

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 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__						       =    MobileNetVaConfig(layer_norm_eps=0.0_01			)

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

     __magic_name__						       =    re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''		,						a			)
     if matches:
          __magic_name__						       =    float(matches[1]			)
          __magic_name__						       =    int(matches[2]			)

     # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
     # the usual 1000. The first class (index 0) is "background".
     __magic_name__						       =    1001
     __magic_name__						       =    '''imagenet-1k-id2label.json'''
     __magic_name__						       =    '''huggingface/label-files'''
     __magic_name__						       =    json.load(open(hf_hub_download(a		,						a		,						repo_type='''dataset'''			)		,						'''r'''			)			)
     __magic_name__						       =    {int(a			) + 1: v for k, v in idalabel.items()}
     __magic_name__						       =    '''background'''
     __magic_name__						       =    idalabel
     __magic_name__						       =    {v: k for k, v in idalabel.items()}

     return config




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






     '''simple docstring'''
     __magic_name__						       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __magic_name__						       =    Image.open(requests.get(a		,						stream=a			).raw			)
     return im




@torch.no_grad()
def 						UpperCamelCase	(			a		,						a		,						a		,						a=False			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    get_mobilenet_va_config(a			)

     # Load 🤗 model
     __magic_name__						       =    MobileNetVaForImageClassification(a			).eval()

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

     # Check outputs on an image, prepared by MobileNetV1ImageProcessor
     __magic_name__						       =    MobileNetVaImageProcessor(
         crop_size={'''width''': config.image_size, '''height''': config.image_size}		,						size={'''shortest_edge''': config.image_size + 32}		,						)
     __magic_name__						       =    image_processor(images=prepare_img()		,						return_tensors='''pt'''			)
     __magic_name__						       =    model(**a			)
     __magic_name__						       =    outputs.logits

     assert logits.shape == (1, 1001)

     if model_name == "mobilenet_v1_1.0_224":
          __magic_name__						       =    torch.tensor([-4.17_39, -1.12_33, 3.12_05]			)
     elif model_name == "mobilenet_v1_0.75_192":
          __magic_name__						       =    torch.tensor([-3.94_40, -2.31_41, -0.33_33]			)
     else:
          __magic_name__						       =    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...'''			)
          __magic_name__						       =    '''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_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
      )









'''simple docstring'''




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Optional[Any]			,				a__		:   Any			,				a__		:   Tuple=sys.maxsize      ):
      __magic_name__						       =    '''bilinear'''
      __magic_name__						       =    max_size
      __magic_name__						       =    short_edge_length



 def __call__(   self		:   Tuple			,				a__		:   List[str]      ):
      __magic_name__						       =    []
      for img in imgs:
           __magic_name__       ,  __magic_name__						       =    img.shape[:2]
           # later: provide list and randomly choose index for resize
           __magic_name__						       =    np.random.randint(self.short_edge_length[0]			,				self.short_edge_length[1] + 1      )
           if size == 0:
                return img
           __magic_name__						       =    size * 1.0 / min(a__			,				a__      )
           if h < w:
                __magic_name__       ,  __magic_name__						       =    size, scale * w
           else:
                __magic_name__       ,  __magic_name__						       =    scale * h, size
           if max(a__			,				a__      ) > self.max_size:
                __magic_name__						       =    self.max_size * 1.0 / max(a__			,				a__      )
                __magic_name__						       =    newh * scale
                __magic_name__						       =    neww * scale
           __magic_name__						       =    int(neww + 0.5      )
           __magic_name__						       =    int(newh + 0.5      )

           if img.dtype == np.uinta:
                __magic_name__						       =    Image.fromarray(a__      )
                __magic_name__						       =    pil_image.resize((neww, newh)			,				PILImageResampling.BILINEAR      )
                __magic_name__						       =    np.asarray(a__      )
           else:
                __magic_name__						       =    img.permute(2			,				0			,				1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
                __magic_name__						       =    nn.functional.interpolate(
                    a__			,				(newh, neww)			,				mode=self.interp_method			,				align_corners=a__      ).squeeze(0      )
           img_augs.append(a__      )

      return img_augs






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]			,				a__		:   Tuple      ):
      __magic_name__						       =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,				cfg.INPUT.MAX_SIZE_TEST      )
      __magic_name__						       =    cfg.INPUT.FORMAT
      __magic_name__						       =    cfg.SIZE_DIVISIBILITY
      __magic_name__						       =    cfg.PAD_VALUE
      __magic_name__						       =    cfg.INPUT.MAX_SIZE_TEST
      __magic_name__						       =    cfg.MODEL.DEVICE
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    lambda a__      : (x - self.pixel_mean) / self.pixel_std



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Dict      ):
      __magic_name__						       =    tuple(max(a__      ) for s in zip(*[img.shape for img in images]      )      )
      __magic_name__						       =    [im.shape[-2:] for im in images]
      __magic_name__						       =    [
          nn.functional.pad(
              a__			,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,				value=self.pad_value			,				)
          for size, im in zip(a__			,				a__      )
      ]

      return torch.stack(a__      ), torch.tensor(a__      )



 def __call__(   self		:   Dict			,				a__		:   Dict			,				a__		:   List[str]=False      ):
      with torch.no_grad():
           if not isinstance(a__			,				a__      ):
                __magic_name__						       =    [images]
           if single_image:
                assert len(a__      ) == 1
           for i in range(len(a__      )      ):
                if isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(a__			,				images.pop(a__      ).to(self.device      ).float()      )
                elif not isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(
                         a__			,				torch.as_tensor(img_tensorize(images.pop(a__      )			,				input_format=self.input_format      )      )
                         .to(self.device      )
                         .float()			,				)
            # resize smallest edge
           __magic_name__						       =    torch.tensor([im.shape[:2] for im in images]      )
           __magic_name__						       =    self.aug(a__      )
           # transpose images and convert to torch tensors
           # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
           # now normalize before pad to avoid useless arithmetic
           __magic_name__						       =    [self.normalizer(a__      ) for x in images]
           # now pad them to do the following operations
           __magic_name__       ,  __magic_name__						       =    self.pad(a__      )
           # Normalize

           if self.size_divisibility > 0:
                raise NotImplementedError()
           # pad
           __magic_name__						       =    torch.true_divide(a__			,				a__      )
           if single_image:
                return images[0], sizes[0], scales_yx[0]
           else:
                return images, sizes, scales_yx




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






     '''simple docstring'''
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes




def 						UpperCamelCase	(			a		,						a			)				->     Any:






     '''simple docstring'''
     assert torch.isfinite(a			).all(), "Box tensor contains infinite or NaN!"
     __magic_name__       ,  __magic_name__						       =    box_size
     tensor[:, 0].clamp_(min=0		,						max=a			)
     tensor[:, 1].clamp_(min=0		,						max=a			)
     tensor[:, 2].clamp_(min=0		,						max=a			)
     tensor[:, 3].clamp_(min=0		,						max=a			)






