




'''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 				SCREAMING_SNAKE_CASE_				(							__A       :				Dict						)				->				Union[str, Any]:
   _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_v2_([^_]*)_([^_]*)$" ,  __A						)
   if matches:
      _SCREAMING_SNAKE_CASE =   float(matches[1]						)
      _SCREAMING_SNAKE_CASE =   int(matches[2]						)

   if model_name.startswith("deeplabv3_"						):
      _SCREAMING_SNAKE_CASE =   8
      _SCREAMING_SNAKE_CASE =   21
      _SCREAMING_SNAKE_CASE =   "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".
      _SCREAMING_SNAKE_CASE =   10_01
      _SCREAMING_SNAKE_CASE =   "imagenet-1k-id2label.json"

   _SCREAMING_SNAKE_CASE =   "huggingface/label-files"
   _SCREAMING_SNAKE_CASE =   json.load(open(hf_hub_download(__A ,  __A ,  repo_type="dataset"						) ,  "r"						)						)

   if config.num_labels == 10_01:
      _SCREAMING_SNAKE_CASE =   {int(__A						) + 1: v for k, v in idalabel.items()}
      _SCREAMING_SNAKE_CASE =   "background"
   else:
      _SCREAMING_SNAKE_CASE =   {int(__A						): v for k, v in idalabel.items()}

   _SCREAMING_SNAKE_CASE =   idalabel
   _SCREAMING_SNAKE_CASE =   {v: k for k, v in idalabel.items()}

   return config
def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   "http://images.cocodataset.org/val2017/000000039769.jpg"
   _SCREAMING_SNAKE_CASE =   Image.open(requests.get(__A ,  stream=__A						).raw						)
   return im
@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_				(							__A       :				str ,  __A       :				List[str] ,  __A       :				int ,  __A       :				List[str]=False						)				->				str:
   _SCREAMING_SNAKE_CASE =   get_mobilenet_va_config(__A						)

   # Load 🤗 model
   if model_name.startswith("deeplabv3_"						):
      _SCREAMING_SNAKE_CASE =   MobileNetVaForSemanticSegmentation(__A						).eval()
   else:
      _SCREAMING_SNAKE_CASE =   MobileNetVaForImageClassification(__A						).eval()

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

   # Check outputs on an image, prepared by MobileNetV2ImageProcessor
   _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(**__A						)
   _SCREAMING_SNAKE_CASE =   outputs.logits

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

      if model_name == "deeplabv3_mobilenet_v2_1.0_513":
         _SCREAMING_SNAKE_CASE =   torch.tensor(
             [
                 [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                 [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                 [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
             ]						)

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

      assert torch.allclose(logits[0, :3, :3, :3] ,  __A ,  atol=1e-4						)
   else:
      assert logits.shape == (1, 10_01)

      if model_name == "mobilenet_v2_1.4_224":
         _SCREAMING_SNAKE_CASE =   torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]						)
      elif model_name == "mobilenet_v2_1.0_224":
         _SCREAMING_SNAKE_CASE =   torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]						)
      elif model_name == "mobilenet_v2_0.75_160":
         _SCREAMING_SNAKE_CASE =   torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]						)
      elif model_name == "mobilenet_v2_0.35_96":
         _SCREAMING_SNAKE_CASE =   torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]						)
      else:
         _SCREAMING_SNAKE_CASE =   None

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

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

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


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'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils







class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @patch("diffusers.utils.hub_utils.get_full_repo_name"					)
   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Mock					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "full_repo_name"
      with TemporaryDirectory() as tmpdir:
         # Dummy args values
         _SCREAMING_SNAKE_CASE =   Mock()
         _SCREAMING_SNAKE_CASE =   tmpdir
         _SCREAMING_SNAKE_CASE =   0
         _SCREAMING_SNAKE_CASE =   "hub_token"
         _SCREAMING_SNAKE_CASE =   "dataset_name"
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0_0
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   0.0_2
         _SCREAMING_SNAKE_CASE =   0.0_3
         _SCREAMING_SNAKE_CASE =   0.0_0_0_5
         _SCREAMING_SNAKE_CASE =   0.0_0_0_0_0_1
         _SCREAMING_SNAKE_CASE =   1
         _SCREAMING_SNAKE_CASE =   1_0
         _SCREAMING_SNAKE_CASE =   0.0_0_1
         _SCREAMING_SNAKE_CASE =   0.1
         _SCREAMING_SNAKE_CASE =   0.2
         _SCREAMING_SNAKE_CASE =   True

         # Model card mush be rendered and saved
         diffusers.utils.hub_utils.create_model_card(__lowerCamelCase						,   model_name="model_name"					)
         self.assertTrue((Path(__lowerCamelCase					) / "README.md").is_file()					)

