





'''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  )		->				Tuple:
 UpperCamelCase			:      List[Any] 			=     VideoMAEConfig()

 set_architecture_configs(_lowerCAmelCase							,     _lowerCAmelCase  )

 if "finetuned" not in model_name:
  UpperCamelCase			:      int 			=     False

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

 return config



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				Dict:
 if "small" in model_name:
  UpperCamelCase			:      Optional[int] 			=     384
  UpperCamelCase			:      Tuple 			=     1536
  UpperCamelCase			:      Optional[int] 			=     12
  UpperCamelCase			:      Dict 			=     16
  UpperCamelCase			:      List[Any] 			=     12
  UpperCamelCase			:      Optional[Any] 			=     3
  UpperCamelCase			:      List[Any] 			=     192
  UpperCamelCase			:      int 			=     768
 elif "large" in model_name:
  UpperCamelCase			:      List[Any] 			=     1024
  UpperCamelCase			:      List[str] 			=     4096
  UpperCamelCase			:      Optional[int] 			=     24
  UpperCamelCase			:      Optional[int] 			=     16
  UpperCamelCase			:      Dict 			=     12
  UpperCamelCase			:      Dict 			=     8
  UpperCamelCase			:      Dict 			=     512
  UpperCamelCase			:      Union[str, Any] 			=     2048
 elif "huge" in model_name:
  UpperCamelCase			:      List[Any] 			=     1280
  UpperCamelCase			:      Optional[Any] 			=     5120
  UpperCamelCase			:      Dict 			=     32
  UpperCamelCase			:      List[Any] 			=     16
  UpperCamelCase			:      int 			=     12
  UpperCamelCase			:      Tuple 			=     8
  UpperCamelCase			:      Tuple 			=     640
  UpperCamelCase			:      List[Any] 			=     2560
 elif "base" not in model_name:
  raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""  )



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

 return name



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				List[Any]:
 for key in orig_state_dict.copy().keys():
  UpperCamelCase			:      Any 			=     orig_state_dict.pop(_lowerCAmelCase  )

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

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

 return orig_state_dict



def 	A_       (  )		->				Tuple:
 UpperCamelCase			:      Optional[int] 			=     hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video"							,     filename="eating_spaghetti.npy"							,     repo_type="dataset"  )
 UpperCamelCase			:      int 			=     np.load(_lowerCAmelCase  )
 return list(_lowerCAmelCase  )



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      int 			=     get_videomae_config(_lowerCAmelCase  )

 if "finetuned" in model_name:
  UpperCamelCase			:      Tuple 			=     VideoMAEForVideoClassification(_lowerCAmelCase  )
 else:
  UpperCamelCase			:      str 			=     VideoMAEForPreTraining(_lowerCAmelCase  )

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

 model.load_state_dict(_lowerCAmelCase  )
 model.eval()

 # verify model on basic input
 UpperCamelCase			:      Tuple 			=     VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,     image_std=[0.5, 0.5, 0.5]  )
 UpperCamelCase			:      str 			=     prepare_video()
 UpperCamelCase			:      Dict 			=     image_processor(_lowerCAmelCase							,     return_tensors="pt"  )

 if "finetuned" not in model_name:
  UpperCamelCase			:      Optional[Any] 			=     hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"							,     filename="bool_masked_pos.pt"  )
  UpperCamelCase			:      Union[str, Any] 			=     torch.load(_lowerCAmelCase  )

 UpperCamelCase			:      str 			=     model(**_lowerCAmelCase  )
 UpperCamelCase			:      Dict 			=     outputs.logits

