





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


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

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

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

   __SCREAMING_SNAKE_CASE   				=							re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""				,						__UpperCAmelCase   )
   if matches:
      __SCREAMING_SNAKE_CASE   				=							float(matches[1]   )
      __SCREAMING_SNAKE_CASE   				=							int(matches[2]   )

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

   return config

def     __magic_name__							(				)    ->       Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   __SCREAMING_SNAKE_CASE   				=							Image.open(requests.get(__UpperCAmelCase				,						stream=__UpperCAmelCase   ).raw   )
   return im

@torch.no_grad()
def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=False   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							get_mobilenet_va_config(__UpperCAmelCase   )

   # Load 🤗 model
   __SCREAMING_SNAKE_CASE   				=							MobileNetVaForImageClassification(__UpperCAmelCase   ).eval()

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

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

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   )
   elif model_name == "mobilenet_v1_0.75_192":
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]   )
   else:
      __SCREAMING_SNAKE_CASE   				=							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..."""   )
      __SCREAMING_SNAKE_CASE   				=							"""google/""" + model_name
      image_processor.push_to_hub(__UpperCAmelCase   )
      model.push_to_hub(__UpperCAmelCase   )


if __name__ == "__main__":
    a   	=    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."
    )

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












from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a__: Tuple							   =						TypeVar('T')


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (position - 1) // 2


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 1


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 2




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		[]
        A__		   =		{}
        A__		   =		0



 def __len__(				self					):
        return self.elements



 def __repr__(				self					):
        return str(self.heap					)



 def 							UpperCamelCase     (				self					):
        # Check if the priority queue is empty
        return self.elements == 0



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Add an element with given priority to the queue
        self.heap.append((elem, weight)					)
        A__		   =		self.elements
        self.elements += 1
        self._bubble_up(__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        # Remove and return the element with lowest weight (highest priority)
        if self.elements > 1:
               self._swap_nodes(0,self.elements - 1					)
        A__   ,      A__		   =		self.heap.pop()
        del self.position_map[elem]
        self.elements -= 1
        if self.elements > 0:
               A__   ,      A__		   =		self.heap[0]
               self._bubble_down(__lowerCamelCase					)
        return elem



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Update the weight of the given key
        A__		   =		self.position_map[elem]
        A__		   =		(elem, weight)
        if position > 0:
               A__		   =		get_parent_position(__lowerCamelCase					)
               A__   ,      A__		   =		self.heap[parent_position]
               if parent_weight > weight:
                      self._bubble_up(__lowerCamelCase					)
               else:
                      self._bubble_down(__lowerCamelCase					)
        else:
               self._bubble_down(__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (upward movement) [to be used internally
        # only]
        A__		   =		self.position_map[elem]
        if curr_pos == 0:
               return None
        A__		   =		get_parent_position(__lowerCamelCase					)
        A__   ,      A__		   =		self.heap[curr_pos]
        A__   ,      A__		   =		self.heap[parent_position]
        if parent_weight > weight:
               self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
               return self._bubble_up(__lowerCamelCase					)
        return None



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (downward movement) [to be used
        # internally only]
        A__		   =		self.position_map[elem]
        A__   ,      A__		   =		self.heap[curr_pos]
        A__		   =		get_child_left_position(__lowerCamelCase					)
        A__		   =		get_child_right_position(__lowerCamelCase					)
        if child_left_position < self.elements and child_right_position < self.elements:
               A__   ,      A__		   =		self.heap[child_left_position]
               A__   ,      A__		   =		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:
               A__   ,      A__		   =		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:
               A__   ,      A__		   =		self.heap[child_right_position]
               if child_right_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        return None




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Swap the nodes at the given positions
        A__		   =		self.heap[nodea_pos][0]
        A__		   =		self.heap[nodea_pos][0]
        A__   ,      A__		   =		(
            self.heap[nodea_pos],
            self.heap[nodea_pos],
        )
        A__		   =		nodea_pos
        A__		   =		nodea_pos




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		{}
        A__		   =		0



 def __repr__(				self					):
        return str(self.connections					)



 def __len__(				self					):
        return self.nodes



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Add a node in the graph if it is not in the graph
        if node not in self.connections:
               A__		   =		{}
               self.nodes += 1




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        # Add an edge between 2 nodes in the graph
        self.add_node(__lowerCamelCase					)
        self.add_node(__lowerCamelCase					)
        A__		   =		weight
        A__		   =		weight


def       UpperCamelCase__(			UpperCamelCase__     :			GraphUndirectedWeighted[T]					,	)->tuple[dict[T, int], dict[T, T | None]]:
       A__		   =		{node: maxsize for node in graph.connections}
       A__		   =		{node: None for node in graph.connections}

       A__		   =		MinPriorityQueue()
       for node, weight in dist.items():
              priority_queue.push(UpperCamelCase__					,	UpperCamelCase__    )

       if priority_queue.is_empty():
              return dist, parent

       # initialization
       A__		   =		priority_queue.extract_min()
       A__		   =		0
       for neighbour in graph.connections[node]:
              if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                     A__		   =		dist[node] + graph.connections[node][neighbour]
                     priority_queue.update_key(UpperCamelCase__					,	dist[neighbour]    )
                     A__		   =		node

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





