

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()
SCREAMING_SNAKE_CASE						:Any      =			logging.get_logger(__name__)




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		MobileNetVaConfig(layer_norm_eps=0.001       )

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

    UpperCamelCase_          =		re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"       ,		SCREAMING_SNAKE_CASE_       )
    if matches:
        UpperCamelCase_          =		float(matches[1]       )
        UpperCamelCase_          =		int(matches[2]       )

    if model_name.startswith("deeplabv3_"       ):
        UpperCamelCase_          =		8
        UpperCamelCase_          =		2_1
        UpperCamelCase_          =		"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".
        UpperCamelCase_          =		1_0_0_1
        UpperCamelCase_          =		"imagenet-1k-id2label.json"

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

    if config.num_labels == 1_0_0_1:
        UpperCamelCase_          =		{int(SCREAMING_SNAKE_CASE_       ) + 1: v for k, v in idalabel.items()}
        UpperCamelCase_          =		"background"
    else:
        UpperCamelCase_          =		{int(SCREAMING_SNAKE_CASE_       ): v for k, v in idalabel.items()}

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

    return config




def        lowerCAmelCase(	)->		Union[str, Any]:



    """simple docstring"""






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




@torch.no_grad()
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=False       )->		Optional[Any]:



    """simple docstring"""






    UpperCamelCase_          =		get_mobilenet_va_config(SCREAMING_SNAKE_CASE_       )

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

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

    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
    UpperCamelCase_          =		MobileNetVaImageProcessor(
        crop_size={"width": config.image_size, "height": config.image_size}       ,		size={"shortest_edge": config.image_size + 3_2}       ,		)
    UpperCamelCase_          =		image_processor(images=prepare_img()       ,		return_tensors="pt"       )
    UpperCamelCase_          =		model(**SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		outputs.logits

    if model_name.startswith("deeplabv3_"       ):
        assert logits.shape == (1, 2_1, 6_5, 6_5)

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            UpperCamelCase_          =		torch.tensor(
                [
                    [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                ]       )

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

        assert torch.allclose(logits[0, :3, :3, :3]       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-4       )
    else:
        assert logits.shape == (1, 1_0_0_1)

        if model_name == "mobilenet_v2_1.4_224":
            UpperCamelCase_          =		torch.tensor([0.0181, -1.0015, 0.4688]       )
        elif model_name == "mobilenet_v2_1.0_224":
            UpperCamelCase_          =		torch.tensor([0.2445, -1.1993, 0.1905]       )
        elif model_name == "mobilenet_v2_0.75_160":
            UpperCamelCase_          =		torch.tensor([0.2482, 0.4136, 0.6669]       )
        elif model_name == "mobilenet_v2_0.35_96":
            UpperCamelCase_          =		torch.tensor([0.1451, -0.4624, 0.7192]       )
        else:
            UpperCamelCase_          =		None

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

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Any      =			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."""
 )

 SCREAMING_SNAKE_CASE						:Union[str, Any]      =			parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE						:int      =			{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE						:List[Any]      =			[
     """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """YosoForMaskedLM""",
     """YosoForMultipleChoice""",
     """YosoForQuestionAnswering""",
     """YosoForSequenceClassification""",
     """YosoForTokenClassification""",
     """YosoLayer""",
     """YosoModel""",
     """YosoPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_yoso import (
      YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
      YosoForMaskedLM,
      YosoForMultipleChoice,
      YosoForQuestionAnswering,
      YosoForSequenceClassification,
      YosoForTokenClassification,
      YosoLayer,
      YosoModel,
      YosoPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE						:str      =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)
