



'''simple docstring'''

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

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



def   __lowerCamelCase  (   )    ->   VivitConfig:
		_a  :   List[str]				 =							VivitConfig()

		_a  :   Optional[Any]				 =							400
		_a  :   Dict				 =							'huggingface/label-files'
		_a  :   Tuple				 =							'kinetics400-id2label.json'

		_a  :   Optional[int]				 =							json.load(open(hf_hub_download(lowerCAmelCase_		,    lowerCAmelCase_		,    repo_type='dataset'						)		,    'r'						)						)
		_a  :   Dict				 =							{int(lowerCAmelCase_						): v for k, v in idalabel.items()}
		_a  :   List[str]				 =							idalabel
		_a  :   str				 =							{v: k for k, v in idalabel.items()}
		return config



def   __lowerCamelCase  (   )    ->   Any:
		_a  :   Any				 =							hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video'		,    filename='eating_spaghetti_32_frames.npy'		,    repo_type='dataset'						)
		_a  :   Any				 =							np.load(lowerCAmelCase_						)
		return list(lowerCAmelCase_						)



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Any:



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

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

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



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Any:
		return current.flatten()



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Union[str, Any]:
		return np.reshape(lowerCAmelCase_		,    (current.shape[0], current.shape[1] * current.shape[2])						).T



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Optional[int]:
		return np.reshape(lowerCAmelCase_		,    (current.shape[0] * current.shape[1], current.shape[2])						).T



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   int:
		_a  :   Tuple				 =							state_dict['optimizer']['target']['Transformer'][f"""encoderblock_{i}"""]

		_a  :   str				 =							f"""encoder.layer.{i}."""
		_a  :   int				 =							{
		    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  (   lowerCAmelCase_						)    ->   List[Any]:
		return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_=False						)    ->   Any:
		_a  :   List[Any]				 =							get_n_layers(lowerCAmelCase_						)

		_a  :   str				 =							OrderedDict()

		_a  :   Optional[int]				 =							state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
		_a  :   List[str]				 =							state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

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

		_a  :   Dict				 =							state_dict['optimizer']['target']['cls']
		_a  :   Union[str, Any]				 =							state_dict['optimizer']['target']['Transformer']['posembed_input'][
		    'pos_embedding'
		]

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

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

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



def   __lowerCamelCase  (   )    ->   VivitImageProcessor:
		_a  :   List[str]				 =							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  (   lowerCAmelCase_						)    ->   List[Any]:
		_a  :   Any				 =							'checkpoint'

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

		_a  :   Tuple				 =							restore_checkpoint(lowerCAmelCase_		,    lowerCAmelCase_						)
		_a  :   Optional[int]				 =							transform_state(lowerCAmelCase_		,    classification_head=lowerCAmelCase_						)

		_a  :   Union[str, Any]				 =							get_vivit_config()

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

		_a  :   Tuple				 =							VivitForVideoClassification(lowerCAmelCase_						)
		model.load_state_dict(lowerCAmelCase_						)
		model.eval()

		_a  :   int				 =							get_processor()

		_a  :   Optional[int]				 =							prepare_video()
		_a  :   str				 =							extractor(lowerCAmelCase_		,    return_tensors='pt'						)

		_a  :   Optional[Any]				 =							model(**lowerCAmelCase_						)

		_a  :   Any				 =							torch.Size([1, 400]						)
		_a  :   Union[str, Any]				 =							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]		,    lowerCAmelCase_		,    atol=1E-4						), outputs.logits[0, :5]

		model.save_pretrained(lowerCAmelCase_						)
		extractor.save_pretrained(lowerCAmelCase_						)


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)









'''simple docstring'''

import math



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   list:
		if n <= 0:
				raise ValueError('Only positive integers have prime factors'						)
		_a  :   Tuple				 =							[]
		while n % 2 == 0:
				pf.append(2						)
				_a  :   str				 =							int(n / 2						)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				while n % i == 0:
						pf.append(lowerCAmelCase_						)
						_a  :   List[Any]				 =							int(n / i						)
		if n > 2:
				pf.append(lowerCAmelCase_						)
		return pf



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   int				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   str				 =							int(n / 2						)
		div *= temp
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   List[str]				 =							1
				while n % i == 0:
						temp += 1
						_a  :   List[str]				 =							int(n / i						)
				div *= temp
		if n > 1:
				div *= 2
		return div



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   List[str]				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   Tuple				 =							int(n / 2						)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   str				 =							1
				while n % i == 0:
						temp += 1
						_a  :   Any				 =							int(n / i						)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(lowerCAmelCase_						)



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		_a  :   Dict				 =							n
		for x in set(prime_factors(lowerCAmelCase_						)						):
				s *= (x - 1) / x
		return int(lowerCAmelCase_						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




