





import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling







def       lowerCAmelCase__(	lowercase       :    Dict				) ->    Tuple:
  __snake_case   :				Optional[int]        =       "https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

  with open(lowercase   ,     "wb"				) as f:
    with requests.get(lowercase   ,     stream=lowercase				) as req:
      for chunk in req.iter_content(chunk_size=2048				):
        f.write(lowercase				)







def       lowerCAmelCase__(	) ->    VivitConfig:
  __snake_case   :				int        =       VivitConfig()

  __snake_case   :				int        =       400
  __snake_case   :				Tuple        =       "huggingface/label-files"
  __snake_case   :				List[str]        =       "kinetics400-id2label.json"

  __snake_case   :				Any        =       json.load(open(hf_hub_download(lowercase   ,     lowercase   ,     repo_type="dataset"				)   ,     "r"				)				)
  __snake_case   :				Optional[Any]        =       {int(lowercase				): v for k, v in idalabel.items()}
  __snake_case   :				List[Any]        =       idalabel
  __snake_case   :				str        =       {v: k for k, v in idalabel.items()}
  return config







def       lowerCAmelCase__(	) ->    Union[str, Any]:
  __snake_case   :				Union[str, Any]        =       hf_hub_download(
      repo_id="hf-internal-testing/spaghetti-video"   ,     filename="eating_spaghetti_32_frames.npy"   ,     repo_type="dataset"				)
  __snake_case   :				str        =       np.load(lowercase				)
  return list(lowercase				)







def       lowerCAmelCase__(	lowercase       :    np.ndarray				) ->    Any:
  if np.ndim(lowercase				) == 2:
    return transform_attention_bias(lowercase				)

  elif np.ndim(lowercase				) == 3:
    return transform_attention_kernel(lowercase				)

  else:
    raise Exception(f"""Invalid number of dimesions: {np.ndim(lowercase				)}"""				)







def       lowerCAmelCase__(	lowercase       :    np.ndarray				) ->    Optional[Any]:
  return current.flatten()







def       lowerCAmelCase__(	lowercase       :    np.ndarray				) ->    Any:
  return np.reshape(lowercase   ,     (current.shape[0], current.shape[1] * current.shape[2])				).T







def       lowerCAmelCase__(	lowercase       :    np.ndarray				) ->    str:
  return np.reshape(lowercase   ,     (current.shape[0] * current.shape[1], current.shape[2])				).T







def       lowerCAmelCase__(	lowercase       :    int   ,     lowercase       :    List[str]				) ->    Optional[Any]:
  __snake_case   :				Dict        =       state_dict["optimizer"]["target"]["Transformer"][f"""encoderblock_{i}"""]

  __snake_case   :				Optional[int]        =       f"""encoder.layer.{i}."""
  __snake_case   :				str        =       {
      prefix + "intermediate.dense.bias": state["MlpBlock_0"]["Dense_0"]["bias"],
      prefix + "intermediate.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_0"]["kernel"]				),
      prefix + "output.dense.bias": state["MlpBlock_0"]["Dense_1"]["bias"],
      prefix + "output.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_1"]["kernel"]				),
      prefix + "layernorm_before.bias": state["LayerNorm_0"]["bias"],
      prefix + "layernorm_before.weight": state["LayerNorm_0"]["scale"],
      prefix + "layernorm_after.bias": state["LayerNorm_1"]["bias"],
      prefix + "layernorm_after.weight": state["LayerNorm_1"]["scale"],
      prefix
      + "attention.attention.query.bias": transform_attention(
          state["MultiHeadDotProductAttention_0"]["query"]["bias"]				),
      prefix
      + "attention.attention.query.weight": transform_attention(
          state["MultiHeadDotProductAttention_0"]["query"]["kernel"]				),
      prefix
      + "attention.attention.key.bias": transform_attention(state["MultiHeadDotProductAttention_0"]["key"]["bias"]				),
      prefix
      + "attention.attention.key.weight": transform_attention(
          state["MultiHeadDotProductAttention_0"]["key"]["kernel"]				),
      prefix
      + "attention.attention.value.bias": transform_attention(
          state["MultiHeadDotProductAttention_0"]["value"]["bias"]				),
      prefix
      + "attention.attention.value.weight": transform_attention(
          state["MultiHeadDotProductAttention_0"]["value"]["kernel"]				),
      prefix + "attention.output.dense.bias": state["MultiHeadDotProductAttention_0"]["out"]["bias"],
      prefix
      + "attention.output.dense.weight": transform_attention_output_weight(
          state["MultiHeadDotProductAttention_0"]["out"]["kernel"]				),
  }

  return new_state







def       lowerCAmelCase__(	lowercase       :    List[str]				) ->    str:
  return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]				)







def       lowerCAmelCase__(	lowercase       :    Tuple   ,     lowercase       :    Union[str, Any]=False				) ->    int:
  __snake_case   :				Union[str, Any]        =       get_n_layers(lowercase				)

