

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					(			SCREAMING_SNAKE_CASE						:     str						)       ->       Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:  str        = 'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

 with open(SCREAMING_SNAKE_CASE       ,   'wb'						) as f:
  with requests.get(SCREAMING_SNAKE_CASE       ,   stream=SCREAMING_SNAKE_CASE						) as req:
   for chunk in req.iter_content(chunk_size=20_48						):
    f.write(SCREAMING_SNAKE_CASE						)







def _a					(			)       ->       VivitConfig:

 """simple docstring"""






 __lowerCAmelCase:  Any        = VivitConfig()

 __lowerCAmelCase:  Any        = 4_00
 __lowerCAmelCase:  int        = 'huggingface/label-files'
 __lowerCAmelCase:  int        = 'kinetics400-id2label.json'

 __lowerCAmelCase:  Optional[int]        = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   repo_type='dataset'						)       ,   'r'						)						)
 __lowerCAmelCase:  Any        = {int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}
 __lowerCAmelCase:  Optional[Any]        = idalabel
 __lowerCAmelCase:  Union[str, Any]        = {v: k for k, v in idalabel.items()}
 return config







def _a					(			)       ->       Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:  Optional[int]        = hf_hub_download(
     repo_id='hf-internal-testing/spaghetti-video'       ,   filename='eating_spaghetti_32_frames.npy'       ,   repo_type='dataset'						)
 __lowerCAmelCase:  str        = np.load(SCREAMING_SNAKE_CASE						)
 return list(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray						)       ->       List[str]:

 """simple docstring"""






 if np.ndim(SCREAMING_SNAKE_CASE						) == 2:
  return transform_attention_bias(SCREAMING_SNAKE_CASE						)

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

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







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray						)       ->       List[str]:

 """simple docstring"""






 return current.flatten()







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray						)       ->       Any:

 """simple docstring"""






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







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray						)       ->       Any:

 """simple docstring"""






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







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     List[str]						)       ->       int:

 """simple docstring"""






 __lowerCAmelCase:  Optional[int]        = state_dict['optimizer']['target']['Transformer'][f'''encoderblock_{i}''']

 __lowerCAmelCase:  Optional[int]        = f'''encoder.layer.{i}.'''
 __lowerCAmelCase:  Any        = {
     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					(			SCREAMING_SNAKE_CASE						:     List[str]						)       ->       Optional[Any]:

 """simple docstring"""






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







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     List[str]=False						)       ->       Dict:

 """simple docstring"""






 __lowerCAmelCase:  Tuple        = get_n_layers(SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase:  List[Any]        = OrderedDict()

 __lowerCAmelCase:  Any        = state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
 __lowerCAmelCase:  Optional[int]        = state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

 __lowerCAmelCase:  Optional[int]        = np.transpose(
     state_dict['optimizer']['target']['embedding']['kernel']       ,   (4, 3, 0, 1, 2)						)
 __lowerCAmelCase:  List[str]        = state_dict['optimizer']['target']['embedding']['bias']

 __lowerCAmelCase:  Optional[int]        = state_dict['optimizer']['target']['cls']
 __lowerCAmelCase:  Optional[int]        = state_dict['optimizer']['target']['Transformer']['posembed_input'][
     'pos_embedding'
 ]

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

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

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







def _a					(			)       ->       VivitImageProcessor:

 """simple docstring"""






 __lowerCAmelCase:  Optional[Any]        = VivitImageProcessor()

 assert extractor.do_resize is True
 assert extractor.size == {"shortest_edge": 2_56}
 assert extractor.do_center_crop is True
 assert extractor.crop_size == {"width": 2_24, "height": 2_24}
 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 / 2_55

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

 return extractor







def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       int:

 """simple docstring"""






 __lowerCAmelCase:  str        = 'checkpoint'

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

 __lowerCAmelCase:  Optional[Any]        = restore_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  int        = transform_state(SCREAMING_SNAKE_CASE       ,   classification_head=SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase:  Dict        = get_vivit_config()

 assert config.image_size == 2_24
 assert config.num_frames == 32

 __lowerCAmelCase:  Tuple        = VivitForVideoClassification(SCREAMING_SNAKE_CASE						)
 model.load_state_dict(SCREAMING_SNAKE_CASE						)
 model.eval()

 __lowerCAmelCase:  Dict        = get_processor()

 __lowerCAmelCase:  Optional[int]        = prepare_video()
 __lowerCAmelCase:  Tuple        = extractor(SCREAMING_SNAKE_CASE       ,   return_tensors='pt'						)

 __lowerCAmelCase:  int        = model(**SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase:  Tuple        = torch.Size([1, 4_00]						)
 __lowerCAmelCase:  List[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]       ,   SCREAMING_SNAKE_CASE       ,   atol=1E-4						), outputs.logits[0, :5]

 model.save_pretrained(SCREAMING_SNAKE_CASE						)
 extractor.save_pretrained(SCREAMING_SNAKE_CASE						)


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

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

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








import numpy as np







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = np.exp(SCREAMING_SNAKE_CASE						)

 # Add up the all the exponentials
 __lowerCAmelCase:  Dict        = np.sum(SCREAMING_SNAKE_CASE						)

 # Divide every exponent by the sum of all exponents
 __lowerCAmelCase:  int        = exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
  print(softmax((0,)))





