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


logging.set_verbosity_info()
__A 		= logging.get_logger(__name__)



def 					_lowerCamelCase(__UpperCamelCase )       ->				Union[str, Any]:
	_lowerCAmelCase			=MobileNetVaConfig(layer_norm_eps=0.0_01 )

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

	_lowerCAmelCase			=re.match(R"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""					,				__UpperCamelCase )
	if matches:
		_lowerCAmelCase			=float(matches[1] )
		_lowerCAmelCase			=int(matches[2] )

	if model_name.startswith("""deeplabv3_""" ):
		_lowerCAmelCase			=8
		_lowerCAmelCase			=21
		_lowerCAmelCase			="""pascal-voc-id2label.json"""
	else:
		# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
		# of the usual 1000. The first class (index 0) is "background".
		_lowerCAmelCase			=1001
		_lowerCAmelCase			="""imagenet-1k-id2label.json"""

	_lowerCAmelCase			="""huggingface/label-files"""
	_lowerCAmelCase			=json.load(open(hf_hub_download(__UpperCamelCase					,				__UpperCamelCase					,				repo_type="""dataset""" )					,				"""r""" ) )

	if config.num_labels == 1001:
		_lowerCAmelCase			={int(__UpperCamelCase ) + 1: v for k, v in idalabel.items()}
		_lowerCAmelCase			="""background"""
	else:
		_lowerCAmelCase			={int(__UpperCamelCase ): v for k, v in idalabel.items()}

	_lowerCAmelCase			=idalabel
	_lowerCAmelCase			={v: k for k, v in idalabel.items()}

	return config



def 					_lowerCamelCase()       ->				Any:
	_lowerCAmelCase			="""http://images.cocodataset.org/val2017/000000039769.jpg"""
	_lowerCAmelCase			=Image.open(requests.get(__UpperCamelCase					,				stream=__UpperCamelCase ).raw )
	return im



@torch.no_grad()
def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase=False )       ->				List[Any]:
	_lowerCAmelCase			=get_mobilenet_va_config(__UpperCamelCase )

	# Load 🤗 model
	if model_name.startswith("""deeplabv3_""" ):
		_lowerCAmelCase			=MobileNetVaForSemanticSegmentation(__UpperCamelCase ).eval()
	else:
		_lowerCAmelCase			=MobileNetVaForImageClassification(__UpperCamelCase ).eval()

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

	# Check outputs on an image, prepared by MobileNetV2ImageProcessor
	_lowerCAmelCase			=MobileNetVaImageProcessor(
	    crop_size={"""width""": config.image_size, """height""": config.image_size}					,				size={"""shortest_edge""": config.image_size + 32}					,				)
	_lowerCAmelCase			=image_processor(images=prepare_img()					,				return_tensors="""pt""" )
	_lowerCAmelCase			=model(**__UpperCamelCase )
	_lowerCAmelCase			=outputs.logits

	if model_name.startswith("""deeplabv3_""" ):
		assert logits.shape == (1, 21, 65, 65)

		if model_name == "deeplabv3_mobilenet_v2_1.0_513":
			_lowerCAmelCase			=torch.tensor(
			    [
			        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
			        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
			        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
			    ] )

		else:
			raise ValueError(F'''Unknown model name: {model_name}''' )

		assert torch.allclose(logits[0, :3, :3, :3]					,				__UpperCamelCase					,				atol=1E-4 )
	else:
		assert logits.shape == (1, 1001)

		if model_name == "mobilenet_v2_1.4_224":
			_lowerCAmelCase			=torch.tensor([0.01_81, -1.00_15, 0.46_88] )
		elif model_name == "mobilenet_v2_1.0_224":
			_lowerCAmelCase			=torch.tensor([0.24_45, -1.19_93, 0.19_05] )
		elif model_name == "mobilenet_v2_0.75_160":
			_lowerCAmelCase			=torch.tensor([0.24_82, 0.41_36, 0.66_69] )
		elif model_name == "mobilenet_v2_0.35_96":
			_lowerCAmelCase			=torch.tensor([0.14_51, -0.46_24, 0.71_92] )
		else:
			_lowerCAmelCase			=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...""" )
		_lowerCAmelCase			="""google/""" + model_name
		image_processor.push_to_hub(__UpperCamelCase )
		model.push_to_hub(__UpperCamelCase )


if __name__ == "__main__":
			__A 		= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--model_name',
			    default='mobilenet_v2_1.0_224',
			    type=str,
			    help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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.'
			)

			__A 		= parser.parse_args()
			convert_movilevit_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)





"""simple docstring"""


class 	lowerCamelCase__      :





			'''simple docstring'''



			def __init__(							self  ,  __UpperCAmelCase=None						)				->  List[Any]:
				_lowerCAmelCase			=data
				_lowerCAmelCase			=None



			def __repr__(							self						)				->  int:
				_lowerCAmelCase			=""""""
				_lowerCAmelCase			=self
				while temp:
					string_rep += f'''<{temp.data}> ---> '''
					_lowerCAmelCase			=temp.next
				string_rep += "<END>"
				return string_rep







def 					_lowerCamelCase(__UpperCamelCase )       ->				str:

	# if elements_list is empty
	if not elements_list:
		raise Exception("""The Elements List is empty""" )

	# Set first element as Head
	_lowerCAmelCase			=Node(elements_list[0] )
	_lowerCAmelCase			=head
	# Loop through elements from position 1
	for data in elements_list[1:]:
		_lowerCAmelCase			=Node(__UpperCamelCase )
		_lowerCAmelCase			=current.next
	return head


__A 		= [1, 3, 5, 32, 44, 12, 43]
print(F"""List: {list_data}""")
print('Creating Linked List from List.')
__A 		= make_linked_list(list_data)
print('Linked List:')

print(linked_list)



