






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      (		a_			)			->    List[Any]:
							lowerCAmelCase_       		=  'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

							with open(a_				,		'wb'			) as f:
														with requests.get(a_				,		stream=a_			) as req:
																					for chunk in req.iter_content(chunk_size=2_048			):
																												f.write(a_			)
def   lowerCamelCase      (		)			->    VivitConfig:
							lowerCAmelCase_       		=  VivitConfig()

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

							lowerCAmelCase_       		=  json.load(open(hf_hub_download(a_				,		a_				,		repo_type='dataset'			)				,		'r'			)			)
							lowerCAmelCase_       		=  {int(a_			): v for k, v in idalabel.items()}
							lowerCAmelCase_       		=  idalabel
							lowerCAmelCase_       		=  {v: k for k, v in idalabel.items()}
							return config
def   lowerCamelCase      (		)			->    Union[str, Any]:
							lowerCAmelCase_       		=  hf_hub_download(
							    repo_id='hf-internal-testing/spaghetti-video'				,		filename='eating_spaghetti_32_frames.npy'				,		repo_type='dataset'			)
							lowerCAmelCase_       		=  np.load(a_			)
							return list(a_			)
def   lowerCamelCase      (		a_			)			->    Optional[Any]:
							if np.ndim(a_			) == 2:
														return transform_attention_bias(a_			)

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

							else:
														raise Exception(F'''Invalid number of dimesions: {np.ndim(a_			)}'''			)
def   lowerCamelCase      (		a_			)			->    List[str]:
							return current.flatten()
def   lowerCamelCase      (		a_			)			->    Dict:
							return np.reshape(a_				,		(current.shape[0], current.shape[1] * current.shape[2])			).T
def   lowerCamelCase      (		a_			)			->    Tuple:
							return np.reshape(a_				,		(current.shape[0] * current.shape[1], current.shape[2])			).T
def   lowerCamelCase      (		a_				,		a_			)			->    List[Any]:
							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   lowerCamelCase      (		a_			)			->    List[str]:
							return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]			)
def   lowerCamelCase      (		a_				,		a_=False			)			->    Dict:
							lowerCAmelCase_       		=  get_n_layers(a_			)

							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(a_			):
														new_state.update(transform_state_encoder_block(a_				,		a_			)			)

							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(a_			) for k, v in new_state.items()}
def   lowerCamelCase      (		)			->    VivitImageProcessor:
							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   lowerCamelCase      (		a_			)			->    Optional[int]:
							lowerCAmelCase_       		=  'checkpoint'

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

							lowerCAmelCase_       		=  restore_checkpoint(a_				,		a_			)
							lowerCAmelCase_       		=  transform_state(a_				,		classification_head=a_			)

							lowerCAmelCase_       		=  get_vivit_config()

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

							lowerCAmelCase_       		=  VivitForVideoClassification(a_			)
							model.load_state_dict(a_			)
							model.eval()

							lowerCAmelCase_       		=  get_processor()

							lowerCAmelCase_       		=  prepare_video()
							lowerCAmelCase_       		=  extractor(a_				,		return_tensors='pt'			)

							lowerCAmelCase_       		=  model(**a_			)

							lowerCAmelCase_       		=  torch.Size([1, 400]			)
							lowerCAmelCase_       		=  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]				,		a_				,		atol=1e-4			), outputs.logits[0, :5]

							model.save_pretrained(a_			)
							extractor.save_pretrained(a_			)


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   lowerCamelCase      (		a_ = 4_000_000			)			->    int:

							if n <= 1:
														return 0
							lowerCAmelCase_       		=  0
							lowerCAmelCase_       		=  2
							lowerCAmelCase_       		=  0
							while 4 * b + a <= n:
														lowerCAmelCase_		,     lowerCAmelCase_       		=  b, 4 * b + a
														count += a
							return count + b


if __name__ == "__main__":
		print(f'''{solution() = }''')



