





import logging

from transformers.configuration_utils import PretrainedConfig


lowercase_          =		logging.getLogger(__name__)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Union[str, Any]			 =	'masked_bert'


   def __init__(   self      :    str      ,					a      :    Any=30_522      ,					a      :    Union[str, Any]=768      ,					a      :    Optional[int]=12      ,					a      :    str=12      ,					a      :    Dict=3_072      ,					a      :    str="gelu"      ,					a      :    List[Any]=0.1      ,					a      :    Tuple=0.1      ,					a      :    Dict=512      ,					a      :    Any=2      ,					a      :    Any=0.02      ,					a      :    List[str]=1E-1_2      ,					a      :    Union[str, Any]=0      ,					a      :    int="topK"      ,					a      :    List[str]="constant"      ,					a      :    str=0.0      ,					**a      :    Union[str, Any]      ,					)->       Optional[int]:





     """simple docstring"""







     super().__init__(pad_token_id=a      ,					**a	)

     lowercase__           =  vocab_size
     lowercase__           =  hidden_size
     lowercase__           =  num_hidden_layers
     lowercase__           =  num_attention_heads
     lowercase__           =  hidden_act
     lowercase__           =  intermediate_size
     lowercase__           =  hidden_dropout_prob
     lowercase__           =  attention_probs_dropout_prob
     lowercase__           =  max_position_embeddings
     lowercase__           =  type_vocab_size
     lowercase__           =  initializer_range
     lowercase__           =  layer_norm_eps
     lowercase__           =  pruning_method
     lowercase__           =  mask_init
     lowercase__           =  mask_scale









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()
lowercase_          =		logging.get_logger(__name__)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  lowercase__           =  MobileNetVaConfig(layer_norm_eps=0.0_0_1		)

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

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

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

  return config




def       __UpperCamelCase  ()    ->       int:
  lowercase__           =  'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__           =  Image.open(requests.get(_SCREAMING_SNAKE_CASE		,						stream=_SCREAMING_SNAKE_CASE		).raw		)
  return im




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=False		)    ->       Union[str, Any]:
  lowercase__           =  get_mobilenet_va_config(_SCREAMING_SNAKE_CASE		)

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

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    lowercase__           =  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":
    lowercase__           =  torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]		)
  else:
    lowercase__           =  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...'		)
    lowercase__           =  'google/' + model_name
    image_processor.push_to_hub(_SCREAMING_SNAKE_CASE		)
    model.push_to_hub(_SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      lowercase_          =		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."""
      )

      lowercase_          =		parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )


