




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_	:   Any     ):
     """simple docstring"""

     lowerCAmelCase__            =					VideoMAEConfig()

     set_architecture_configs(lowerCAmelCase_  ,							lowerCAmelCase_     )

     if "finetuned" not in model_name:
          lowerCAmelCase__            =					False

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

     return config



def 				_A  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   int     ):
     """simple docstring"""

     if "small" in model_name:
          lowerCAmelCase__            =					384
          lowerCAmelCase__            =					1536
          lowerCAmelCase__            =					12
          lowerCAmelCase__            =					16
          lowerCAmelCase__            =					12
          lowerCAmelCase__            =					3
          lowerCAmelCase__            =					192
          lowerCAmelCase__            =					768
     elif "large" in model_name:
          lowerCAmelCase__            =					1024
          lowerCAmelCase__            =					4096
          lowerCAmelCase__            =					24
          lowerCAmelCase__            =					16
          lowerCAmelCase__            =					12
          lowerCAmelCase__            =					8
          lowerCAmelCase__            =					512
          lowerCAmelCase__            =					2048
     elif "huge" in model_name:
          lowerCAmelCase__            =					1280
          lowerCAmelCase__            =					5120
          lowerCAmelCase__            =					32
          lowerCAmelCase__            =					16
          lowerCAmelCase__            =					12
          lowerCAmelCase__            =					8
          lowerCAmelCase__            =					640
          lowerCAmelCase__            =					2560
     elif "base" not in model_name:
          raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""     )



def 				_A  (		lowerCAmelCase_	:   Union[str, Any]     ):
     """simple docstring"""

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

     return name



def 				_A  (		lowerCAmelCase_	:   Dict  ,							lowerCAmelCase_	:   Dict     ):
     """simple docstring"""

     for key in orig_state_dict.copy().keys():
          lowerCAmelCase__            =					orig_state_dict.pop(lowerCAmelCase_     )

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

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

     return orig_state_dict



def 				_A  (		):
     """simple docstring"""

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



def 				_A  (		lowerCAmelCase_	:   List[str]  ,							lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   Dict  ,							lowerCAmelCase_	:   str     ):
     """simple docstring"""

     lowerCAmelCase__            =					get_videomae_config(lowerCAmelCase_     )

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

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

     model.load_state_dict(lowerCAmelCase_     )
     model.eval()

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

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

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

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

     UpperCamelCase								=	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 Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	int = 1_6			,       __lowerCamelCase							:	int = 8_8			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "geglu"			,       __lowerCamelCase							:	Optional[int] = None			,       ):
    super().__init__()
    UpperCAmelCase__	:Optional[int]      =      nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=__lowerCamelCase			,       attention_head_dim=__lowerCamelCase			,       in_channels=__lowerCamelCase			,       num_layers=__lowerCamelCase			,       dropout=__lowerCamelCase			,       norm_num_groups=__lowerCamelCase			,       cross_attention_dim=__lowerCamelCase			,       attention_bias=__lowerCamelCase			,       sample_size=__lowerCamelCase			,       num_vector_embeds=__lowerCamelCase			,       activation_fn=__lowerCamelCase			,       num_embeds_ada_norm=__lowerCamelCase			,       )
            for _ in range(2 )
        ] )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase__	:Tuple      =      0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase__	:int      =      [7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase__	:Any      =      [1, 0]





 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:Optional[int]      =      hidden_states

    UpperCAmelCase__	:Dict      =      []
    UpperCAmelCase__	:Tuple      =      0
    # attention_mask is not used yet
    for i in range(2 ):
       # for each of the two transformers, pass the corresponding condition tokens
       UpperCAmelCase__	:List[Any]      =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       UpperCAmelCase__	:Dict      =      self.transformer_index_for_condition[i]
       UpperCAmelCase__	:Tuple      =      self.transformers[transformer_index](
           __lowerCamelCase			,       encoder_hidden_states=__lowerCamelCase			,       timestep=__lowerCamelCase			,       cross_attention_kwargs=__lowerCamelCase			,       return_dict=__lowerCamelCase			,       )[0]
       encoded_states.append(encoded_state - input_states )
       tokens_start += self.condition_lengths[i]

    UpperCAmelCase__	:Optional[int]      =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase__	:Optional[Any]      =      output_states + input_states

    if not return_dict:
       return (output_states,)

    return TransformeraDModelOutput(sample=__lowerCamelCase )



