






'''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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :							Tuple         =   logging.get_logger(__name__)



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     MobileNetVaConfig(layer_norm_eps=0.0_0_1     )

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

   __a					  =     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$',       SCREAMING_SNAKE_CASE__     )
   if matches:
      __a					  =     float(matches[1]     )
      __a					  =     int(matches[2]     )

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

   return config



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

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



@torch.no_grad()
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str=False     )	->      int:

   """simple docstring"""
   __a					  =     get_mobilenet_va_config(SCREAMING_SNAKE_CASE__     )

   # Load 🤗 model
   __a					  =     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 MobileNetV1ImageProcessor
   __a					  =     MobileNetVaImageProcessor(
       crop_size={'width': config.image_size, 'height': config.image_size},       size={'shortest_edge': config.image_size + 32},       )
   __a					  =     image_processor(images=prepare_img(),       return_tensors='pt'     )
   __a					  =     model(**SCREAMING_SNAKE_CASE__     )
   __a					  =     outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      __a					  =     torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]     )
   elif model_name == "mobilenet_v1_0.75_192":
      __a					  =     torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]     )
   else:
      __a					  =     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...'     )
      __a					  =     'google/' + model_name
      image_processor.push_to_hub(SCREAMING_SNAKE_CASE__     )
      model.push_to_hub(SCREAMING_SNAKE_CASE__     )


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

 __UpperCamelCase   :							Any         =   parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )







'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase   :							Dict         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["pixel_values"]




      def __init__(	self			, lowerCamelCase = True			, lowerCamelCase = 32			, lowerCamelCase=PILImageResampling.BILINEAR			, lowerCamelCase = True			, **lowerCamelCase			, )				->None:



         '''simple docstring'''
         __a					  =     do_resize
         __a					  =     do_rescale
         __a					  =     size_divisor
         __a					  =     resample
         super().__init__(**lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         __a  ,      __a					  =     get_image_size(lowerCamelCase   )
         # Rounds the height and width down to the closest multiple of size_divisor
         __a					  =     height // size_divisor * size_divisor
         __a					  =     width // size_divisor * size_divisor
         __a					  =     resize(lowerCamelCase			, (new_h, new_w)			, resample=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )
         return image




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         return rescale(image=lowerCamelCase			, scale=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase=None			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = ChannelDimension.FIRST			, **lowerCamelCase			, )				->BatchFeature:



         '''simple docstring'''
         __a					  =     do_resize if do_resize is not None else self.do_resize
         __a					  =     do_rescale if do_rescale is not None else self.do_rescale
         __a					  =     size_divisor if size_divisor is not None else self.size_divisor
         __a					  =     resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
            raise ValueError('size_divisor is required for resizing'   )

         __a					  =     make_list_of_images(lowerCamelCase   )

         if not valid_images(lowerCamelCase   ):
            raise ValueError('Invalid image(s)'   )

         # All transformations expect numpy arrays.
         __a					  =     [to_numpy_array(lowerCamelCase   ) for img in images]

         if do_resize:
            __a					  =     [self.resize(lowerCamelCase			, size_divisor=lowerCamelCase			, resample=lowerCamelCase   ) for image in images]

         if do_rescale:
            __a					  =     [self.rescale(lowerCamelCase			, scale=1 / 255   ) for image in images]

         __a					  =     [to_channel_dimension_format(lowerCamelCase			, lowerCamelCase   ) for image in images]

         __a					  =     {'pixel_values': images}
         return BatchFeature(data=lowerCamelCase			, tensor_type=lowerCamelCase   )