


'''simple docstring'''
from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						@abstractmethod
						def __lt__(     self       :			T   ,     lowerCAmelCase       :			T  ):
													pass






						@abstractmethod
						def __gt__(     self       :			T   ,     lowerCAmelCase       :			T  ):
													pass






						@abstractmethod
						def __eq__(     self       :			T   ,     lowerCAmelCase       :			object  ):
													pass



__SCREAMING_SNAKE_CASE				:							Union[str, Any]		       =		TypeVar('''T''', bound=Comparable)






class 				__lowerCAmelCase      (      Generic[T]	):

						"""simple docstring"""






						def __init__(     self       :			Any  ):
													A_ 						=		[]
													A_ 						=		0






						def __repr__(     self       :			Tuple  ):
													return str(self.h  )






						def 							_UpperCAmelCase					(     self       :			int   ,     lowerCAmelCase       :			int  ):
													if child_idx > 0:
																				return (child_idx - 1) // 2
													return None






						def 							_UpperCAmelCase					(     self       :			Optional[Any]   ,     lowerCAmelCase       :			int  ):
													A_ 						=		2 * parent_idx + 1
													if left_child_index < self.heap_size:
																				return left_child_index
													return None






						def 							_UpperCAmelCase					(     self       :			Optional[Any]   ,     lowerCAmelCase       :			int  ):
													A_ 						=		2 * parent_idx + 2
													if right_child_index < self.heap_size:
																				return right_child_index
													return None






						def 							_UpperCAmelCase					(     self       :			int   ,     lowerCAmelCase       :			int  ):
													if index < self.heap_size:
																				A_ 						=		index
																				A_ 						=		self.left_child_idx(lowerCAmelCase  )
																				A_ 						=		self.right_child_idx(lowerCAmelCase  )
																				# check which child is larger than its parent
																				if left_child is not None and self.h[left_child] > self.h[violation]:
																											A_ 						=		left_child
																				if right_child is not None and self.h[right_child] > self.h[violation]:
																											A_ 						=		right_child
																				# if violation indeed exists
																				if violation != index:
																											# swap to fix the violation
																											A_						,				A_ 						=		self.h[index], self.h[violation]
																											# fix the subsequent violation recursively if any
																											self.max_heapify(lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Optional[int]   ,     lowerCAmelCase       :			Iterable[T]  ):
													A_ 						=		list(lowerCAmelCase  )
													A_ 						=		len(self.h  )
													if self.heap_size > 1:
																				# max_heapify from right to left but exclude leaves (last level)
																				for i in range(self.heap_size // 2 - 1   ,     -1   ,     -1  ):
																											self.max_heapify(lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													if self.heap_size >= 2:
																				A_ 						=		self.h[0]
																				A_ 						=		self.h.pop(-1  )
																				self.heap_size -= 1
																				self.max_heapify(0  )
																				return me
													elif self.heap_size == 1:
																				self.heap_size -= 1
																				return self.h.pop(-1  )
													else:
																				raise Exception("Empty heap"  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     lowerCAmelCase       :			T  ):
													self.h.append(lowerCAmelCase  )
													A_ 						=		(self.heap_size - 1) // 2
													self.heap_size += 1
													while idx >= 0:
																				self.max_heapify(lowerCAmelCase  )
																				A_ 						=		(idx - 1) // 2






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		self.heap_size
													for j in range(size - 1   ,     0   ,     -1  ):
																				A_						,				A_ 						=		self.h[j], self.h[0]
																				self.heap_size -= 1
																				self.max_heapify(0  )
													A_ 						=		size


if __name__ == "__main__":
							import doctest

							# run doc test
							doctest.testmod()

							# demo
							for unsorted in [
							    [0],
							    [2],
							    [3, 5],
							    [5, 3],
							    [5, 5],
							    [0, 0, 0, 0],
							    [1, 1, 1, 1],
							    [2, 2, 3, 5],
							    [0, 2, 2, 3, 5],
							    [2, 5, 3, 0, 2, 3, 0, 3],
							    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
							    [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
							    [-45, -2, -5],
							]:
														print(F"unsorted array: {unsorted}")

														__SCREAMING_SNAKE_CASE				:							Heap[int]		       =		Heap()
														heap.build_max_heap(unsorted)
														print(F"after build heap: {heap}")

														print(F"max value: {heap.extract_max()}")
														print(F"after max value removed: {heap}")

														heap.insert(100)
														print(F"after new value 100 inserted: {heap}")

														heap.heap_sort()
														print(F"heap-sorted array: {heap}\n")









'''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)





