





'''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					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     MobileNetVaConfig(layer_norm_eps=0.0_01 )

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

			__snake_case					:      List[Any]							      =     re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""    ,	_lowerCamelCase )
			if matches:
						__snake_case					:      Optional[Any]							      =     float(matches[1] )
						__snake_case					:      Union[str, Any]							      =     int(matches[2] )

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

			return config







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[Any]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )
			return im







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     get_mobilenet_va_config(_lowerCamelCase )

			# Load 🤗 model
			__snake_case					:      Optional[Any]							      =     MobileNetVaForImageClassification(_lowerCamelCase ).eval()

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

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

			assert logits.shape == (1, 1001)

			if model_name == "mobilenet_v1_1.0_224":
						__snake_case					:      str							      =     torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
			elif model_name == "mobilenet_v1_0.75_192":
						__snake_case					:      Tuple							      =     torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
			else:
						__snake_case					:      List[Any]							      =     None

			if expected_logits is not None:
						assert torch.allclose(logits[0, :3]    ,	_lowerCamelCase    ,	atol=1E-4 )

			Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
			print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
			model.save_pretrained(_lowerCamelCase )
			print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
			image_processor.save_pretrained(_lowerCamelCase )

			if push_to_hub:
						print("""Pushing to the hub...""" )
						__snake_case					:      Optional[Any]							      =     """google/""" + model_name
						image_processor.push_to_hub(_lowerCamelCase )
						model.push_to_hub(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	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					    =	parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)









'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCamelCase					    =	"\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

__UpperCamelCase					    =	"\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

__UpperCamelCase					    =	"\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION		)
class  _A (					datasets.Metric		):

		def 	lowercase__     (   self					:   Tuple			)					->    Dict:


					"""simple docstring"""

					return datasets.MetricInfo(
					    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
					        {
					            """predictions""": datasets.Value("""int32"""			),
					            """references""": datasets.Value("""int32"""			),
					        }			)  ,      reference_urls=[
					        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
					    ]  ,      )

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Union[str, Any]=None			)					->    Optional[int]:


					"""simple docstring"""

					return {
					    "matthews_correlation": float(matthews_corrcoef(__magic_name__  ,      __magic_name__  ,      sample_weight=__magic_name__			)			),
					}


