






"""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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase    :   Tuple   			=						logging.get_logger(__name__)




def       snake_case  (A_				:List[Any]				):







  '''simple docstring'''

  _A					:      Tuple    =       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_([^_]*)_([^_]*)$'     ,      lowercase__				)
  if matches:
    _A					:      Optional[int]    =       float(matches[1]				)
    _A					:      Dict    =       int(matches[2]				)

  # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
  # the usual 1000. The first class (index 0) is "background".
  _A					:      Union[str, Any]    =       1_0_0_1
  _A					:      Dict    =       """imagenet-1k-id2label.json"""
  _A					:      List[str]    =       """huggingface/label-files"""
  _A					:      Dict    =       json.load(open(hf_hub_download(lowercase__     ,      lowercase__     ,      repo_type='dataset'				)     ,      'r'				)				)
  _A					:      Tuple    =       {int(lowercase__				) + 1: v for k, v in idalabel.items()}
  _A					:      str    =       """background"""
  _A					:      Union[str, Any]    =       idalabel
  _A					:      List[str]    =       {v: k for k, v in idalabel.items()}

  return config




def       snake_case  ():







  '''simple docstring'''

  _A					:      Dict    =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _A					:      Optional[int]    =       Image.open(requests.get(lowercase__     ,      stream=lowercase__				).raw				)
  return im




@torch.no_grad()
def       snake_case  (A_				:Optional[Any]     ,      A_				:Optional[int]     ,      A_				:Union[str, Any]     ,      A_				:str=False				):







  '''simple docstring'''

  _A					:      Dict    =       get_mobilenet_va_config(lowercase__				)

  # Load 🤗 model
  _A					:      str    =       MobileNetVaForImageClassification(lowercase__				).eval()

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

  # 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 + 3_2}     ,      )
  _A					:      List[str]    =       image_processor(images=prepare_img()     ,      return_tensors='pt'				)
  _A					:      int    =       model(**lowercase__				)
  _A					:      List[Any]    =       outputs.logits

  assert logits.shape == (1, 1_0_0_1)

  if model_name == "mobilenet_v1_1.0_224":
    _A					:      str    =       torch.tensor([-4.1739, -1.1233, 3.1205]				)
  elif model_name == "mobilenet_v1_0.75_192":
    _A					:      Optional[int]    =       torch.tensor([-3.9440, -2.3141, -0.3333]				)
  else:
    _A					:      List[str]    =       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...'				)
    _A					:      Union[str, Any]    =       """google/""" + model_name
    image_processor.push_to_hub(lowercase__				)
    model.push_to_hub(lowercase__				)


if __name__ == "__main__":
   _UpperCamelCase    :   List[Any]   			=						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.'
   )

   _UpperCamelCase    :   Union[str, Any]   			=						parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )












"""simple docstring"""


import os
import sys

_UpperCamelCase    :   str   			=						os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_UpperCamelCase    :   int   			=						[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]




@add_start_docstrings(AutoConfig.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:int				):







  '''simple docstring'''

  return AutoConfig.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoTokenizer.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoTokenizer.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModel.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Tuple				):







  '''simple docstring'''

  return AutoModel.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def       snake_case  (*A_				:str     ,      **A_				:Optional[int]				):







  '''simple docstring'''

  return AutoModelForCausalLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Dict				):







  '''simple docstring'''

  return AutoModelForMaskedLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:str				):







  '''simple docstring'''

  return AutoModelForSequenceClassification.from_pretrained(*A_     ,      **A_				)






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoModelForQuestionAnswering.from_pretrained(*A_     ,      **A_				)




