




'''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()
__a						:      str       =						logging.get_logger(__name__)





def      __a     (       _UpperCamelCase:   Any   )						->			List[Any]:

     """simple docstring"""




     _snake_case							=				MobileNetVaConfig(layer_norm_eps=0.001   )

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

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

     # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
     # the usual 1000. The first class (index 0) is "background".
     _snake_case							=				1_001
     _snake_case							=				'''imagenet-1k-id2label.json'''
     _snake_case							=				'''huggingface/label-files'''
     _snake_case							=				json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type="dataset"   ) ,						"r"   )   )
     _snake_case							=				{int(_UpperCamelCase   ) + 1: v for k, v in idalabel.items()}
     _snake_case							=				'''background'''
     _snake_case							=				idalabel
     _snake_case							=				{v: k for k, v in idalabel.items()}

     return config





def      __a     (       )						->			Any:

     """simple docstring"""




     _snake_case							=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _snake_case							=				Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase   ).raw   )
     return im





@torch.no_grad()
def      __a     (       _UpperCamelCase:   int ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   int ,						_UpperCamelCase:   Dict=False   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				get_mobilenet_va_config(_UpperCamelCase   )

     # Load 🤗 model
     _snake_case							=				MobileNetVaForImageClassification(_UpperCamelCase   ).eval()

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

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

     assert logits.shape == (1, 1_001)

     if model_name == "mobilenet_v1_1.0_224":
          _snake_case							=				torch.tensor([-4.1739, -1.1233, 3.1205]   )
     elif model_name == "mobilenet_v1_0.75_192":
          _snake_case							=				torch.tensor([-3.9440, -2.3141, -0.3333]   )
     else:
          _snake_case							=				None

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

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

     if push_to_hub:
          print("Pushing to the hub..."   )
          _snake_case							=				'''google/''' + model_name
          image_processor.push_to_hub(_UpperCamelCase   )
          model.push_to_hub(_UpperCamelCase   )


if __name__ == "__main__":
   __a						:      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.'''
   )

   __a						:      List[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

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Any       =						["""image_processor""", """tokenizer"""]
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						"""BridgeTowerImageProcessor"""
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def __call__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      BatchEncoding:
          _snake_case							=				self.tokenizer(
              text=_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,return_token_type_ids=_SCREAMING_SNAKE_CASE ,return_attention_mask=_SCREAMING_SNAKE_CASE ,return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,return_offsets_mapping=_SCREAMING_SNAKE_CASE ,return_length=_SCREAMING_SNAKE_CASE ,verbose=_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
          # add pixel_values + pixel_mask
          _snake_case							=				self.image_processor(
              _SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,do_normalize=_SCREAMING_SNAKE_CASE ,do_center_crop=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
          encoding.update(_SCREAMING_SNAKE_CASE   )

          return encoding






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.tokenizer.model_input_names
          _snake_case							=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )


