


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a      :      List[Any]       				=      {"configuration_umt5": ["UMT5Config", "UMT5OnnxConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _a      :      List[str]       				=      [
           "UMT5EncoderModel",
           "UMT5ForConditionalGeneration",
           "UMT5ForQuestionAnswering",
           "UMT5Model",
           "UMT5PreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_umta import UMTaConfig, UMTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_umta import (
                  UMTaEncoderModel,
                  UMTaForConditionalGeneration,
                  UMTaForQuestionAnswering,
                  UMTaModel,
                  UMTaPreTrainedModel,
              )
else:
       import sys

       _a      :      Any       				=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








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


logging.set_verbosity_info()
_a      :      int       				=      logging.get_logger(__name__)





def      _a		(lowercase__ :      int	) -> Union[str, Any]:






     """simple docstring"""
     __snake_case    			=	MobileNetVaConfig(layer_norm_eps=0.0_01	)

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

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

     if model_name.startswith('deeplabv3_'	):
          __snake_case    			=	8
          __snake_case    			=	2_1
          __snake_case    			=	'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".
          __snake_case    			=	1_0_0_1
          __snake_case    			=	'imagenet-1k-id2label.json'

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

     if config.num_labels == 1_0_0_1:
          __snake_case    			=	{int(lowercase__	) + 1: v for k, v in idalabel.items()}
          __snake_case    			=	'background'
     else:
          __snake_case    			=	{int(lowercase__	): v for k, v in idalabel.items()}

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

     return config





def      _a		() -> Dict:






     """simple docstring"""
     __snake_case    			=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     __snake_case    			=	Image.open(requests.get(lowercase__       ,      stream=lowercase__	).raw	)
     return im





@torch.no_grad()
def      _a		(lowercase__ :      str       ,      lowercase__ :      Optional[int]       ,      lowercase__ :      Optional[Any]       ,      lowercase__ :      Dict=False	) -> Any:






     """simple docstring"""
     __snake_case    			=	get_mobilenet_va_config(lowercase__	)

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

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

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     __snake_case    			=	MobileNetVaImageProcessor(
         crop_size={'width': config.image_size, 'height': config.image_size}       ,      size={'shortest_edge': config.image_size + 3_2}       ,      )
     __snake_case    			=	image_processor(images=prepare_img()       ,      return_tensors='pt'	)
     __snake_case    			=	model(**lowercase__	)
     __snake_case    			=	outputs.logits

     if model_name.startswith('deeplabv3_'	):
          assert logits.shape == (1, 2_1, 6_5, 6_5)

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               __snake_case    			=	torch.tensor(
                   [
                       [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                       [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                       [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
                   ]	)

          else:
               raise ValueError(f'Unknown model name: {model_name}'	)

          assert torch.allclose(logits[0, :3, :3, :3]       ,      lowercase__       ,      atol=1e-4	)
     else:
          assert logits.shape == (1, 1_0_0_1)

          if model_name == "mobilenet_v2_1.4_224":
               __snake_case    			=	torch.tensor([0.01_81, -1.00_15, 0.46_88]	)
          elif model_name == "mobilenet_v2_1.0_224":
               __snake_case    			=	torch.tensor([0.24_45, -1.19_93, 0.19_05]	)
          elif model_name == "mobilenet_v2_0.75_160":
               __snake_case    			=	torch.tensor([0.24_82, 0.41_36, 0.66_69]	)
          elif model_name == "mobilenet_v2_0.35_96":
               __snake_case    			=	torch.tensor([0.14_51, -0.46_24, 0.71_92]	)
          else:
               __snake_case    			=	None

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

     Path(lowercase__	).mkdir(exist_ok=lowercase__	)
     print(f'Saving model {model_name} to {pytorch_dump_folder_path}'	)
     model.save_pretrained(lowercase__	)
     print(f'Saving image processor to {pytorch_dump_folder_path}'	)
     image_processor.save_pretrained(lowercase__	)

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


if __name__ == "__main__":
       _a      :      List[str]       				=      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."
       )

       _a      :      str       				=      parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )




