


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				:  Any      					=							logging.get_logger(__name__)
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      str						=   MobileNetVaConfig(layer_norm_eps=0.0_0_1       )

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

   UpperCAmelCase_							:      List[Any]						=   re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,       lowerCAmelCase__       )
   if matches:
      UpperCAmelCase_							:      Union[str, Any]						=   float(matches[1]       )
      UpperCAmelCase_							:      Optional[Any]						=   int(matches[2]       )

   # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
   # the usual 1000. The first class (index 0) is "background".
   UpperCAmelCase_							:      Union[str, Any]						=   1_0_0_1
   UpperCAmelCase_							:      List[Any]						=   'imagenet-1k-id2label.json'
   UpperCAmelCase_							:      List[Any]						=   'huggingface/label-files'
   UpperCAmelCase_							:      Union[str, Any]						=   json.load(open(hf_hub_download(lowerCAmelCase__			,       lowerCAmelCase__			,       repo_type='''dataset'''       )			,       '''r'''       )       )
   UpperCAmelCase_							:      Union[str, Any]						=   {int(lowerCAmelCase__       ) + 1: v for k, v in idalabel.items()}
   UpperCAmelCase_							:      Optional[Any]						=   'background'
   UpperCAmelCase_							:      Optional[int]						=   idalabel
   UpperCAmelCase_							:      Optional[int]						=   {v: k for k, v in idalabel.items()}

   return config
def      snake_case_						( ):
   UpperCAmelCase_							:      str						=   'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCAmelCase_							:      Optional[int]						=   Image.open(requests.get(lowerCAmelCase__			,       stream=lowerCAmelCase__       ).raw       )
   return im
@torch.no_grad()
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase=False       ):
   UpperCAmelCase_							:      List[Any]						=   get_mobilenet_va_config(lowerCAmelCase__       )

   # Load 🤗 model
   UpperCAmelCase_							:      Dict						=   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
   UpperCAmelCase_							:      Union[str, Any]						=   MobileNetVaImageProcessor(
       crop_size={'''width''': config.image_size, '''height''': config.image_size}			,       size={'''shortest_edge''': config.image_size + 3_2}			,       )
   UpperCAmelCase_							:      Tuple						=   image_processor(images=prepare_img()			,       return_tensors='''pt'''       )
   UpperCAmelCase_							:      List[Any]						=   model(**lowerCAmelCase__       )
   UpperCAmelCase_							:      List[str]						=   outputs.logits

   assert logits.shape == (1, 1_0_0_1)

   if model_name == "mobilenet_v1_1.0_224":
      UpperCAmelCase_							:      List[Any]						=   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":
      UpperCAmelCase_							:      Dict						=   torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]       )
   else:
      UpperCAmelCase_							:      Union[str, Any]						=   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...'''       )
      UpperCAmelCase_							:      Union[str, Any]						=   'google/' + model_name
      image_processor.push_to_hub(lowerCAmelCase__       )
      model.push_to_hub(lowerCAmelCase__       )


if __name__ == "__main__":
       __UpperCamelCase				:  List[str]      					=							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				:  Tuple      					=							parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase				:  Tuple      					=							logging.get_logger(__name__)





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Union[str, Any]			= 'encoder-decoder'
    A_  :     Optional[int]			= True





    def __init__(	self				:   Dict       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       super().__init__(**__snake_case	)
       assert (
           "encoder" in kwargs and "decoder" in kwargs
       ), "Config has to be initialized with encoder and decoder config"
       UpperCAmelCase_							:      int						=   kwargs.pop('''encoder'''	)
       UpperCAmelCase_							:      List[Any]						=   encoder_config.pop('''model_type'''	)
       UpperCAmelCase_							:      int						=   kwargs.pop('''decoder'''	)
       UpperCAmelCase_							:      Union[str, Any]						=   decoder_config.pop('''model_type'''	)

       from ..auto.configuration_auto import AutoConfig

       UpperCAmelCase_							:      Optional[int]						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)
       UpperCAmelCase_							:      Any						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   True





    @classmethod
    def 					_lowerCamelCase (	cls				:   Optional[Any]       ,   __snake_case				:   PretrainedConfig       ,   __snake_case				:   PretrainedConfig       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''	)
       UpperCAmelCase_							:      Dict						=   True
       UpperCAmelCase_							:      str						=   True

       return cls(encoder=encoder_config.to_dict()       ,   decoder=decoder_config.to_dict()       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   copy.deepcopy(self.__dict__	)
       UpperCAmelCase_							:      Tuple						=   self.encoder.to_dict()
       UpperCAmelCase_							:      Tuple						=   self.decoder.to_dict()
       UpperCAmelCase_							:      Tuple						=   self.__class__.model_type
       return output