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_     =	logging.get_logger(__name__)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    MobileNetVaConfig(layer_norm_eps=0.0_01  )

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

							_SCREAMING_SNAKE_CASE		:						Any			  =    re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""     ,	a_  )
							if matches:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    float(matches[1]  )
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    int(matches[2]  )

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    1_001
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    '''imagenet-1k-id2label.json'''
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    '''huggingface/label-files'''
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    json.load(open(hf_hub_download(a_     ,	a_     ,	repo_type="""dataset"""  )     ,	"""r"""  )  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    {int(a_  ) + 1: v for k, v in idalabel.items()}
							_SCREAMING_SNAKE_CASE		:						int			  =    '''background'''
							_SCREAMING_SNAKE_CASE		:						str			  =    idalabel
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {v: k for k, v in idalabel.items()}

							return config



def      lowerCamelCase_()->    List[str]:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    Image.open(requests.get(a_     ,	stream=a_  ).raw  )
							return im



@torch.no_grad()
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						int			  =    get_mobilenet_va_config(a_  )

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

							assert logits.shape == (1, 1_001)

							if model_name == "mobilenet_v1_1.0_224":
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.tensor([-4.17_39, -1.12_33, 3.12_05]  )
							elif model_name == "mobilenet_v1_0.75_192":
														_SCREAMING_SNAKE_CASE		:						str			  =    torch.tensor([-3.94_40, -2.31_41, -0.33_33]  )
							else:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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..."""  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    '''google/''' + model_name
														image_processor.push_to_hub(a_  )
														model.push_to_hub(a_  )


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



"""simple docstring"""





from typing import Any

import numpy as np



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    bool:
							return np.array_equal(__SCREAMING_SNAKE_CASE     ,	matrix.conjugate().T  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    v.conjugate().T
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    v_star.dot(__SCREAMING_SNAKE_CASE  )
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	np.ndarray  )
							return (v_star_dot.dot(__SCREAMING_SNAKE_CASE  )) / (v_star.dot(__SCREAMING_SNAKE_CASE  ))



def      lowerCamelCase_()->    None:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]  )
							_SCREAMING_SNAKE_CASE		:						int			  =    np.array([[1], [2], [3]]  )
							assert is_hermitian(__SCREAMING_SNAKE_CASE  ), F"""{a} is not hermitian."""
							print(rayleigh_quotient(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )  )

							_SCREAMING_SNAKE_CASE		:						int			  =    np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]  )
							assert is_hermitian(__SCREAMING_SNAKE_CASE  ), F"""{a} is not hermitian."""
							assert rayleigh_quotient(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) == float(3  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			tests()


