





'''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__      :   str      	=			logging.get_logger(__name__)


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		__UpperCAmelCase	:							int     =     MobileNetVaConfig(layer_norm_eps=0.001  )

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

		__UpperCAmelCase	:							Dict     =     re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$",       _UpperCAmelCase  )
		if matches:
				__UpperCAmelCase	:							Union[str, Any]     =     float(matches[1]  )
				__UpperCAmelCase	:							Tuple     =     int(matches[2]  )

		if model_name.startswith("deeplabv3_"  ):
				__UpperCAmelCase	:							Tuple     =     8
				__UpperCAmelCase	:							List[str]     =     21
				__UpperCAmelCase	:							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	:							Optional[Any]     =     1001
				__UpperCAmelCase	:							Union[str, Any]     =     "imagenet-1k-id2label.json"

		__UpperCAmelCase	:							int     =     "huggingface/label-files"
		__UpperCAmelCase	:							Dict     =     json.load(open(hf_hub_download(_UpperCAmelCase,       _UpperCAmelCase,       repo_type="dataset"  ),       "r"  )  )

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

		__UpperCAmelCase	:							Tuple     =     idalabel
		__UpperCAmelCase	:							List[Any]     =     {v: k for k, v in idalabel.items()}

		return config


def 						__UpperCamelCase						(			):
		__UpperCAmelCase	:							List[Any]     =     "http://images.cocodataset.org/val2017/000000039769.jpg"
		__UpperCAmelCase	:							Union[str, Any]     =     Image.open(requests.get(_UpperCAmelCase,       stream=_UpperCAmelCase  ).raw  )
		return im


@torch.no_grad()
def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase=False  ):
		__UpperCAmelCase	:							Tuple     =     get_mobilenet_va_config(_UpperCAmelCase  )

		# Load 🤗 model
		if model_name.startswith("deeplabv3_"  ):
				__UpperCAmelCase	:							Tuple     =     MobileNetVaForSemanticSegmentation(_UpperCAmelCase  ).eval()
		else:
				__UpperCAmelCase	:							str     =     MobileNetVaForImageClassification(_UpperCAmelCase  ).eval()

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

		# Check outputs on an image, prepared by MobileNetV2ImageProcessor
		__UpperCAmelCase	:							Tuple     =     MobileNetVaImageProcessor(
		    crop_size={"width": config.image_size, "height": config.image_size},       size={"shortest_edge": config.image_size + 32},       )
		__UpperCAmelCase	:							Optional[int]     =     image_processor(images=prepare_img(),       return_tensors="pt"  )
		__UpperCAmelCase	:							List[Any]     =     model(**_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     outputs.logits

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

				if model_name == "deeplabv3_mobilenet_v2_1.0_513":
						__UpperCAmelCase	:							str     =     torch.tensor(
						    [
						        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
						        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
						        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
						    ]  )

				else:
						raise ValueError(F"Unknown model name: {model_name}"  )

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

				if model_name == "mobilenet_v2_1.4_224":
						__UpperCAmelCase	:							Any     =     torch.tensor([0.0_181, -1.0_015, 0.4_688]  )
				elif model_name == "mobilenet_v2_1.0_224":
						__UpperCAmelCase	:							List[Any]     =     torch.tensor([0.2_445, -1.1_993, 0.1_905]  )
				elif model_name == "mobilenet_v2_0.75_160":
						__UpperCAmelCase	:							Optional[Any]     =     torch.tensor([0.2_482, 0.4_136, 0.6_669]  )
				elif model_name == "mobilenet_v2_0.35_96":
						__UpperCAmelCase	:							str     =     torch.tensor([0.1_451, -0.4_624, 0.7_192]  )
				else:
						__UpperCAmelCase	:							List[Any]     =     None

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

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

		if push_to_hub:
				print("Pushing to the hub..."  )
				__UpperCAmelCase	:							Dict     =     "google/" + model_name
				image_processor.push_to_hub(_UpperCAmelCase  )
				model.push_to_hub(_UpperCAmelCase  )


if __name__ == "__main__":
			lowerCAmelCase__      :   int      	=			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__      :   str      	=			parser.parse_args()
			convert_movilevit_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)











