





"""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_     (  lowercase_       :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

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




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	VivitConfig()

 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	400
 __SCREAMING_SNAKE_CASE			:      str 							=	'''huggingface/label-files'''
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	'''kinetics400-id2label.json'''

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




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






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




def 		lowerCAmelCase_     (  lowercase_       :							np.ndarray		):

 '''simple docstring'''






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

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

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




def 		lowerCAmelCase_     (  lowercase_       :							np.ndarray		):

 '''simple docstring'''






 return current.flatten()




def 		lowerCAmelCase_     (  lowercase_       :							np.ndarray		):

 '''simple docstring'''






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




def 		lowerCAmelCase_     (  lowercase_       :							np.ndarray		):

 '''simple docstring'''






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




def 		lowerCAmelCase_     (  lowercase_       :							Dict			,				lowercase_       :							str		):

 '''simple docstring'''






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

 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	F'''encoder.layer.{i}.'''
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	{
     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_     (  lowercase_       :							Union[str, Any]		):

 '''simple docstring'''






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




def 		lowerCAmelCase_     (  lowercase_       :							List[Any]			,				lowercase_       :							Any=False		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Tuple 							=	get_n_layers(lowercase_		)

 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	OrderedDict()

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

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

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

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

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

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




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      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_     (  lowercase_       :							str		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Tuple 							=	'''checkpoint'''

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

 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	restore_checkpoint(lowercase_			,				lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	transform_state(lowercase_			,				classification_head=lowercase_		)

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

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

 __SCREAMING_SNAKE_CASE			:      str 							=	VivitForVideoClassification(lowercase_		)
 model.load_state_dict(lowercase_		)
 model.eval()

 __SCREAMING_SNAKE_CASE			:      str 							=	get_processor()

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	prepare_video()
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	extractor(lowercase_			,				return_tensors='''pt'''		)

 __SCREAMING_SNAKE_CASE			:      int 							=	model(**lowercase_		)

 __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.Size([1, 400]		)
 __SCREAMING_SNAKE_CASE			:      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]			,				lowercase_			,				atol=1E-4		), outputs.logits[0, :5]

 model.save_pretrained(lowercase_		)
 extractor.save_pretrained(lowercase_		)


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"""





def 		lowerCAmelCase_     (  lowercase_       :							int = 30		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      str 							=	exponent + 2
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	(1 + 5**0.5) / 2
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

 return int(lowercase_		)


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

