



'''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()
lowerCAmelCase:      List[str]		 =		logging.get_logger(__name__)
def    lowerCamelCase__ (		_A       ):
  a		:      str       =  MobileNetVaConfig(layer_norm_eps=0.001       )

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

  a		:      Dict       =  re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'				,    _A       )
  if matches:
    a		:      List[str]       =  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		:      Any       =  1001
  a		:      str       =  'imagenet-1k-id2label.json'
  a		:      int       =  'huggingface/label-files'
  a		:      Optional[Any]       =  json.load(open(hf_hub_download(_A				,    _A				,    repo_type='dataset'       )				,    'r'       )       )
  a		:      Tuple       =  {int(_A       ) + 1: v for k, v in idalabel.items()}
  a		:      Tuple       =  'background'
  a		:      List[Any]       =  idalabel
  a		:      Any       =  {v: k for k, v in idalabel.items()}

  return config
def    lowerCamelCase__ (		):
  a		:      Tuple       =  'http://images.cocodataset.org/val2017/000000039769.jpg'
  a		:      List[Any]       =  Image.open(requests.get(_A				,    stream=_A       ).raw       )
  return im
@torch.no_grad()
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A=False       ):
  a		:      Tuple       =  get_mobilenet_va_config(_A       )

  # Load 🤗 model
  a		:      Optional[Any]       =  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		:      Any       =  image_processor(images=prepare_img()				,    return_tensors='pt'       )
  a		:      Tuple       =  model(**_A       )
  a		:      Optional[int]       =  outputs.logits

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    a		:      Union[str, Any]       =  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		:      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...'       )
    a		:      int       =  'google/' + model_name
    image_processor.push_to_hub(_A       )
    model.push_to_hub(_A       )


if __name__ == "__main__":
     lowerCAmelCase:      int		 =		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.'
     )

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




'''simple docstring'''






import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def    lowerCamelCase__ (		_A       ):
  return (data["data"], data["target"])
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Any       =  XGBRegressor(verbosity=0				,    random_state=42       )
  xgb.fit(_A				,    _A       )
  # Predict target for test data
  a		:      Any       =  xgb.predict(_A       )
  a		:      Any       =  predictions.reshape(len(_A       )				,    1       )
  return predictions
def    lowerCamelCase__ (		):
  a		:      Optional[int]       =  fetch_california_housing()
  a     ,			a		:      Optional[Any]       =  data_handling(_A       )
  a     ,			a     ,			a     ,			a		:      Dict       =  train_test_split(
      _A				,    _A				,    test_size=0.25				,    random_state=1       )
  a		:      List[str]       =  xgboost(_A				,    _A				,    _A       )
  # Error printing
  print(f"""Mean Absolute Error : {mean_absolute_error(_A				,    _A       )}"""       )
  print(f"""Mean Square Error  : {mean_squared_error(_A				,    _A       )}"""       )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()