





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()
SCREAMING_SNAKE_CASE__       :			str			      =						logging.get_logger(__name__)






def       _A  (      lowerCamelCase   ):
   a__	:						int      		=  MobileNetVaConfig(layer_norm_eps=0.001   )

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

   a__	:						List[Any]      		=  re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"							, __A   )
   if matches:
      a__	:						Union[str, Any]      		=  float(matches[1]   )
      a__	:						Any      		=  int(matches[2]   )

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

   return config






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






@torch.no_grad()
def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=False   ):
   a__	:						Any      		=  get_mobilenet_va_config(__A   )

   # Load 🤗 model
   a__	:						Tuple      		=  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
   a__	:						Optional[int]      		=  MobileNetVaImageProcessor(
       crop_size={"width": config.image_size, "height": config.image_size}							, size={"shortest_edge": config.image_size + 32}							, )
   a__	:						str      		=  image_processor(images=prepare_img()							, return_tensors="pt"   )
   a__	:						int      		=  model(**__A   )
   a__	:						List[Any]      		=  outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      a__	:						Dict      		=  torch.tensor([-4.1739, -1.1233, 3.1205]   )
   elif model_name == "mobilenet_v1_0.75_192":
      a__	:						int      		=  torch.tensor([-3.9440, -2.3141, -0.3333]   )
   else:
      a__	:						Dict      		=  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__	:						Optional[Any]      		=  "google/" + model_name
      image_processor.push_to_hub(__A   )
      model.push_to_hub(__A   )


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

  SCREAMING_SNAKE_CASE__       :			List[str]			      =						parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )









# Lint as: python3
import itertools
import os
import re


SCREAMING_SNAKE_CASE__       :			Tuple			      =						re.compile(R"""([A-Z]+)([A-Z][a-z])""")
SCREAMING_SNAKE_CASE__       :			List[str]			      =						re.compile(R"""([a-z\d])([A-Z])""")

SCREAMING_SNAKE_CASE__       :			Tuple			      =						re.compile(R"""(?<!_)_(?!_)""")
SCREAMING_SNAKE_CASE__       :			Dict			      =						re.compile(R"""(_{2,})""")

SCREAMING_SNAKE_CASE__       :			List[Any]			      =						R"""^\w+(\.\w+)*$"""

SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						R"""<>:/\|?*"""






def       _A  (      lowerCamelCase   ):
   a__	:						List[str]      		=  _uppercase_uppercase_re.sub(r"\1_\2"							, lowerCamelCase   )
   a__	:						Dict      		=  _lowercase_uppercase_re.sub(r"\1_\2"							, lowerCamelCase   )
   return name.lower()






def       _A  (      lowerCamelCase   ):
   a__	:						Tuple      		=  _single_underscore_re.split(lowerCamelCase   )
   a__	:						Any      		=  [_multiple_underscores_re.split(lowerCamelCase   ) for n in name]
   return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowerCamelCase   ) if n != ""   )






def       _A  (      lowerCamelCase   ):
   if os.path.basename(lowerCamelCase   ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}"""   )
   return camelcase_to_snakecase(lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if os.path.basename(lowerCamelCase   ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}"""   )
   if not re.match(_split_re							, lowerCamelCase   ):
      raise ValueError(F"""Split name should match '{_split_re}'' but got '{split}'."""   )
   return F"""{filename_prefix_for_name(lowerCamelCase   )}-{split}"""






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None   ):
   a__	:						Union[str, Any]      		=  filename_prefix_for_split(lowerCamelCase							, lowerCamelCase   )
   if filetype_suffix:
      prefix += F""".{filetype_suffix}"""
   a__	:						Any      		=  os.path.join(lowerCamelCase							, lowerCamelCase   )
   return F"""{filepath}*"""






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None							, lowerCamelCase=None   ):
   a__	:						List[str]      		=  filename_prefix_for_split(lowerCamelCase							, lowerCamelCase   )
   a__	:						Tuple      		=  os.path.join(lowerCamelCase							, lowerCamelCase   )

   if shard_lengths:
      a__	:						List[str]      		=  len(lowerCamelCase   )
      a__	:						str      		=  [F"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(lowerCamelCase   )]
      if filetype_suffix:
         a__	:						Optional[Any]      		=  [filename + F""".{filetype_suffix}""" for filename in filenames]
      return filenames
   else:
      a__	:						Optional[int]      		=  prefix
      if filetype_suffix:
         filename += F""".{filetype_suffix}"""
      return [filename]