  __snake_case   :				Optional[Any]        =       OrderedDict()

  __snake_case   :				Union[str, Any]        =       state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
  __snake_case   :				Dict        =       state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

  __snake_case   :				Optional[int]        =       np.transpose(
      state_dict["optimizer"]["target"]["embedding"]["kernel"]   ,     (4, 3, 0, 1, 2)				)
  __snake_case   :				Dict        =       state_dict["optimizer"]["target"]["embedding"]["bias"]

  __snake_case   :				List[Any]        =       state_dict["optimizer"]["target"]["cls"]
  __snake_case   :				Optional[int]        =       state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
      "pos_embedding"
  ]

  for i in range(lowercase				):
    new_state.update(transform_state_encoder_block(lowercase   ,     lowercase				)				)

  if classification_head:
    __snake_case   :				Optional[int]        =       {"vivit." + k: v for k, v in new_state.items()}
    __snake_case   :				List[str]        =       np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]				)
    __snake_case   :				Union[str, Any]        =       np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]				)

  return {k: torch.tensor(lowercase				) for k, v in new_state.items()}







def       lowerCAmelCase__(	) ->    VivitImageProcessor:
  __snake_case   :				Any        =       VivitImageProcessor()

  assert extractor.do_resize is True
  assert extractor.size == {"shortest_edge": 256}
  assert extractor.do_center_crop is True
  assert extractor.crop_size == {"width": 224, "height": 224}
  assert extractor.resample == PILImageResampling.BILINEAR

  # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
  # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
  # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
  assert extractor.do_normalize is False
  assert extractor.do_rescale is True
  assert extractor.rescale_factor == 1 / 255

  # zero-centering = True in original implementation
  assert extractor.do_zero_centering is True

  return extractor







def       lowerCAmelCase__(	lowercase       :    str				) ->    str:
  __snake_case   :				Optional[Any]        =       "checkpoint"

  if not os.path.exists(lowercase				):
    download_checkpoint(lowercase				)

  __snake_case   :				List[str]        =       restore_checkpoint(lowercase   ,     lowercase				)
  __snake_case   :				List[Any]        =       transform_state(lowercase   ,     classification_head=lowercase				)

  __snake_case   :				str        =       get_vivit_config()

  assert config.image_size == 224
  assert config.num_frames == 32

  __snake_case   :				str        =       VivitForVideoClassification(lowercase				)
  model.load_state_dict(lowercase				)
  model.eval()

  __snake_case   :				Union[str, Any]        =       get_processor()

  __snake_case   :				Optional[int]        =       prepare_video()
  __snake_case   :				List[Any]        =       extractor(lowercase   ,     return_tensors="pt"				)

  __snake_case   :				Tuple        =       model(**lowercase				)

  __snake_case   :				Dict        =       torch.Size([1, 400]				)
  __snake_case   :				Any        =       torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]				)

  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, :5]   ,     lowercase   ,     atol=1E-4				), outputs.logits[0, :5]

  model.save_pretrained(lowercase				)
  extractor.save_pretrained(lowercase				)


if __name__ == "__main__":
     _UpperCamelCase       = argparse.ArgumentParser()

     parser.add_argument('''--output_model_name''', '''-o''', type=str, help='''Output path for the converted HuggingFace model''')

     _UpperCamelCase       = parser.parse_args()
     convert(args.output_model_name)








def       lowerCAmelCase__(	lowercase       :    str				) ->    int:
  __snake_case   :				List[str]        =       str(lowercase				).strip()
  if not oct_string:
    raise ValueError("Empty string was passed to the function"				)
  __snake_case   :				int        =       oct_string[0] == "-"
  if is_negative:
    __snake_case   :				Union[str, Any]        =       oct_string[1:]
  if not oct_string.isdigit() or not all(0 <= int(lowercase				) <= 7 for char in oct_string				):
    raise ValueError("Non-octal value was passed to the function"				)
  __snake_case   :				List[Any]        =       0
  for char in oct_string:
    __snake_case   :				Optional[Any]        =       8 * decimal_number + int(lowercase				)
  if is_negative:
    __snake_case   :				Any        =       -decimal_number
  return decimal_number


if __name__ == "__main__":
     from doctest import testmod

     testmod()

