




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_    (	__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=MobileNetVaConfig(layer_norm_eps=0.001	)

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

    SCREAMING_SNAKE_CASE__								=re.match(R"""^.*mobilenet_v2_([^_]*)_([^_]*)$""",				snake_case__	)
    if matches:
        SCREAMING_SNAKE_CASE__								=float(matches[1]	)
        SCREAMING_SNAKE_CASE__								=int(matches[2]	)

    if model_name.startswith("""deeplabv3_"""	):
        SCREAMING_SNAKE_CASE__								=8
        SCREAMING_SNAKE_CASE__								=21
        SCREAMING_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".
        SCREAMING_SNAKE_CASE__								=1_001
        SCREAMING_SNAKE_CASE__								="""imagenet-1k-id2label.json"""

    SCREAMING_SNAKE_CASE__								="""huggingface/label-files"""
    SCREAMING_SNAKE_CASE__								=json.load(open(hf_hub_download(snake_case__,				snake_case__,				repo_type="""dataset"""	),				"""r"""	)	)

    if config.num_labels == 1_001:
        SCREAMING_SNAKE_CASE__								={int(snake_case__	) + 1: v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE__								="""background"""
    else:
        SCREAMING_SNAKE_CASE__								={int(snake_case__	): v for k, v in idalabel.items()}

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

    return config



def 	UpperCAmelCase_    (	):
    SCREAMING_SNAKE_CASE__								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
    SCREAMING_SNAKE_CASE__								=Image.open(requests.get(snake_case__,				stream=snake_case__	).raw	)
    return im



@torch.no_grad()
def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase=False	):
    SCREAMING_SNAKE_CASE__								=get_mobilenet_va_config(snake_case__	)

    # Load 🤗 model
    if model_name.startswith("""deeplabv3_"""	):
        SCREAMING_SNAKE_CASE__								=MobileNetVaForSemanticSegmentation(snake_case__	).eval()
    else:
        SCREAMING_SNAKE_CASE__								=MobileNetVaForImageClassification(snake_case__	).eval()

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

    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
    SCREAMING_SNAKE_CASE__								=MobileNetVaImageProcessor(
        crop_size={"""width""": config.image_size, """height""": config.image_size},				size={"""shortest_edge""": config.image_size + 32},				)
    SCREAMING_SNAKE_CASE__								=image_processor(images=prepare_img(),				return_tensors="""pt"""	)
    SCREAMING_SNAKE_CASE__								=model(**snake_case__	)
    SCREAMING_SNAKE_CASE__								=outputs.logits

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

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            SCREAMING_SNAKE_CASE__								=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]],
                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                ]	)

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

        assert torch.allclose(logits[0, :3, :3, :3],				snake_case__,				atol=1E-4	)
    else:
        assert logits.shape == (1, 1_001)

        if model_name == "mobilenet_v2_1.4_224":
            SCREAMING_SNAKE_CASE__								=torch.tensor([0.0181, -1.0015, 0.4688]	)
        elif model_name == "mobilenet_v2_1.0_224":
            SCREAMING_SNAKE_CASE__								=torch.tensor([0.2445, -1.1993, 0.1905]	)
        elif model_name == "mobilenet_v2_0.75_160":
            SCREAMING_SNAKE_CASE__								=torch.tensor([0.2482, 0.4136, 0.6669]	)
        elif model_name == "mobilenet_v2_0.35_96":
            SCREAMING_SNAKE_CASE__								=torch.tensor([0.1451, -0.4624, 0.7192]	)
        else:
            SCREAMING_SNAKE_CASE__								=None

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

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

    if push_to_hub:
        print("""Pushing to the hub..."""	)
        SCREAMING_SNAKE_CASE__								="""google/""" + model_name
        image_processor.push_to_hub(snake_case__	)
        model.push_to_hub(snake_case__	)


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 collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase			       =			logging.get_logger(__name__)

_UpperCamelCase			       =			{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""rembert"""




 def __init__(self       ,       __a=250300       ,       __a=1152       ,       __a=32       ,       __a=18       ,       __a=256       ,       __a=1664       ,       __a=4608       ,       __a="gelu"       ,       __a=0.0       ,       __a=0.0       ,       __a=0.1       ,       __a=512       ,       __a=2       ,       __a=0.02       ,       __a=1E-1_2       ,       __a=True       ,       __a=0       ,       __a=312       ,       __a=313       ,       **__a       ,       ) ->					str:






  """simple docstring"""





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

  UpperCAmelCase__							=				vocab_size
  UpperCAmelCase__							=				input_embedding_size
  UpperCAmelCase__							=				output_embedding_size
  UpperCAmelCase__							=				max_position_embeddings
  UpperCAmelCase__							=				hidden_size
  UpperCAmelCase__							=				num_hidden_layers
  UpperCAmelCase__							=				num_attention_heads
  UpperCAmelCase__							=				intermediate_size
  UpperCAmelCase__							=				hidden_act
  UpperCAmelCase__							=				hidden_dropout_prob
  UpperCAmelCase__							=				attention_probs_dropout_prob
  UpperCAmelCase__							=				classifier_dropout_prob
  UpperCAmelCase__							=				initializer_range
  UpperCAmelCase__							=				type_vocab_size
  UpperCAmelCase__							=				layer_norm_eps
  UpperCAmelCase__							=				use_cache
  UpperCAmelCase__							=				False







class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 @property
 def   UpperCamelCase__							(self ) ->					Mapping[str, Mapping[int, str]]:






  """simple docstring"""





  if self.task == "multiple-choice":
   UpperCAmelCase__							=				{0: 'batch', 1: 'choice', 2: 'sequence'}
  else:
   UpperCAmelCase__							=				{0: 'batch', 1: 'sequence'}
  return OrderedDict(
      [
          ('input_ids', dynamic_axis),
          ('attention_mask', dynamic_axis),
          ('token_type_ids', dynamic_axis),
      ] )




 @property
 def   UpperCamelCase__							(self ) ->					float:






  """simple docstring"""





  return 1E-4



