





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 _A   (    __magic_name__       ):
   lowercase__    =  "https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

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


def _A   (    ):
   lowercase__    =  VivitConfig()

   lowercase__    =  400
   lowercase__    =  "huggingface/label-files"
   lowercase__    =  "kinetics400-id2label.json"

   lowercase__    =  json.load(open(hf_hub_download(__magic_name__  ,					__magic_name__  ,					repo_type="dataset"       )  ,					"r"       )       )
   lowercase__    =  {int(__magic_name__       ): v for k, v in idalabel.items()}
   lowercase__    =  idalabel
   lowercase__    =  {v: k for k, v in idalabel.items()}
   return config


def _A   (    ):
   lowercase__    =  hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video"  ,					filename="eating_spaghetti_32_frames.npy"  ,					repo_type="dataset"       )
   lowercase__    =  np.load(__magic_name__       )
   return list(__magic_name__       )


def _A   (    __magic_name__       ):
   if np.ndim(__magic_name__       ) == 2:
      return transform_attention_bias(__magic_name__       )

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

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


def _A   (    __magic_name__       ):
   return current.flatten()


def _A   (    __magic_name__       ):
   return np.reshape(__magic_name__  ,					(current.shape[0], current.shape[1] * current.shape[2])       ).T


def _A   (    __magic_name__       ):
   return np.reshape(__magic_name__  ,					(current.shape[0] * current.shape[1], current.shape[2])       ).T


def _A   (    __magic_name__  ,					__magic_name__       ):
   lowercase__    =  state_dict["optimizer"]["target"]["Transformer"][f'''encoderblock_{i}''']

   lowercase__    =  f'''encoder.layer.{i}.'''
   lowercase__    =  {
       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 _A   (    __magic_name__       ):
   return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]       )


def _A   (    __magic_name__  ,					__magic_name__=False       ):
   lowercase__    =  get_n_layers(__magic_name__       )

   lowercase__    =  OrderedDict()

   lowercase__    =  state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
   lowercase__    =  state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

   lowercase__    =  np.transpose(
       state_dict["optimizer"]["target"]["embedding"]["kernel"]  ,					(4, 3, 0, 1, 2)       )
   lowercase__    =  state_dict["optimizer"]["target"]["embedding"]["bias"]

   lowercase__    =  state_dict["optimizer"]["target"]["cls"]
   lowercase__    =  state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
       "pos_embedding"
   ]

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

   if classification_head:
      lowercase__    =  {"vivit." + k: v for k, v in new_state.items()}
      lowercase__    =  np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]       )
      lowercase__    =  np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]       )

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


def _A   (    ):
   lowercase__    =  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 _A   (    __magic_name__       ):
   lowercase__    =  "checkpoint"

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

   lowercase__    =  restore_checkpoint(__magic_name__  ,					__magic_name__       )
   lowercase__    =  transform_state(__magic_name__  ,					classification_head=__magic_name__       )

   lowercase__    =  get_vivit_config()

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

   lowercase__    =  VivitForVideoClassification(__magic_name__       )
   model.load_state_dict(__magic_name__       )
   model.eval()

   lowercase__    =  get_processor()

   lowercase__    =  prepare_video()
   lowercase__    =  extractor(__magic_name__  ,					return_tensors="pt"       )

   lowercase__    =  model(**__magic_name__       )

   lowercase__    =  torch.Size([1, 400]       )
   lowercase__    =  torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]       )

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

   model.save_pretrained(__magic_name__       )
   extractor.save_pretrained(__magic_name__       )


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

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

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case				= {"""configuration_bit""": ["""BIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BitConfig""", """BitOnnxConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """BIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BitForImageClassification""",
      """BitModel""",
      """BitPreTrainedModel""",
      """BitBackbone""",
  ]


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""BitImageProcessor"""]


if TYPE_CHECKING:
  from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bit import (
        BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BitBackbone,
        BitForImageClassification,
        BitModel,
        BitPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bit import BitImageProcessor

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






