'''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()
lowerCAmelCase_ : int						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						List[str]				)	->							Union[str, Any]:
      '''simple docstring'''


      lowercase_     =      MobileNetVaConfig(layer_norm_eps=0.0_0_1				)

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

      lowercase_     =      re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$" ,		__lowerCamelCase				)
      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(__lowerCamelCase ,		__lowerCamelCase ,		repo_type="dataset"				) ,		"r"				)				)
      lowercase_     =      {int(__lowerCamelCase				) + 1: v for k, v in idalabel.items()}
      lowercase_     =      "background"
      lowercase_     =      idalabel
      lowercase_     =      {v: k for k, v in idalabel.items()}

      return config


def __a (	)	->							List[Any]:
      '''simple docstring'''


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


@torch.no_grad()
def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						str=False				)	->							Union[str, Any]:
      '''simple docstring'''


      lowercase_     =      get_mobilenet_va_config(__lowerCamelCase				)

      # Load 🤗 model
      lowercase_     =      MobileNetVaForImageClassification(__lowerCamelCase				).eval()

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

      # 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(**__lowerCamelCase				)
      lowercase_     =      outputs.logits

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

      if model_name == "mobilenet_v1_1.0_224":
            lowercase_     =      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":
            lowercase_     =      torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]				)
      else:
            lowercase_     =      None

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

      Path(__lowerCamelCase				).mkdir(exist_ok=__lowerCamelCase				)
      print(f'Saving model {model_name} to {pytorch_dump_folder_path}'				)
      model.save_pretrained(__lowerCamelCase				)
      print(f'Saving image processor to {pytorch_dump_folder_path}'				)
      image_processor.save_pretrained(__lowerCamelCase				)

      if push_to_hub:
            print("Pushing to the hub..."				)
            lowercase_     =      "google/" + model_name
            image_processor.push_to_hub(__lowerCamelCase				)
            model.push_to_hub(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : Dict						  =      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."
       )

       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'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCAmelCase_ : Dict						  =      TypeVar("T")





class 							lowercase						(						Generic[T] ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	bool = True)      ->					None:
         lowercase_     =      {}  # dictionary of lists
         lowercase_     =      directed


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	T)      ->					GraphAdjacencyList[T]:

         if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      [source_vertex]
         else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      []
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      []

         return self


   def __repr__(   self				:	str)      ->					str:
         return pformat(self.adj_list)






