



"""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()
_lowerCAmelCase							:   str				=				logging.get_logger(__name__)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       MobileNetVaConfig(layer_norm_eps=0.001							)

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

     _UpperCAmelCase     :      Any														=       re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"		,    SCREAMING_SNAKE_CASE__							)
     if matches:
          _UpperCAmelCase     :      Optional[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     :      Optional[Any]														=       1_001
     _UpperCAmelCase     :      Union[str, Any]														=       "imagenet-1k-id2label.json"
     _UpperCAmelCase     :      str														=       "huggingface/label-files"
     _UpperCAmelCase     :      Tuple														=       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    repo_type="dataset"							)		,    "r"							)							)
     _UpperCAmelCase     :      str														=       {int(SCREAMING_SNAKE_CASE__							) + 1: v for k, v in idalabel.items()}
     _UpperCAmelCase     :      List[Any]														=       "background"
     _UpperCAmelCase     :      List[Any]														=       idalabel
     _UpperCAmelCase     :      Dict														=       {v: k for k, v in idalabel.items()}

     return config

def 		__snake_case		( )    ->  Optional[int]:





     '''simple docstring'''

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

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





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_mobilenet_va_config(SCREAMING_SNAKE_CASE__							)

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

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

     if model_name == "mobilenet_v1_1.0_224":
          _UpperCAmelCase     :      Dict														=       torch.tensor([-4.1_739, -1.1_233, 3.1_205]							)
     elif model_name == "mobilenet_v1_0.75_192":
          _UpperCAmelCase     :      Any														=       torch.tensor([-3.9_440, -2.3_141, -0.3_333]							)
     else:
          _UpperCAmelCase     :      Optional[int]														=       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..."							)
          _UpperCAmelCase     :      Optional[Any]														=       "google/" + model_name
          image_processor.push_to_hub(SCREAMING_SNAKE_CASE__							)
          model.push_to_hub(SCREAMING_SNAKE_CASE__							)


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





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase							:   Optional[int]				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					'roberta'



      def __init__(	self	:      str	,      A	:      Optional[int]=5_0_2_6_5	,      A	:      Optional[int]=7_6_8	,      A	:      Tuple=1_2	,      A	:      Optional[Any]=1_2	,      A	:      Optional[int]=3_0_7_2	,      A	:      str="gelu"	,      A	:      List[Any]=0.1	,      A	:      Optional[Any]=0.1	,      A	:      Any=5_1_2	,      A	:      Dict=2	,      A	:      Union[str, Any]=0.02	,      A	:      str=1e-12	,      A	:      Optional[int]=1	,      A	:      List[str]=0	,      A	:      int=2	,      A	:      Any="absolute"	,      A	:      Optional[int]=True	,      A	:      int=None	,      **A	:      str	,      ):
           super().__init__(pad_token_id=A	,      bos_token_id=A	,      eos_token_id=A	,      **A		)

           _UpperCAmelCase     :      str														=       vocab_size
           _UpperCAmelCase     :      Dict														=       hidden_size
           _UpperCAmelCase     :      List[str]														=       num_hidden_layers
           _UpperCAmelCase     :      Any														=       num_attention_heads
           _UpperCAmelCase     :      List[str]														=       hidden_act
           _UpperCAmelCase     :      Any														=       intermediate_size
           _UpperCAmelCase     :      Union[str, Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      str														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Tuple														=       max_position_embeddings
           _UpperCAmelCase     :      Union[str, Any]														=       type_vocab_size
           _UpperCAmelCase     :      str														=       initializer_range
           _UpperCAmelCase     :      int														=       layer_norm_eps
           _UpperCAmelCase     :      Tuple														=       position_embedding_type
           _UpperCAmelCase     :      int														=       use_cache
           _UpperCAmelCase     :      Dict														=       classifier_dropout







class       UpperCAmelCase_    (							_UpperCamelCase		):



      @property
      def 		snake_case_   (	self	:      Tuple		):
           if self.task == "multiple-choice":
                _UpperCAmelCase     :      Optional[Any]														=       {0: "batch", 1: "choice", 2: "sequence"}
           else:
                _UpperCAmelCase     :      List[Any]														=       {0: "batch", 1: "sequence"}
           return OrderedDict(
               [
                   ("input_ids", dynamic_axis),
                   ("attention_mask", dynamic_axis),
               ]		)