'''simple docstring'''

class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      Tuple		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     val
							# Number of nodes in left subtree
							__UpperCAmelCase	:							Dict     =     0
							__UpperCAmelCase	:							int     =     None
							__UpperCAmelCase	:							Optional[int]     =     None
							__UpperCAmelCase	:							List[str]     =     None






					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      Optional[Any]		):







							"""simple docstring"""
							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

							if self.val < other.val:
									__UpperCAmelCase	:							str     =     self.right
									__UpperCAmelCase	:							Tuple     =     None
									if self.right:
											__UpperCAmelCase	:							int     =     other
									__UpperCAmelCase	:							List[str]     =     other
									__UpperCAmelCase	:							Any     =     self.left_tree_size * 2 + 1
									return self
							else:
									__UpperCAmelCase	:							List[Any]     =     other.right
									__UpperCAmelCase	:							Union[str, Any]     =     None
									if other.right:
											__UpperCAmelCase	:							List[str]     =     self
									__UpperCAmelCase	:							Optional[Any]     =     self
									__UpperCAmelCase	:							Any     =     other.left_tree_size * 2 + 1
									return other






class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Optional[int]=0		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     heap_size
							__UpperCAmelCase	:							Any     =     bottom_root
							__UpperCAmelCase	:							List[str]     =     min_node
					def 		lowerCamelCase_   (   self       :      List[str]		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""

							# Empty heaps corner cases
							if other.size == 0:
									return None
							if self.size == 0:
									__UpperCAmelCase	:							Dict     =     other.size
									__UpperCAmelCase	:							Optional[Any]     =     other.bottom_root
									__UpperCAmelCase	:							str     =     other.min_node
									return None
							# Update size
							__UpperCAmelCase	:							str     =     self.size + other.size

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

							# Order roots by left_subtree_size
							__UpperCAmelCase	:							int     =     []
							__UpperCAmelCase  ,			__UpperCAmelCase	:							List[str]     =     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)		)
											__UpperCAmelCase	:							Optional[Any]     =     i.parent
									else:
											combined_roots_list.append((j, False)		)
											__UpperCAmelCase	:							Tuple     =     j.parent
        # Insert links between them
							for i in range(len(UpperCAmelCase_		) - 1		):
									if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
											__UpperCAmelCase	:							Optional[Any]     =     combined_roots_list[i + 1][0]
											__UpperCAmelCase	:							Any     =     combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							__UpperCAmelCase	:							List[str]     =     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
											__UpperCAmelCase	:							List[str]     =     i.left
											__UpperCAmelCase	:							Dict     =     i.parent.parent

											# Merging trees
											__UpperCAmelCase	:							Optional[int]     =     i.merge_trees(i.parent		)

											# Updating links
											__UpperCAmelCase	:							Dict     =     previous_node
											__UpperCAmelCase	:							Union[str, Any]     =     next_node
											if previous_node:
													__UpperCAmelCase	:							List[str]     =     i
											if next_node:
													__UpperCAmelCase	:							Dict     =     i
									else:
											__UpperCAmelCase	:							Optional[int]     =     i.parent
        # Updating self.bottom_root
							while i.left:
									__UpperCAmelCase	:							str     =     i.left
							__UpperCAmelCase	:							List[Any]     =     i

							# Update other
							__UpperCAmelCase	:							int     =     self.size
							__UpperCAmelCase	:							Dict     =     self.bottom_root
							__UpperCAmelCase	:							Union[str, Any]     =     self.min_node

							# Return the merged heap
							return self
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      Optional[int]		):







							"""simple docstring"""
							if self.size == 0:
									__UpperCAmelCase	:							Optional[Any]     =     Node(UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[Any]     =     1
									__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root
							else:
									# Create new node
									__UpperCAmelCase	:							Optional[Any]     =     Node(UpperCAmelCase_		)

