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						self.map[data]
         if elem_ref != elem_ref.parent:
             __A       =						self.find_set(elem_ref.parent.data					)
         return elem_ref.parent




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      DisjointSetTreeNode[T]			,						UpperCamelCase_							:      DisjointSetTreeNode[T]					):


         """simple docstring"""



         if nodea.rank > nodea.rank:
             __A       =						nodea
         else:
             __A       =						nodea
             if nodea.rank == nodea.rank:
                 nodea.rank += 1





     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         self.link(self.find_set(UpperCamelCase_					)			,						self.find_set(UpperCamelCase_					)					)


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						{}




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         if node not in self.connections:
             __A       =						{}




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         self.add_node(UpperCamelCase_					)
         self.add_node(UpperCamelCase_					)
         __A       =						weight
         __A       =						weight





     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						[]
         __A       =						set()
         for start in self.connections:
             for end in self.connections[start]:
                 if (start, end) not in seen:
                     seen.add((end, start)					)
                     edges.append((start, end, self.connections[start][end])					)
         edges.sort(key=lambda UpperCamelCase_					: x[2]					)

         # creating the disjoint set
         __A       =						DisjointSetTree[T]()
         for node in self.connections:
             disjoint_set.make_set(UpperCamelCase_					)

         # MST generation
         __A       =						0
         __A       =						0
         __A       =						GraphUndirectedWeighted[T]()
         while num_edges < len(self.connections					) - 1:
             __A     ,						__A     ,						__A       =						edges[index]
             index += 1
             __A       =						disjoint_set.find_set(UpperCamelCase_					)
             __A       =						disjoint_set.find_set(UpperCamelCase_					)
             if parent_u != parent_v:
                 num_edges += 1
                 graph.add_edge(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
                 disjoint_set.union(UpperCamelCase_			,						UpperCamelCase_					)
         return graph




