




"""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 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Union[str, Any]						 =		'''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=2048						):
				f.write(_UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					str						 =		VivitConfig()

	A_	:					List[str]						 =		400
	A_	:					int						 =		'''huggingface/label-files'''
	A_	:					Optional[Any]						 =		'''kinetics400-id2label.json'''

	A_	:					Tuple						 =		json.load(open(hf_hub_download(_UpperCAmelCase	,   _UpperCAmelCase	,   repo_type='''dataset'''						)	,   '''r'''						)						)
	A_	:					Tuple						 =		{int(_UpperCAmelCase						): v for k, v in idalabel.items()}
	A_	:					int						 =		idalabel
	A_	:					Optional[int]						 =		{v: k for k, v in idalabel.items()}
	return config

def 		lowercase_   (		):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase						):




	"""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 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	return current.flatten()

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					List[Any]						 =		state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

	A_	:					Optional[Any]						 =		f"""encoder.layer.{i}."""
	A_	:					Union[str, Any]						 =		{
	    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 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase=False						):




	"""simple docstring"""






	A_	:					Optional[Any]						 =		get_n_layers(_UpperCAmelCase						)

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

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

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

	A_	:					Tuple						 =		state_dict['''optimizer''']['''target''']['''cls''']
	A_	:					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:
		A_	:					int						 =		{'''vivit.''' + k: v for k, v in new_state.items()}
		A_	:					Dict						 =		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(_UpperCAmelCase						) for k, v in new_state.items()}

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					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 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Optional[int]						 =		'''checkpoint'''

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

	A_	:					Optional[int]						 =		restore_checkpoint(_UpperCAmelCase	,   _UpperCAmelCase						)
	A_	:					Optional[int]						 =		transform_state(_UpperCAmelCase	,   classification_head=_UpperCAmelCase						)

	A_	:					List[str]						 =		get_vivit_config()

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

	A_	:					Any						 =		VivitForVideoClassification(_UpperCAmelCase						)
	model.load_state_dict(_UpperCAmelCase						)
	model.eval()

	A_	:					Dict						 =		get_processor()

	A_	:					List[Any]						 =		prepare_video()
	A_	:					Dict						 =		extractor(_UpperCAmelCase	,   return_tensors='''pt'''						)

	A_	:					Tuple						 =		model(**_UpperCAmelCase						)

	A_	:					List[str]						 =		torch.Size([1, 400]						)
	A_	:					Union[str, Any]						 =		torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]						)

	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	:      Union[str, Any]   =							argparse.ArgumentParser()

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

		_lowerCamelCase	:      int   =							parser.parse_args()
		convert(args.output_model_name)











"""simple docstring"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class 							lowercase   :







				def __init__(				self :       Optional[int]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		key
					A_	:					str						 =		freq







				def __str__(				self :       Optional[Any]							):



					"""simple docstring"""

					return F"""Node(key={self.key}, freq={self.freq})"""

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if i > j or i < 0 or j > len(_UpperCAmelCase						) - 1:
		return

	A_	:					Union[str, Any]						 =		root[i][j]
	if parent == -1:  # root does not have a parent
		print(f"""{key[node]} is the root of the binary search tree."""						)
	elif is_left:
		print(f"""{key[node]} is the left child of key {parent}."""						)
	else:
		print(f"""{key[node]} is the right child of key {parent}."""						)

	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   node - 1	,   key[node]	,   _UpperCAmelCase						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   node + 1	,   _UpperCAmelCase	,   key[node]	,   _UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	nodes.sort(key=lambda _UpperCAmelCase						: node.key						)

	A_	:					Any						 =		len(_UpperCAmelCase						)

	A_	:					int						 =		[nodes[i].key for i in range(_UpperCAmelCase						)]
	A_	:					List[str]						 =		[nodes[i].freq for i in range(_UpperCAmelCase						)]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	A_	:					List[str]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	A_	:					Optional[int]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# stores tree roots that will be used later for constructing binary search tree
	A_	:					List[Any]						 =		[[i if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]

	for interval_length in range(2	,   n + 1						):
		for i in range(n - interval_length + 1						):
			A_	:					List[Any]						 =		i + interval_length - 1

			A_	:					List[Any]						 =		sys.maxsize  # set the value to "infinity"
			A_	:					Union[str, Any]						 =		total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]	,   root[i + 1][j] + 1						):  # r is a temporal root
				A_	:					Tuple						 =		dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				A_	:					List[str]						 =		dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				A_	:					Optional[int]						 =		left + total[i][j] + right

				if dp[i][j] > cost:
					A_	:					Dict						 =		cost
					A_	:					List[str]						 =		r

	print('''Binary search tree nodes:'''						)
	for node in nodes:
		print(_UpperCAmelCase						)

	print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   0	,   n - 1	,   -1	,   _UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		[Node(_UpperCAmelCase	,   randint(1	,   50						)						) for i in range(10	,   0	,   -1						)]
	find_optimal_binary_search_tree(_UpperCAmelCase						)


if __name__ == "__main__":
		main()