									# Update size
									self.size += 1

									# update min_node
									if val < self.min_node.val:
											__UpperCAmelCase	:							Optional[int]     =     new_node
									# Put new_node as a bottom_root in heap
									__UpperCAmelCase	:							Union[str, Any]     =     new_node
									__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root
									__UpperCAmelCase	:							List[str]     =     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
											__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root.parent.parent

											# Merge
											__UpperCAmelCase	:							str     =     self.bottom_root.merge_trees(self.bottom_root.parent		)

											# Update Links
											__UpperCAmelCase	:							int     =     next_node
											__UpperCAmelCase	:							Any     =     None
											if next_node:
													__UpperCAmelCase	:							Any     =     self.bottom_root
					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							return self.min_node.val
					def 		lowerCamelCase_   (   self       :      str		):







							"""simple docstring"""
							return self.size == 0
					def 		lowerCamelCase_   (   self       :      int		):







							"""simple docstring"""
							__UpperCAmelCase	:							str     =     self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
									# Update size
									__UpperCAmelCase	:							Optional[int]     =     0

									# Update bottom root
									__UpperCAmelCase	:							List[str]     =     None

									# Update min_node
									__UpperCAmelCase	:							int     =     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
									__UpperCAmelCase	:							Union[str, Any]     =     self.bottom_root.parent
									__UpperCAmelCase	:							str     =     None

									# Update min_node
									__UpperCAmelCase	:							int     =     self.bottom_root
									__UpperCAmelCase	:							int     =     self.bottom_root.parent
									while i:
											if i.val < self.min_node.val:
													__UpperCAmelCase	:							Union[str, Any]     =     i
											__UpperCAmelCase	:							Optional[int]     =     i.parent
									return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							__UpperCAmelCase	:							Optional[int]     =     self.min_node.right
							__UpperCAmelCase	:							List[Any]     =     None
							__UpperCAmelCase	:							Optional[int]     =     bottom_of_new
							__UpperCAmelCase	:							Union[str, Any]     =     1

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

							# Update size
							__UpperCAmelCase	:							Any     =     self.size - 1 - size_of_new

							# Neighbour nodes
							__UpperCAmelCase	:							Union[str, Any]     =     self.min_node.left
							__UpperCAmelCase	:							List[str]     =     self.min_node.parent

							# Initialize new bottom_root and min_node
							__UpperCAmelCase	:							Optional[Any]     =     previous_node or next_node
							__UpperCAmelCase	:							Optional[Any]     =     next_node

							# Update links of previous_node and search below for new min_node and
							# bottom_root
							if previous_node:
									__UpperCAmelCase	:							List[str]     =     next_node

									# Update bottom_root and search for min_node below
									__UpperCAmelCase	:							List[str]     =     previous_node
									__UpperCAmelCase	:							int     =     previous_node
									while self.bottom_root.left:
											__UpperCAmelCase	:							Dict     =     self.bottom_root.left
											if self.bottom_root.val < self.min_node.val:
													__UpperCAmelCase	:							Union[str, Any]     =     self.bottom_root
							if next_node:
									__UpperCAmelCase	:							Tuple     =     previous_node

									# Search for new min_node above min_node
									__UpperCAmelCase	:							Optional[int]     =     next_node
									while i:
											if i.val < self.min_node.val:
													__UpperCAmelCase	:							List[Any]     =     i
											__UpperCAmelCase	:							List[Any]     =     i.parent
        # Merge heaps
							self.merge_heaps(UpperCAmelCase_		)

							return min_value
					def 		lowerCamelCase_   (   self       :      Union[str, Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Tuple     =     self.bottom_root
							while top_root.parent:
									__UpperCAmelCase	:							Optional[int]     =     top_root.parent
							# preorder
							__UpperCAmelCase	:							Union[str, Any]     =     []
							self.__traversal(UpperCAmelCase_		,	UpperCAmelCase_		)
							return heap_pre_order
					def 		lowerCamelCase_   (   self       :      Optional[int]		,	UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      Dict=0		):







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






					def __str__(   self       :      Optional[int]		):







							"""simple docstring"""
							if self.is_empty():
									return ""
							__UpperCAmelCase	:							Optional[Any]     =     self.pre_order()

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


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

			doctest.testmod()




