


'''simple docstring'''
class 				__lowerCAmelCase      :

						"""simple docstring"""






						def __init__(     self       :			Optional[Any]   ,     lowerCAmelCase       :			int  ):
													A_ 						=		val
													# Number of nodes in left subtree
													A_ 						=		0
													A_ 						=		None
													A_ 						=		None
													A_ 						=		None






						def 							_UpperCAmelCase					(     self       :			str   ,     lowerCAmelCase       :			str  ):
													assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

													if self.val < other.val:
																				A_ 						=		self.right
																				A_ 						=		None
																				if self.right:
																											A_ 						=		other
																				A_ 						=		other
																				A_ 						=		self.left_tree_size * 2 + 1
																				return self
													else:
																				A_ 						=		other.right
																				A_ 						=		None
																				if other.right:
																											A_ 						=		self
																				A_ 						=		self
																				A_ 						=		other.left_tree_size * 2 + 1
																				return other






class 				__lowerCAmelCase      :

						"""simple docstring"""






						def __init__(     self       :			Any   ,     lowerCAmelCase       :			Dict=None   ,     lowerCAmelCase       :			List[str]=None   ,     lowerCAmelCase       :			List[str]=0  ):
													A_ 						=		heap_size
													A_ 						=		bottom_root
													A_ 						=		min_node






						def 							_UpperCAmelCase					(     self       :			Optional[Any]   ,     lowerCAmelCase       :			Dict  ):






													if other.size == 0:
																				return None
													if self.size == 0:
																				A_ 						=		other.size
																				A_ 						=		other.bottom_root
																				A_ 						=		other.min_node
																				return None
													# Update size
													A_ 						=		self.size + other.size

													# Update min.node
													if self.min_node.val > other.min_node.val:
																				A_ 						=		other.min_node
													# Merge

													# Order roots by left_subtree_size
													A_ 						=		[]
													A_						,				A_ 						=		self.bottom_root, other.bottom_root
													while i or j:
																				if i and ((not j) or i.left_tree_size < j.left_tree_size):
																											combined_roots_list.append((i, True)  )
																											A_ 						=		i.parent
																				else:
																											combined_roots_list.append((j, False)  )
																											A_ 						=		j.parent
        # Insert links between them
													for i in range(len(lowerCAmelCase_  ) - 1  ):
																				if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
																											A_ 						=		combined_roots_list[i + 1][0]
																											A_ 						=		combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
													A_ 						=		combined_roots_list[0][0]
													while i.parent:
																				if (
																				    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
																				) or (
																				    i.left_tree_size == i.parent.left_tree_size
																				    and i.left_tree_size != i.parent.parent.left_tree_size
																				):
																											# Neighbouring Nodes
																											A_ 						=		i.left
																											A_ 						=		i.parent.parent

																											# Merging trees
																											A_ 						=		i.merge_trees(i.parent  )

																											# Updating links
																											A_ 						=		previous_node
																											A_ 						=		next_node
																											if previous_node:
																																		A_ 						=		i
																											if next_node:
																																		A_ 						=		i
																				else:
																											A_ 						=		i.parent
        # Updating self.bottom_root
													while i.left:
																				A_ 						=		i.left
													A_ 						=		i

													# Update other
													A_ 						=		self.size
													A_ 						=		self.bottom_root
													A_ 						=		self.min_node

													# Return the merged heap
													return self






						def 							_UpperCAmelCase					(     self       :			Optional[int]   ,     lowerCAmelCase       :			List[Any]  ):






													if self.size == 0:
																				A_ 						=		Node(lowerCAmelCase_  )
																				A_ 						=		1
																				A_ 						=		self.bottom_root
													else:
																				# Create new node
																				A_ 						=		Node(lowerCAmelCase_  )

																				# Update size
																				self.size += 1

																				# update min_node
																				if val < self.min_node.val:
																											A_ 						=		new_node
																				# Put new_node as a bottom_root in heap
																				A_ 						=		new_node
																				A_ 						=		self.bottom_root
																				A_ 						=		new_node

