


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__      :   List[str]  ):
							'''simple docstring'''





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

							with open(UpperCamelCase_	,    'wb'  ) as f:
														with requests.get(UpperCamelCase_	,    stream=UpperCamelCase_  ) as req:
																					for chunk in req.iter_content(chunk_size=2_048  ):
																												f.write(UpperCamelCase_  )




def 			a_   (					):
							'''simple docstring'''





							_lowerCamelCase :	int						=VivitConfig()

							_lowerCamelCase :	Optional[int]						=400
							_lowerCamelCase :	int						='huggingface/label-files'
							_lowerCamelCase :	int						='kinetics400-id2label.json'

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




def 			a_   (					):
							'''simple docstring'''





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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Optional[Any]  ):
							'''simple docstring'''





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

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

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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Optional[int]  ):
							'''simple docstring'''





							return current.flatten()




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str  ):
							'''simple docstring'''





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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Optional[int]  ):
							'''simple docstring'''





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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[Any]	,    SCREAMING_SNAKE_CASE__      :   Dict  ):
							'''simple docstring'''





							_lowerCamelCase :	List[str]						=state_dict['optimizer']['target']['Transformer'][F'''encoderblock_{i}''']

							_lowerCamelCase :	List[str]						=F'''encoder.layer.{i}.'''
							_lowerCamelCase :	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 			a_   (					SCREAMING_SNAKE_CASE__      :   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__      :   Union[str, Any]	,    SCREAMING_SNAKE_CASE__      :   str=False  ):
							'''simple docstring'''





							_lowerCamelCase :	List[Any]						=get_n_layers(UpperCamelCase_  )

							_lowerCamelCase :	Any						=OrderedDict()

							_lowerCamelCase :	Any						=state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
							_lowerCamelCase :	Union[str, Any]						=state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

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

							_lowerCamelCase :	Dict						=state_dict['optimizer']['target']['cls']
							_lowerCamelCase :	List[str]						=state_dict['optimizer']['target']['Transformer']['posembed_input'][
							    'pos_embedding'
							]

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

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

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




def 			a_   (					):
							'''simple docstring'''





							_lowerCamelCase :	Optional[Any]						=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__      :   int  ):
							'''simple docstring'''





							_lowerCamelCase :	int						='checkpoint'

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

							_lowerCamelCase :	Optional[Any]						=restore_checkpoint(UpperCamelCase_	,    UpperCamelCase_  )
							_lowerCamelCase :	Optional[int]						=transform_state(UpperCamelCase_	,    classification_head=UpperCamelCase_  )

							_lowerCamelCase :	Optional[Any]						=get_vivit_config()

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

							_lowerCamelCase :	List[Any]						=VivitForVideoClassification(UpperCamelCase_  )
							model.load_state_dict(UpperCamelCase_  )
							model.eval()

							_lowerCamelCase :	Optional[int]						=get_processor()

							_lowerCamelCase :	Dict						=prepare_video()
							_lowerCamelCase :	Tuple						=extractor(UpperCamelCase_	,    return_tensors='pt'  )

							_lowerCamelCase :	str						=model(**UpperCamelCase_  )

							_lowerCamelCase :	Union[str, Any]						=torch.Size([1, 400]  )
							_lowerCamelCase :	List[Any]						=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]	,    UpperCamelCase_	,    atol=1e-4  ), outputs.logits[0, :5]

							model.save_pretrained(UpperCamelCase_  )
							extractor.save_pretrained(UpperCamelCase_  )


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)






from collections.abc import Callable

lowerCamelCase		      = Callable[[float], float]  # type alias for a real -> real function




def 			a_   (					SCREAMING_SNAKE_CASE__      :   RealFunc	,    SCREAMING_SNAKE_CASE__      :   RealFunc	,    SCREAMING_SNAKE_CASE__      :   int	,    ):
       '''simple docstring'''





       _lowerCamelCase :	Tuple						=float(SCREAMING_SNAKE_CASE__  )
       while True:
              try:
                     _lowerCamelCase :	int						=prev_guess - function(SCREAMING_SNAKE_CASE__  ) / derivative(SCREAMING_SNAKE_CASE__  )
              except ZeroDivisionError:
                     raise ZeroDivisionError('Could not find root'  ) from None
              if abs(prev_guess - next_guess  ) < 10**-5:
                     return next_guess
              _lowerCamelCase :	Union[str, Any]						=next_guess




def 			a_   (					SCREAMING_SNAKE_CASE__      :   float  ):
       '''simple docstring'''





       return (x**3) - (2 * x) - 5




def 			a_   (					SCREAMING_SNAKE_CASE__      :   float  ):
       '''simple docstring'''





       return 3 * (x**2) - 2


if __name__ == "__main__":
 print(newton(f, fa, 3))


