





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()
__A									=				logging.get_logger(__name__)





def      lowerCAmelCase_ (       __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Dict			  =MobileNetVaConfig(layer_norm_eps=0.0_0_1		)

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

  lowerCamelCase__:      Dict			  =re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"	,    __a		)
  if matches:
    lowerCamelCase__:      Optional[int]			  =float(matches[1]		)
    lowerCamelCase__:      str			  =int(matches[2]		)

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

  return config





def      lowerCAmelCase_ (       )     ->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  ="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase__:      Tuple			  =Image.open(requests.get(__a	,    stream=__a		).raw		)
  return im





@torch.no_grad()
def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a=False		)     ->		str:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  =get_mobilenet_va_config(__a		)

  # Load 🤗 model
  lowerCamelCase__:      Optional[int]			  =MobileNetVaForImageClassification(__a		).eval()

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

  # Check outputs on an image, prepared by MobileNetV1ImageProcessor
  lowerCamelCase__:      Optional[int]			  =MobileNetVaImageProcessor(
      crop_size={"width": config.image_size, "height": config.image_size}	,    size={"shortest_edge": config.image_size + 32}	,    )
  lowerCamelCase__:      Union[str, Any]			  =image_processor(images=prepare_img()	,    return_tensors="pt"		)
  lowerCamelCase__:      Dict			  =model(**__a		)
  lowerCamelCase__:      List[str]			  =outputs.logits

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    lowerCamelCase__:      Tuple			  =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":
    lowerCamelCase__:      Tuple			  =torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]		)
  else:
    lowerCamelCase__:      Optional[int]			  =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..."		)
    lowerCamelCase__:      Optional[Any]			  ="google/" + model_name
    image_processor.push_to_hub(__a		)
    model.push_to_hub(__a		)


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

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A									=				{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["DistilBertTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DistilBertForMaskedLM",
         "DistilBertForMultipleChoice",
         "DistilBertForQuestionAnswering",
         "DistilBertForSequenceClassification",
         "DistilBertForTokenClassification",
         "DistilBertModel",
         "DistilBertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDistilBertForMaskedLM",
         "TFDistilBertForMultipleChoice",
         "TFDistilBertForQuestionAnswering",
         "TFDistilBertForSequenceClassification",
         "TFDistilBertForTokenClassification",
         "TFDistilBertMainLayer",
         "TFDistilBertModel",
         "TFDistilBertPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "FlaxDistilBertForMaskedLM",
         "FlaxDistilBertForMultipleChoice",
         "FlaxDistilBertForQuestionAnswering",
         "FlaxDistilBertForSequenceClassification",
         "FlaxDistilBertForTokenClassification",
         "FlaxDistilBertModel",
         "FlaxDistilBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