																				# Consecutively merge roots with same left_tree_size
																				while (
																				    self.bottom_root.parent
																				    and self.bottom_root.left_tree_size
																				    == self.bottom_root.parent.left_tree_size
																				):
																											# Next node
																											A_ 						=		self.bottom_root.parent.parent

																											# Merge
																											A_ 						=		self.bottom_root.merge_trees(self.bottom_root.parent  )

																											# Update Links
																											A_ 						=		next_node
																											A_ 						=		None
																											if next_node:
																																		A_ 						=		self.bottom_root






						def 							_UpperCAmelCase					(     self       :			Dict  ):
													return self.min_node.val






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													return self.size == 0






						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													A_ 						=		self.min_node.val

													# Last element in heap corner case
													if self.size == 1:
																				# Update size
																				A_ 						=		0

																				# Update bottom root
																				A_ 						=		None

																				# Update min_node
																				A_ 						=		None

																				return min_value
													# No right subtree corner case
													# The structure of the tree implies that this should be the bottom root
													# and there is at least one other root
													if self.min_node.right is None:
																				# Update size
																				self.size -= 1

																				# Update bottom root
																				A_ 						=		self.bottom_root.parent
																				A_ 						=		None

																				# Update min_node
																				A_ 						=		self.bottom_root
																				A_ 						=		self.bottom_root.parent
																				while i:
																											if i.val < self.min_node.val:
																																		A_ 						=		i
																											A_ 						=		i.parent
																				return min_value
													# General case
													# Find the BinomialHeap of the right subtree of min_node
													A_ 						=		self.min_node.right
													A_ 						=		None
													A_ 						=		bottom_of_new
													A_ 						=		1

													# Size, min_node and bottom_root
													while bottom_of_new.left:
																				A_ 						=		size_of_new * 2 + 1
																				A_ 						=		bottom_of_new.left
																				if bottom_of_new.val < min_of_new.val:
																											A_ 						=		bottom_of_new
        # Corner case of single root on top left path
													if (not self.min_node.left) and (not self.min_node.parent):
																				A_ 						=		size_of_new
																				A_ 						=		bottom_of_new
																				A_ 						=		min_of_new
																				# print("Single root, multiple nodes case")
																				return min_value
													# Remaining cases
													# Construct heap of right subtree
													A_ 						=		BinomialHeap(
													    bottom_root=lowerCAmelCase_   ,     min_node=lowerCAmelCase_   ,     heap_size=lowerCAmelCase_  )

													# Update size
													A_ 						=		self.size - 1 - size_of_new

													# Neighbour nodes
													A_ 						=		self.min_node.left
													A_ 						=		self.min_node.parent

													# Initialize new bottom_root and min_node
													A_ 						=		previous_node or next_node
													A_ 						=		next_node

													# Update links of previous_node and search below for new min_node and
													# bottom_root
													if previous_node:
																				A_ 						=		next_node

																				# Update bottom_root and search for min_node below
																				A_ 						=		previous_node
																				A_ 						=		previous_node
																				while self.bottom_root.left:
																											A_ 						=		self.bottom_root.left
																											if self.bottom_root.val < self.min_node.val:
																																		A_ 						=		self.bottom_root
													if next_node:
																				A_ 						=		previous_node

																				# Search for new min_node above min_node
																				A_ 						=		next_node
																				while i:
																											if i.val < self.min_node.val:
																																		A_ 						=		i
																											A_ 						=		i.parent
        # Merge heaps
													self.merge_heaps(lowerCAmelCase_  )

													return min_value






						def 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		self.bottom_root
													while top_root.parent:
																				A_ 						=		top_root.parent
													# preorder
													A_ 						=		[]
													self.__traversal(lowerCAmelCase_   ,     lowerCAmelCase_  )
													return heap_pre_order






