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_   : Union[str, Any]					)    ->  Any:
  __UpperCAmelCase						    =       MobileNetVaConfig(layer_norm_eps=0.0_01					)

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

  __UpperCAmelCase						    =       re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"						,	A_					)
  if matches:
    __UpperCAmelCase						    =       float(matches[1]					)
    __UpperCAmelCase						    =       int(matches[2]					)

  # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
  # the usual 1000. The first class (index 0) is "background".
  __UpperCAmelCase						    =       10_01
  __UpperCAmelCase						    =       "imagenet-1k-id2label.json"
  __UpperCAmelCase						    =       "huggingface/label-files"
  __UpperCAmelCase						    =       json.load(open(hf_hub_download(A_						,	A_						,	repo_type="dataset"					)						,	"r"					)					)
  __UpperCAmelCase						    =       {int(A_					) + 1: v for k, v in idalabel.items()}
  __UpperCAmelCase						    =       "background"
  __UpperCAmelCase						    =       idalabel
  __UpperCAmelCase						    =       {v: k for k, v in idalabel.items()}

  return config




def       __lowerCAmelCase      (						)    ->  Any:
  __UpperCAmelCase						    =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					)
  return im




@torch.no_grad()
def       __lowerCAmelCase      (						A_   : int						,	A_   : List[str]						,	A_   : str						,	A_   : str=False					)    ->  Union[str, Any]:
  __UpperCAmelCase						    =       get_mobilenet_va_config(A_					)

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

  assert logits.shape == (1, 10_01)

  if model_name == "mobilenet_v1_1.0_224":
    __UpperCAmelCase						    =       torch.tensor([-4.17_39, -1.12_33, 3.12_05]					)
  elif model_name == "mobilenet_v1_0.75_192":
    __UpperCAmelCase						    =       torch.tensor([-3.94_40, -2.31_41, -0.33_33]					)
  else:
    __UpperCAmelCase						    =       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..."					)
    __UpperCAmelCase						    =       "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_tokenizers_available, is_torch_available


a_							=      {
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SqueezeBertForMaskedLM""",
           """SqueezeBertForMultipleChoice""",
           """SqueezeBertForQuestionAnswering""",
           """SqueezeBertForSequenceClassification""",
           """SqueezeBertForTokenClassification""",
           """SqueezeBertModel""",
           """SqueezeBertModule""",
           """SqueezeBertPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_squeezebert import (
           SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SqueezeBertConfig,
           SqueezeBertOnnxConfig,
       )
       from .tokenization_squeezebert import SqueezeBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_squeezebert import (
                  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SqueezeBertForMaskedLM,
                  SqueezeBertForMultipleChoice,
                  SqueezeBertForQuestionAnswering,
                  SqueezeBertForSequenceClassification,
                  SqueezeBertForTokenClassification,
                  SqueezeBertModel,
                  SqueezeBertModule,
                  SqueezeBertPreTrainedModel,
              )

else:
       import sys

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