 UpperCamelCase			:      List[str] 			=     [
     "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			:      int 			=     torch.Size([1, 400]  )
  UpperCamelCase			:      int 			=     torch.tensor([-0.9_291, -0.4_061, -0.9_307]  )
 elif model_name == "videomae-small-finetuned-ssv2":
  UpperCamelCase			:      Dict 			=     torch.Size([1, 174]  )
  UpperCamelCase			:      Dict 			=     torch.tensor([0.2_671, -0.4_689, -0.8_235]  )
 elif model_name == "videomae-base":
  UpperCamelCase			:      List[str] 			=     torch.Size([1, 1408, 1536]  )
  UpperCamelCase			:      Union[str, Any] 			=     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":
  UpperCamelCase			:      Optional[Any] 			=     torch.Size([1, 1408, 1536]  )
  UpperCamelCase			:      Tuple 			=     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
  UpperCamelCase			:      Optional[int] 			=     torch.tensor([0.5_142]  ) if config.norm_pix_loss else torch.tensor([0.6_469]  )
 elif model_name == "videomae-large":
  UpperCamelCase			:      int 			=     torch.Size([1, 1408, 1536]  )
  UpperCamelCase			:      int 			=     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":
  UpperCamelCase			:      Any 			=     torch.Size([1, 400]  )
  UpperCamelCase			:      List[Any] 			=     torch.tensor([0.0_771, 0.0_011, -0.3_625]  )
 elif model_name == "videomae-huge-finetuned-kinetics":
  UpperCamelCase			:      Union[str, Any] 			=     torch.Size([1, 400]  )
  UpperCamelCase			:      Optional[int] 			=     torch.tensor([0.2_433, 0.1_632, -0.4_894]  )
 elif model_name == "videomae-base-short-finetuned-kinetics":
  UpperCamelCase			:      List[str] 			=     torch.Size([1, 400]  )
  UpperCamelCase			:      Tuple 			=     torch.tensor([0.6_588, 0.0_990, -0.2_493]  )
 elif model_name == "videomae-base-finetuned-kinetics":
  UpperCamelCase			:      str 			=     torch.Size([1, 400]  )
  UpperCamelCase			:      Optional[Any] 			=     torch.tensor([0.3_669, -0.0_688, -0.2_421]  )
 elif model_name == "videomae-base-short-ssv2":
  UpperCamelCase			:      int 			=     torch.Size([1, 1408, 1536]  )
  UpperCamelCase			:      str 			=     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":
  UpperCamelCase			:      Optional[Any] 			=     torch.Size([1, 174]  )
  UpperCamelCase			:      Dict 			=     torch.tensor([-0.0_537, -0.1_539, -0.3_266]  )
 elif model_name == "videomae-base-ssv2":
  UpperCamelCase			:      Optional[int] 			=     torch.Size([1, 1408, 1536]  )
  UpperCamelCase			:      Dict 			=     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":
  UpperCamelCase			:      Optional[Any] 			=     torch.Size([1, 174]  )
  UpperCamelCase			:      Any 			=     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]							,     _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			:      Optional[int] 			=     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 :  Any										=  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 :  List[Any]										=  parser.parse_args()
    convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     ['image_processor', 'tokenizer']
      _UpperCAmelCase			:Tuple							=     'BlipImageProcessor'
      _UpperCAmelCase			:Optional[int]							=     'AutoTokenizer'





      def __init__(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     False
       super().__init__(A_ ,							A_     )
       UpperCamelCase			:      str 			=     self.image_processor





      def __call__(				self ,							A_ = None ,							A_ = None ,							A_ = True ,							A_ = False ,							A_ = None ,							A_ = None ,							A_ = 0 ,							A_ = None ,							A_ = None ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = True ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       if images is None and text is None:
        raise ValueError("You have to specify either images or text."     )

       # Get only text
       if images is None:
        UpperCamelCase			:      int 			=     self.tokenizer
        UpperCamelCase			:      Optional[int] 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
        return text_encoding

       # add pixel_values
       UpperCamelCase			:      int 			=     self.image_processor(A_ ,							return_tensors=A_     )

       if text is not None:
        UpperCamelCase			:      Dict 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
       else:
        UpperCamelCase			:      Dict 			=     None

       if text_encoding is not None:
        encoding_image_processor.update(A_     )

       return encoding_image_processor





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




       return self.tokenizer.batch_decode(*A_ ,							**A_     )





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




       return self.tokenizer.decode(*A_ ,							**A_     )





      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.tokenizer.model_input_names
       UpperCamelCase			:      int 			=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