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     lowerCAmelCase       :			List[str]   ,     lowerCAmelCase       :			int   ,     lowerCAmelCase       :			Union[str, Any]=0  ):






													if curr_node:
																				preorder.append((curr_node.val, level)  )
																				self.__traversal(curr_node.left   ,     lowerCAmelCase_   ,     level + 1  )
																				self.__traversal(curr_node.right   ,     lowerCAmelCase_   ,     level + 1  )
													else:
																				preorder.append(("#", level)  )






						def __str__(     self       :			str  ):






													if self.is_empty():
																				return ""
													A_ 						=		self.pre_order()

													return "\n".join(("-" * level + str(lowerCAmelCase_  )) for value, level in preorder_heap  )


# Unit Tests
if __name__ == "__main__":
							import doctest

							doctest.testmod()









'''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 			a_							(  UpperCamelCase_   ):
							A_ 						=		"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_0_4_8   ):
																												f.write(UpperCamelCase_   )







def 			a_							(  ):
							A_ 						=		VivitConfig()

							A_ 						=		4_0_0
							A_ 						=		"huggingface/label-files"
							A_ 						=		"kinetics400-id2label.json"

							A_ 						=		json.load(open(hf_hub_download(UpperCamelCase_	,     UpperCamelCase_	,     repo_type="dataset"   )	,     "r"   )   )
							A_ 						=		{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
							A_ 						=		idalabel
							A_ 						=		{v: k for k, v in idalabel.items()}
							return config







def 			a_							(  ):
							A_ 						=		hf_hub_download(
							    repo_id="hf-internal-testing/spaghetti-video"	,     filename="eating_spaghetti_32_frames.npy"	,     repo_type="dataset"   )
							A_ 						=		np.load(UpperCamelCase_   )
							return list(UpperCamelCase_   )







def 			a_							(  UpperCamelCase_   ):
							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_							(  UpperCamelCase_   ):
							return current.flatten()







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







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







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		state_dict["optimizer"]["target"]["Transformer"][f"encoderblock_{i}"]

							A_ 						=		f"encoder.layer.{i}."
							A_ 						=		{
							    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_							(  UpperCamelCase_   ):
							return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]   )







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_=False   ):
							A_ 						=		get_n_layers(UpperCamelCase_   )

							A_ 						=		OrderedDict()

							A_ 						=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
							A_ 						=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

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

							A_ 						=		state_dict["optimizer"]["target"]["cls"]
							A_ 						=		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_ 						=		{"vivit." + k: v for k, v in new_state.items()}
														A_ 						=		np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]   )
														A_ 						=		np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]   )

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







def 			a_							(  ):
							A_ 						=		VivitImageProcessor()

							assert extractor.do_resize is True
							assert extractor.size == {"shortest_edge": 2_5_6}
							assert extractor.do_center_crop is True
							assert extractor.crop_size == {"width": 2_2_4, "height": 2_2_4}
							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 / 2_5_5

							# zero-centering = True in original implementation
							assert extractor.do_zero_centering is True

							return extractor







def 			a_							(  UpperCamelCase_   ):
							A_ 						=		"checkpoint"

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

							A_ 						=		restore_checkpoint(UpperCamelCase_	,     UpperCamelCase_   )
							A_ 						=		transform_state(UpperCamelCase_	,     classification_head=UpperCamelCase_   )

							A_ 						=		get_vivit_config()

							assert config.image_size == 2_2_4
							assert config.num_frames == 3_2

							A_ 						=		VivitForVideoClassification(UpperCamelCase_   )
							model.load_state_dict(UpperCamelCase_   )
							model.eval()

							A_ 						=		get_processor()

							A_ 						=		prepare_video()
							A_ 						=		extractor(UpperCamelCase_	,     return_tensors="pt"   )

							A_ 						=		model(**UpperCamelCase_   )

							A_ 						=		torch.Size([1, 4_0_0]   )
							A_ 						=		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__":
							__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		argparse.ArgumentParser()

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

							__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		parser.parse_args()
							convert(args.output_model_name)





