
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					:  Optional[int]    ):






  """simple docstring"""




  _lowerCAmelCase						=		'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=2048    ):
        f.write(__SCREAMING_SNAKE_CASE    )



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		VivitConfig()

  _lowerCAmelCase						=		400
  _lowerCAmelCase						=		'huggingface/label-files'
  _lowerCAmelCase						=		'kinetics400-id2label.json'

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



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		hf_hub_download(
      repo_id='hf-internal-testing/spaghetti-video'		,				filename='eating_spaghetti_32_frames.npy'		,				repo_type='dataset'    )
  _lowerCAmelCase						=		np.load(__SCREAMING_SNAKE_CASE    )
  return list(__SCREAMING_SNAKE_CASE    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  np.ndarray    ):






  """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    ):






  """simple docstring"""




  return current.flatten()



def 	_a  (					__SCREAMING_SNAKE_CASE					:  np.ndarray    ):






  """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    ):






  """simple docstring"""




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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  Any    ):






  """simple docstring"""




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

  _lowerCAmelCase						=		f'''encoder.layer.{i}.'''
  _lowerCAmelCase						=		{
      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]    ):






  """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					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  Any=False    ):






  """simple docstring"""




  _lowerCAmelCase						=		get_n_layers(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		OrderedDict()

  _lowerCAmelCase						=		state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
  _lowerCAmelCase						=		state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

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

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






  """simple docstring"""




  _lowerCAmelCase						=		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  (					__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  _lowerCAmelCase						=		'checkpoint'

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

  _lowerCAmelCase						=		restore_checkpoint(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		transform_state(__SCREAMING_SNAKE_CASE		,				classification_head=__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		get_vivit_config()

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

  _lowerCAmelCase						=		VivitForVideoClassification(__SCREAMING_SNAKE_CASE    )
  model.load_state_dict(__SCREAMING_SNAKE_CASE    )
  model.eval()

  _lowerCAmelCase						=		get_processor()

  _lowerCAmelCase						=		prepare_video()
  _lowerCAmelCase						=		extractor(__SCREAMING_SNAKE_CASE		,				return_tensors='pt'    )

  _lowerCAmelCase						=		model(**__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		torch.Size([1, 400]    )
  _lowerCAmelCase						=		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__":
       _UpperCamelCase:					Dict     =argparse.ArgumentParser()

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

       _UpperCamelCase:					Union[str, Any]     =parser.parse_args()
       convert(args.output_model_name)


import random



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  def is_sorted(__SCREAMING_SNAKE_CASE					:  str    ):
    for i in range(len(__SCREAMING_SNAKE_CASE    ) - 1    ):
      if collection[i] > collection[i + 1]:
        return False
    return True

  while not is_sorted(__SCREAMING_SNAKE_CASE    ):
    random.shuffle(__SCREAMING_SNAKE_CASE    )
  return collection


if __name__ == "__main__":
       _UpperCamelCase:					Dict     =input('Enter numbers separated by a comma:\n').strip()
       _UpperCamelCase:					Optional[Any]     =[int(item) for item in user_input.split(',')]
       print(bogo_sort(unsorted))
