



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						    =  logging.get_logger(__name__)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	MobileNetVaConfig(layer_norm_eps=0.001     )

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

 __SCREAMING_SNAKE_CASE       :    int       =	re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"		,	__lowerCamelCase     )
 if matches:
  __SCREAMING_SNAKE_CASE       :    int       =	float(matches[1]     )
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	int(matches[2]     )

 # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
 # the usual 1000. The first class (index 0) is "background".
 __SCREAMING_SNAKE_CASE       :    Dict       =	1001
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"imagenet-1k-id2label.json"
 __SCREAMING_SNAKE_CASE       :    Dict       =	"huggingface/label-files"
 __SCREAMING_SNAKE_CASE       :    Dict       =	json.load(open(hf_hub_download(__lowerCamelCase		,	__lowerCamelCase		,	repo_type="dataset"     )		,	"r"     )     )
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{int(__lowerCamelCase     ) + 1: v for k, v in idalabel.items()}
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"background"
 __SCREAMING_SNAKE_CASE       :    Tuple       =	idalabel
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{v: k for k, v in idalabel.items()}

 return config






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Dict       =	"http://images.cocodataset.org/val2017/000000039769.jpg"
 __SCREAMING_SNAKE_CASE       :    List[str]       =	Image.open(requests.get(__lowerCamelCase		,	stream=__lowerCamelCase     ).raw     )
 return im






@torch.no_grad()
def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     List[str]=False     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	get_mobilenet_va_config(__lowerCamelCase     )

 # Load 🤗 model
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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
 __SCREAMING_SNAKE_CASE       :    Any       =	MobileNetVaImageProcessor(
     crop_size={"width": config.image_size, "height": config.image_size}		,	size={"shortest_edge": config.image_size + 32}		,	)
 __SCREAMING_SNAKE_CASE       :    Any       =	image_processor(images=prepare_img()		,	return_tensors="pt"     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	model(**__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	outputs.logits

 assert logits.shape == (1, 1001)

 if model_name == "mobilenet_v1_1.0_224":
  __SCREAMING_SNAKE_CASE       :    str       =	torch.tensor([-4.1739, -1.1233, 3.1205]     )
 elif model_name == "mobilenet_v1_0.75_192":
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	torch.tensor([-3.9440, -2.3141, -0.3333]     )
 else:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	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..."     )
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	"google/" + model_name
  image_processor.push_to_hub(__lowerCamelCase     )
  model.push_to_hub(__lowerCamelCase     )


if __name__ == "__main__":
      _lowerCamelCase						    =  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						    =  parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )











from diffusers.utils.testing_utils import require_onnxruntime

@require_onnxruntime
class       _SCREAMING_SNAKE_CASE	:
     pass






