




"""simple docstring"""

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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase								=						logging.get_logger(__name__)



def 				a (						__snake_case     :				str  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[Any]      			=      MobileNetVaConfig(layer_norm_eps=0.001  )

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

				UpperCAmelCase_					:List[str]      			=      re.match(r'''^.*mobilenet_v2_([^_]*)_([^_]*)$''',    __snake_case  )
				if matches:
								UpperCAmelCase_					:List[str]      			=      float(matches[1]  )
								UpperCAmelCase_					:List[str]      			=      int(matches[2]  )

				if model_name.startswith('''deeplabv3_'''  ):
								UpperCAmelCase_					:int      			=      8
								UpperCAmelCase_					:Optional[Any]      			=      21
								UpperCAmelCase_					:List[str]      			=      '''pascal-voc-id2label.json'''
				else:
								# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
								# of the usual 1000. The first class (index 0) is "background".
								UpperCAmelCase_					:Union[str, Any]      			=      1001
								UpperCAmelCase_					:Optional[Any]      			=      '''imagenet-1k-id2label.json'''

				UpperCAmelCase_					:List[Any]      			=      '''huggingface/label-files'''
				UpperCAmelCase_					:Optional[int]      			=      json.load(open(hf_hub_download(__snake_case,    __snake_case,    repo_type='''dataset'''  ),    '''r'''  )  )

				if config.num_labels == 1001:
								UpperCAmelCase_					:int      			=      {int(__snake_case  ) + 1: v for k, v in idalabel.items()}
								UpperCAmelCase_					:List[Any]      			=      '''background'''
				else:
								UpperCAmelCase_					:Union[str, Any]      			=      {int(__snake_case  ): v for k, v in idalabel.items()}

				UpperCAmelCase_					:Optional[Any]      			=      idalabel
				UpperCAmelCase_					:int      			=      {v: k for k, v in idalabel.items()}

				return config



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				UpperCAmelCase_					:Tuple      			=      Image.open(requests.get(__snake_case,    stream=__snake_case  ).raw  )
				return im



@torch.no_grad()
def 				a (						__snake_case     :				Dict,    __snake_case     :				Optional[Any],    __snake_case     :				List[str],    __snake_case     :				Optional[int]=False  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      get_mobilenet_va_config(__snake_case  )

				# Load 🤗 model
				if model_name.startswith('''deeplabv3_'''  ):
								UpperCAmelCase_					:Union[str, Any]      			=      MobileNetVaForSemanticSegmentation(__snake_case  ).eval()
				else:
								UpperCAmelCase_					:Union[str, Any]      			=      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 MobileNetV2ImageProcessor
				UpperCAmelCase_					:Any      			=      MobileNetVaImageProcessor(
				    crop_size={'''width''': config.image_size, '''height''': config.image_size},    size={'''shortest_edge''': config.image_size + 32},    )
				UpperCAmelCase_					:str      			=      image_processor(images=prepare_img(),    return_tensors='''pt'''  )
				UpperCAmelCase_					:int      			=      model(**__snake_case  )
				UpperCAmelCase_					:Union[str, Any]      			=      outputs.logits

				if model_name.startswith('''deeplabv3_'''  ):
								assert logits.shape == (1, 21, 65, 65)

								if model_name == "deeplabv3_mobilenet_v2_1.0_513":
												UpperCAmelCase_					:Tuple      			=      torch.tensor(
												    [
												        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
												        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
												        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
												    ]  )

								else:
												raise ValueError(f'Unknown model name: {model_name}'  )

								assert torch.allclose(logits[0, :3, :3, :3],    __snake_case,    atol=1E-4  )
				else:
								assert logits.shape == (1, 1001)

								if model_name == "mobilenet_v2_1.4_224":
												UpperCAmelCase_					:Optional[int]      			=      torch.tensor([0.0181, -1.0015, 0.4688]  )
								elif model_name == "mobilenet_v2_1.0_224":
												UpperCAmelCase_					:Dict      			=      torch.tensor([0.2445, -1.1993, 0.1905]  )
								elif model_name == "mobilenet_v2_0.75_160":
												UpperCAmelCase_					:Tuple      			=      torch.tensor([0.2482, 0.4136, 0.6669]  )
								elif model_name == "mobilenet_v2_0.35_96":
												UpperCAmelCase_					:int      			=      torch.tensor([0.1451, -0.4624, 0.7192]  )
								else:
												UpperCAmelCase_					:Union[str, Any]      			=      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...'''  )
								UpperCAmelCase_					:List[Any]      			=      '''google/''' + model_name
								image_processor.push_to_hub(__snake_case  )
								model.push_to_hub(__snake_case  )


if __name__ == "__main__":
						__lowerCamelCase								=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="mobilenet_v2_1.0_224",
						    type=str,
						    help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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."
						)

						__lowerCamelCase								=						parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)








"""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  _snake_case				:

				'''simple docstring'''




				def __init__(							self      :  str					,							snake_case      :  Optional[Any]					,							snake_case      :  str     ):
								UpperCAmelCase_					:Union[str, Any]      			=      key
								UpperCAmelCase_					:str      			=      freq





				def __str__(							self      :  Optional[Any]     ):
								return f'Node(key={self.key}, freq={self.freq})'



def 				a (						__snake_case     :				int,    __snake_case     :				Optional[int],    __snake_case     :				Tuple,    __snake_case     :				Any,    __snake_case     :				Dict,    __snake_case     :				List[str]  ):



				'''simple docstring'''





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

				UpperCAmelCase_					:List[str]      			=      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(__snake_case,    __snake_case,    __snake_case,    node - 1,    key[node],    __snake_case  )
				print_binary_search_tree(__snake_case,    __snake_case,    node + 1,    __snake_case,    key[node],    __snake_case  )



def 				a (						__snake_case     :				Optional[Any]  ):



				'''simple docstring'''





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

				UpperCAmelCase_					:List[str]      			=      len(__snake_case  )

				UpperCAmelCase_					:Any      			=      [nodes[i].key for i in range(__snake_case  )]
				UpperCAmelCase_					:int      			=      [nodes[i].freq for i in range(__snake_case  )]

				# 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.
				UpperCAmelCase_					:Dict      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
				# array
				UpperCAmelCase_					:Optional[Any]      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# stores tree roots that will be used later for constructing binary search tree
				UpperCAmelCase_					:int      			=      [[i if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]

				for interval_length in range(2,    n + 1  ):
								for i in range(n - interval_length + 1  ):
												UpperCAmelCase_					:Union[str, Any]      			=      i + interval_length - 1

												UpperCAmelCase_					:Optional[int]      			=      sys.maxsize  # set the value to "infinity"
												UpperCAmelCase_					:List[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
																UpperCAmelCase_					:Any      			=      dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																UpperCAmelCase_					:Optional[int]      			=      dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																UpperCAmelCase_					:Union[str, Any]      			=      left + total[i][j] + right

																if dp[i][j] > cost:
																				UpperCAmelCase_					:Optional[Any]      			=      cost
																				UpperCAmelCase_					:Optional[int]      			=      r

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

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



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      [Node(__snake_case,    randint(1,    50  )  ) for i in range(10,    0,    -1  )]
				find_optimal_binary_search_tree(__snake_case  )


if __name__ == "__main__":
						main()


