

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


logging.set_verbosity_info()
lowerCamelCase 	=   logging.get_logger(__name__)




def      UpperCAmelCase__							(     _A :					List[str]				):





  '''simple docstring'''







  a__    =MobileNetVaConfig(layer_norm_eps=0.001				)

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

  a__    =re.match(r'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''      , _A				)
  if matches:
    a__    =float(matches[1]				)
    a__    =int(matches[2]				)

  if model_name.startswith('''deeplabv3_'''				):
    a__    =8
    a__    =21
    a__    ='''pascal-voc-id2label.json'''
  else:
    # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
    # of the usual 1000. The first class (index 0) is "background".
    a__    =10_01
    a__    ='''imagenet-1k-id2label.json'''

  a__    ='''huggingface/label-files'''
  a__    =json.load(open(hf_hub_download(_A      , _A      , repo_type='''dataset'''				)      , '''r'''				)				)

  if config.num_labels == 10_01:
    a__    ={int(_A				) + 1: v for k, v in idalabel.items()}
    a__    ='''background'''
  else:
    a__    ={int(_A				): v for k, v in idalabel.items()}

  a__    =idalabel
  a__    ={v: k for k, v in idalabel.items()}

  return config




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







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




@torch.no_grad()
def      UpperCAmelCase__							(     _A :					int      , _A :					Optional[int]      , _A :					Optional[Any]      , _A :					Tuple=False				):





  '''simple docstring'''







  a__    =get_mobilenet_va_config(_A				)

  # Load 🤗 model
  if model_name.startswith('''deeplabv3_'''				):
    a__    =MobileNetVaForSemanticSegmentation(_A				).eval()
  else:
    a__    =MobileNetVaForImageClassification(_A				).eval()

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

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  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(**_A				)
  a__    =outputs.logits

  if model_name.startswith('''deeplabv3_'''				):
    assert logits.shape == (1, 21, 65, 65)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      a__    =torch.tensor(
          [
              [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
              [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
              [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
          ]				)

    else:
      raise ValueError(F"""Unknown model name: {model_name}"""				)

    assert torch.allclose(logits[0, :3, :3, :3]      , _A      , atol=1E-4				)
  else:
    assert logits.shape == (1, 10_01)

    if model_name == "mobilenet_v2_1.4_224":
      a__    =torch.tensor([0.0_181, -1.0_015, 0.4_688]				)
    elif model_name == "mobilenet_v2_1.0_224":
      a__    =torch.tensor([0.2_445, -1.1_993, 0.1_905]				)
    elif model_name == "mobilenet_v2_0.75_160":
      a__    =torch.tensor([0.2_482, 0.4_136, 0.6_669]				)
    elif model_name == "mobilenet_v2_0.35_96":
      a__    =torch.tensor([0.1_451, -0.4_624, 0.7_192]				)
    else:
      a__    =None

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

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

  if push_to_hub:
    print('''Pushing to the hub...'''				)
    a__    ='''google/''' + model_name
    image_processor.push_to_hub(_A				)
    model.push_to_hub(_A				)


if __name__ == "__main__":
       lowerCamelCase 	=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--model_name''',
           default='''mobilenet_v2_1.0_224''',
           type=str,
           help='''Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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 	=   parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )



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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  'yoso'



   def __init__(							self,       lowercase_=50265,       lowercase_=768,       lowercase_=12,       lowercase_=12,       lowercase_=3072,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=4096,       lowercase_=1,       lowercase_=0.02,       lowercase_=1E-12,       lowercase_="absolute",       lowercase_=True,       lowercase_=9,       lowercase_=64,       lowercase_=None,       lowercase_=True,       lowercase_=True,       lowercase_=1,       lowercase_=0,       lowercase_=2,       **lowercase_,       )				->       List[str]:






     """simple docstring"""






     super().__init__(pad_token_id=lowercase_,       bos_token_id=lowercase_,       eos_token_id=lowercase_,       **lowercase_			)

     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =initializer_range
     a__    =type_vocab_size
     a__    =layer_norm_eps
     a__    =position_embedding_type
     a__    =use_expectation
     a__    =hash_code_len
     a__    =num_hash
     a__    =conv_window
     a__    =use_fast_hash
     a__    =lsh_backward
