
'''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()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Tuple      ):
      snake_case_    							=				MobileNetVaConfig(layer_norm_eps=0.0_0_1      )

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

      snake_case_    							=				re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"			,       _a      )
      if matches:
            snake_case_    							=				float(matches[1]      )
            snake_case_    							=				int(matches[2]      )

      if model_name.startswith("deeplabv3_"      ):
            snake_case_    							=				8
            snake_case_    							=				21
            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".
            snake_case_    							=				1_001
            snake_case_    							=				"imagenet-1k-id2label.json"

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

      if config.num_labels == 1_001:
            snake_case_    							=				{int(_a      ) + 1: v for k, v in idalabel.items()}
            snake_case_    							=				"background"
      else:
            snake_case_    							=				{int(_a      ): v for k, v in idalabel.items()}

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

      return config
def 	snake_case_							(				):
      snake_case_    							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
      snake_case_    							=				Image.open(requests.get(_a			,       stream=_a      ).raw      )
      return im
@torch.no_grad()
def 	snake_case_							(				_a  :				Any			,       _a  :				Dict			,       _a  :				Union[str, Any]			,       _a  :				Any=False      ):
      snake_case_    							=				get_mobilenet_va_config(_a      )

      # Load 🤗 model
      if model_name.startswith("deeplabv3_"      ):
            snake_case_    							=				MobileNetVaForSemanticSegmentation(_a      ).eval()
      else:
            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
      snake_case_    							=				MobileNetVaImageProcessor(
          crop_size={"width": config.image_size, "height": config.image_size}			,       size={"shortest_edge": config.image_size + 32}			,       )
      snake_case_    							=				image_processor(images=prepare_img()			,       return_tensors="pt"      )
      snake_case_    							=				model(**_a      )
      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":
                  snake_case_    							=				torch.tensor(
                      [
                          [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                          [[-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, 1_001)

            if model_name == "mobilenet_v2_1.4_224":
                  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":
                  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":
                  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":
                  snake_case_    							=				torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]      )
            else:
                  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..."      )
            snake_case_    							=				"google/" + model_name
            image_processor.push_to_hub(_a      )
            model.push_to_hub(_a      )


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

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






'''simple docstring'''



lowerCAmelCase						:		Tuple						      = '''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

lowerCAmelCase						:		Tuple						      = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]