




"""simple docstring"""







from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__A :	Optional[int]       =   TypeVar('T')

def     snake_case__							(       _lowerCamelCase					)      ->int:






		"""simple docstring"""


		return (position - 1) // 2

def     snake_case__							(       _lowerCamelCase					)      ->int:






		"""simple docstring"""


		return (2 * position) + 1

def     snake_case__							(       _lowerCamelCase					)      ->int:






		"""simple docstring"""


		return (2 * position) + 2




class    lowerCAmelCase__							(      Generic[T]     ):


				"""simple docstring"""





				def __init__(  self       :						Any    ):
						__lowercase					:		Union[str, Any]        =					[]
						__lowercase					:		int        =					{}
						__lowercase					:		List[str]        =					0



				def __len__(  self       :						Optional[Any]    ):
						return self.elements



				def __repr__(  self       :						Optional[Any]    ):
						return str(self.heap    )



				def    snake_case     (  self       :						Union[str, Any]    ):
						return self.elements == 0



				def    snake_case     (  self       :						str     , lowercase__       :						T     , lowercase__       :						int    ):
						self.heap.append((elem, weight)    )
						__lowercase					:		Optional[int]        =					self.elements
						self.elements += 1
						self._bubble_up(__lowerCamelCase    )



				def    snake_case     (  self       :						Dict    ):




						if self.elements > 1:
								self._swap_nodes(0     , self.elements - 1    )
						__lowercase    ,__lowercase					:		Optional[Any]        =					self.heap.pop()
						del self.position_map[elem]
						self.elements -= 1
						if self.elements > 0:
								__lowercase    ,__lowercase					:		Union[str, Any]        =					self.heap[0]
								self._bubble_down(__lowerCamelCase    )
						return elem



				def    snake_case     (  self       :						int     , lowercase__       :						T     , lowercase__       :						int    ):
						__lowercase					:		Union[str, Any]        =					self.position_map[elem]
						__lowercase					:		Union[str, Any]        =					(elem, weight)
						if position > 0:
								__lowercase					:		int        =					get_parent_position(__lowerCamelCase    )
								__lowercase    ,__lowercase					:		Tuple        =					self.heap[parent_position]
								if parent_weight > weight:
										self._bubble_up(__lowerCamelCase    )
								else:
										self._bubble_down(__lowerCamelCase    )
						else:
								self._bubble_down(__lowerCamelCase    )



				def    snake_case     (  self       :						Tuple     , lowercase__       :						T    ):
						__lowercase					:		str        =					self.position_map[elem]
						if curr_pos == 0:
								return None
						__lowercase					:		Dict        =					get_parent_position(__lowerCamelCase    )
						__lowercase    ,__lowercase					:		Dict        =					self.heap[curr_pos]
						__lowercase    ,__lowercase					:		int        =					self.heap[parent_position]
						if parent_weight > weight:
								self._swap_nodes(__lowerCamelCase     , __lowerCamelCase    )
								return self._bubble_up(__lowerCamelCase    )
						return None



				def    snake_case     (  self       :						List[str]     , lowercase__       :						T    ):
						__lowercase					:		Any        =					self.position_map[elem]
						__lowercase    ,__lowercase					:		List[Any]        =					self.heap[curr_pos]
						__lowercase					:		Union[str, Any]        =					get_child_left_position(__lowerCamelCase    )
						__lowercase					:		Union[str, Any]        =					get_child_right_position(__lowerCamelCase    )
						if child_left_position < self.elements and child_right_position < self.elements:
								__lowercase    ,__lowercase					:		List[Any]        =					self.heap[child_left_position]
								__lowercase    ,__lowercase					:		int        =					self.heap[child_right_position]
								if child_right_weight < child_left_weight and child_right_weight < weight:
										self._swap_nodes(__lowerCamelCase     , __lowerCamelCase    )
										return self._bubble_down(__lowerCamelCase    )
						if child_left_position < self.elements:
								__lowercase    ,__lowercase					:		str        =					self.heap[child_left_position]
								if child_left_weight < weight:
										self._swap_nodes(__lowerCamelCase     , __lowerCamelCase    )
										return self._bubble_down(__lowerCamelCase    )
						else:
								return None
						if child_right_position < self.elements:
								__lowercase    ,__lowercase					:		List[Any]        =					self.heap[child_right_position]
								if child_right_weight < weight:
										self._swap_nodes(__lowerCamelCase     , __lowerCamelCase    )
										return self._bubble_down(__lowerCamelCase    )
						return None



