

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   UpperCamelCase(   lowercase_				)      ->							int:





  '''simple docstring'''





  snake_case_					= """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   UpperCamelCase(   )      ->							VivitConfig:





  '''simple docstring'''





  snake_case_					= VivitConfig()

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

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



def   UpperCamelCase(   )      ->							Optional[int]:





  '''simple docstring'''





  snake_case_					= hf_hub_download(
      repo_id="""hf-internal-testing/spaghetti-video"""     ,   filename="""eating_spaghetti_32_frames.npy"""     ,   repo_type="""dataset"""				)
  snake_case_					= np.load(lowercase_				)
  return list(lowercase_				)



def   UpperCamelCase(   lowercase_				)      ->							Dict:





  '''simple docstring'''





  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   UpperCamelCase(   lowercase_				)      ->							List[str]:





  '''simple docstring'''





  return current.flatten()



def   UpperCamelCase(   lowercase_				)      ->							Optional[int]:





  '''simple docstring'''





  return np.reshape(lowercase_     ,   (current.shape[0], current.shape[1] * current.shape[2])				).T



def   UpperCamelCase(   lowercase_				)      ->							List[Any]:





  '''simple docstring'''





  return np.reshape(lowercase_     ,   (current.shape[0] * current.shape[1], current.shape[2])				).T



def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							List[str]:





  '''simple docstring'''





  snake_case_					= state_dict["""optimizer"""]["""target"""]["""Transformer"""][f'''encoderblock_{i}''']

  snake_case_					= f'''encoder.layer.{i}.'''
  snake_case_					= {
      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   UpperCamelCase(   lowercase_				)      ->							Any:





  '''simple docstring'''





  return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]				)



def   UpperCamelCase(   lowercase_     ,   lowercase_=False				)      ->							Tuple:





  '''simple docstring'''





  snake_case_					= get_n_layers(lowercase_				)

  snake_case_					= OrderedDict()

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

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

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

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



def   UpperCamelCase(   )      ->							VivitImageProcessor:





  '''simple docstring'''





  snake_case_					= 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   UpperCamelCase(   lowercase_				)      ->							Optional[int]:





  '''simple docstring'''





  snake_case_					= """checkpoint"""

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

  snake_case_					= restore_checkpoint(lowercase_     ,   lowercase_				)
  snake_case_					= transform_state(lowercase_     ,   classification_head=lowercase_				)

  snake_case_					= get_vivit_config()

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

  snake_case_					= VivitForVideoClassification(lowercase_				)
  model.load_state_dict(lowercase_				)
  model.eval()

  snake_case_					= get_processor()

  snake_case_					= prepare_video()
  snake_case_					= extractor(lowercase_     ,   return_tensors="""pt"""				)

  snake_case_					= model(**lowercase_				)

  snake_case_					= torch.Size([1, 400]				)
  snake_case_					= torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]				)

  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__":
    lowerCamelCase_					  =   argparse.ArgumentParser()

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

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


def   UpperCamelCase(   lowercase_				)      ->							str:





  '''simple docstring'''





  return "".join(chr(ord(lowercase_				) + 32				) if """A""" <= char <= """Z""" else char for char in word				)


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

    testmod()