




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							:				Optional[Any]			     =	logging.get_logger(__name__)
def     snake_case_  (       a__		:     List[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_v2_([^_]*)_([^_]*)$"""   ,lowerCamelCase_      )
    if matches:
        __lowercase          =						float(matches[1]      )
        __lowercase          =						int(matches[2]      )

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

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

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

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

    return config
def     snake_case_  (       ):



    """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     snake_case_  (       a__		:     Optional[int]   ,a__		:     str   ,a__		:     int   ,a__		:     Optional[Any]=False      ):



    """simple docstring"""




    __lowercase          =						get_mobilenet_va_config(lowerCamelCase_      )

    # Load 🤗 model
    if model_name.startswith("""deeplabv3_"""      ):
        __lowercase          =						MobileNetVaForSemanticSegmentation(lowerCamelCase_      ).eval()
    else:
        __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 MobileNetV2ImageProcessor
    __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

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

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            __lowercase          =						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]   ,lowerCamelCase_   ,atol=1e-4      )
    else:
        assert logits.shape == (1, 10_01)

        if model_name == "mobilenet_v2_1.4_224":
            __lowercase          =						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":
            __lowercase          =						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":
            __lowercase          =						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":
            __lowercase          =						torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]      )
        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__":
  A							:				Optional[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."""
  )

  A							:				int			     =	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
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir






@dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					str






class 	SCREAMING_SNAKE_CASE(    unittest.TestCase					):

       def    snake_case__      (   self					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece.model"""					)
           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model"""					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		0					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		1					)

           self.assertIn(
               """The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"""
               """ which is not implemented in the fast tokenizers.""" ,		str(w[0].message					) ,		)