				def    snake_case     (  self       :						Optional[int]     , lowercase__       :						int     , lowercase__       :						int    ):
						__lowercase					:		Dict        =					self.heap[nodea_pos][0]
						__lowercase					:		List[str]        =					self.heap[nodea_pos][0]
						__lowercase    ,__lowercase					:		List[str]        =					(
						    self.heap[nodea_pos],
						    self.heap[nodea_pos],
						)
						__lowercase					:		Optional[int]        =					nodea_pos
						__lowercase					:		List[str]        =					nodea_pos





class    lowerCAmelCase__							(      Generic[T]     ):


				"""simple docstring"""





				def __init__(  self       :						Optional[Any]    ):
						__lowercase					:		Union[str, Any]        =					{}
						__lowercase					:		Dict        =					0



				def __repr__(  self       :						Union[str, Any]    ):
						return str(self.connections    )



				def __len__(  self       :						int    ):
						return self.nodes



				def    snake_case     (  self       :						Optional[int]     , lowercase__       :						T    ):




						if node not in self.connections:
								__lowercase					:		List[str]        =					{}
								self.nodes += 1



				def    snake_case     (  self       :						List[Any]     , lowercase__       :						T     , lowercase__       :						T     , lowercase__       :						int    ):
						self.add_node(__lowerCamelCase    )
						self.add_node(__lowerCamelCase    )
						__lowercase					:		Union[str, Any]        =					weight
						__lowercase					:		int        =					weight





def     snake_case__							(       _lowerCamelCase,	)      ->tuple[dict[T, int], dict[T, T | None]]:






		"""simple docstring"""


		__lowercase					:		str        =					{node: maxsize for node in graph.connections}
		__lowercase					:		List[Any]        =					{node: None for node in graph.connections}

		__lowercase					:		Optional[Any]        =					MinPriorityQueue()
		for node, weight in dist.items():
				priority_queue.push(_lowerCamelCase,	_lowerCamelCase					)

		if priority_queue.is_empty():
				return dist, parent

		# initialization
		__lowercase					:		Dict        =					priority_queue.extract_min()
		__lowercase					:		str        =					0
		for neighbour in graph.connections[node]:
				if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
						__lowercase					:		str        =					dist[node] + graph.connections[node][neighbour]
						priority_queue.update_key(_lowerCamelCase,	dist[neighbour]					)
						__lowercase					:		int        =					node

    # running prim's algorithm
		while not priority_queue.is_empty():
				__lowercase					:		List[Any]        =					priority_queue.extract_min()
				for neighbour in graph.connections[node]:
						if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
								__lowercase					:		str        =					dist[node] + graph.connections[node][neighbour]
								priority_queue.update_key(_lowerCamelCase,	dist[neighbour]					)
								__lowercase					:		Dict        =					node
		return dist, parent







import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

		if "_quant" in model_name:
				raise ValueError('Quantized models are not supported.'							)

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							) + 1: v for k, v in idalabel.items()}
		__lowercase											=     'background'
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

		# Load weights from TensorFlow checkpoint
		load_tf_weights_in_mobilenet_va(snake_case						,   snake_case						,   snake_case							)

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

		if expected_logits is not None:
				assert torch.allclose(logits[0, :3]						,   snake_case						,   atol=1E-4							)

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		print(F"Saving model {model_name} to {pytorch_dump_folder_path}"							)
		model.save_pretrained(snake_case							)
		print(F"Saving image processor to {pytorch_dump_folder_path}"							)
		image_processor.save_pretrained(snake_case							)

		if push_to_hub:
				print('Pushing to the hub...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''mobilenet_v1_1.0_224''',
		    type=str,
		    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
		)
		parser.add_argument(
		    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




