"""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_     			=						logging.get_logger(__name__)



def   lowerCamelCase_(__SCREAMING_SNAKE_CASE	)->     int:
							_SCREAMING_SNAKE_CASE		:    Any      	=     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_([^_]*)_([^_]*)$"""  ,						UpperCamelCase__	)
							if matches:
														_SCREAMING_SNAKE_CASE		:    Tuple      	=     float(matches[1]	)
														_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     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		:    Any      	=     1_001
							_SCREAMING_SNAKE_CASE		:    str      	=     """imagenet-1k-id2label.json"""
							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     """huggingface/label-files"""
							_SCREAMING_SNAKE_CASE		:    List[Any]      	=     json.load(open(hf_hub_download(UpperCamelCase__  ,						UpperCamelCase__  ,						repo_type="""dataset"""	)  ,						"""r"""	)	)
							_SCREAMING_SNAKE_CASE		:    int      	=     {int(UpperCamelCase__	) + 1: v for k, v in idalabel.items()}
							_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     """background"""
							_SCREAMING_SNAKE_CASE		:    int      	=     idalabel
							_SCREAMING_SNAKE_CASE		:    List[str]      	=     {v: k for k, v in idalabel.items()}

							return config



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



@torch.no_grad()
def   lowerCamelCase_(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE=False	)->     Dict:
							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     get_mobilenet_va_config(UpperCamelCase__	)

							# Load 🤗 model
							_SCREAMING_SNAKE_CASE		:    List[Any]      	=     MobileNetVaForImageClassification(UpperCamelCase__	).eval()

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

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

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

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

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

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

							if push_to_hub:
														print("""Pushing to the hub..."""	)
														_SCREAMING_SNAKE_CASE		:    Tuple      	=     """google/""" + model_name
														image_processor.push_to_hub(UpperCamelCase__	)
														model.push_to_hub(UpperCamelCase__	)


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
			)









class        SCREAMING_SNAKE_CASE__					:



 def __init__(				self,__lowerCamelCase					):
        A__		   =		size
        A__		   =		[0] * size
        A__		   =		[0] * size



 @staticmethod
 def 							UpperCamelCase     (				__lowerCamelCase					):
        return index | (index + 1)



 @staticmethod
 def 							UpperCamelCase     (				__lowerCamelCase					):
        return (index & (index + 1)) - 1



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		value
        while index < self.size:
               A__		   =		self.get_prev(__lowerCamelCase					) + 1
               if current_left_border == index:
                      A__		   =		value
               else:
                      A__		   =		max(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
               A__		   =		self.get_next(__lowerCamelCase					)




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        right -= 1  # Because of right is exclusive
        A__		   =		0
        while left <= right:
               A__		   =		self.get_prev(__lowerCamelCase					)
               if left <= current_left:
                      A__		   =		max(__lowerCamelCase,self.tree[right]					)
                      A__		   =		current_left
               else:
                      A__		   =		max(__lowerCamelCase,self.arr[right]					)
                      right -= 1
        return result


if __name__ == "__main__":
       import doctest

       doctest.testmod()





