





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Dict					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     Optional[Any]=None					,      __lowerCamelCase							:     Optional[Any]=None   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     data
			__lowercase											=     previous
			__lowercase											=     next_node




	def __str__(						self							:     int   )       -> str:

			'''simple docstring'''




			return F"{self.data}"




	def    UpperCAmelCase			(						self							:     int   )       -> int:

			'''simple docstring'''




			return self.data




	def    UpperCAmelCase			(						self							:     Tuple   )       -> Optional[int]:

			'''simple docstring'''




			return self.next






	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			return self.previous





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Tuple					,      __lowerCamelCase							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			__lowercase											=     head




	def __iter__(						self							:     Union[str, Any]   )       -> List[Any]:

			'''simple docstring'''




			return self






	def    UpperCAmelCase			(						self							:     str   )       -> str:

			'''simple docstring'''




			if not self.current:
					raise StopIteration
			else:
					__lowercase											=     self.current.get_data()
					__lowercase											=     self.current.get_next()
					return value





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     List[str]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     None  # First node in list
			__lowercase											=     None  # Last node in list




	def __str__(						self							:     int   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.head
			__lowercase											=     []
			while current is not None:
					nodes.append(current.get_data()   )
					__lowercase											=     current.get_next()
			return " ".join(str(__lowerCamelCase   ) for node in nodes   )




	def __contains__(						self							:     Tuple					,      __lowerCamelCase							:     int   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     self.head
			while current:
					if current.get_data() == value:
							return True
					__lowercase											=     current.get_next()
			return False




	def __iter__(						self							:     Optional[int]   )       -> List[str]:

			'''simple docstring'''




			return LinkedListIterator(self.head   )




	def    UpperCAmelCase			(						self							:     Any   )       -> str:

			'''simple docstring'''




			if self.head:
					return self.head.get_data()
			return None




	def    UpperCAmelCase			(						self							:     str   )       -> Union[str, Any]:

			'''simple docstring'''




			if self.tail:
					return self.tail.get_data()
			return None




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			if self.head is None:
					__lowercase											=     node
					__lowercase											=     node
			else:
					self.insert_before_node(self.head					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			if self.head is None:
					self.set_head(__lowerCamelCase   )
			else:
					self.insert_after_node(self.tail					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     Node(__lowerCamelCase   )
			if self.head is None:
					self.set_head(__lowerCamelCase   )
			else:
					self.set_tail(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Node					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			__lowercase											=     node
			__lowercase											=     node.previous

			if node.get_previous() is None:
					__lowercase											=     node_to_insert
			else:
					__lowercase											=     node_to_insert

			__lowercase											=     node_to_insert




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     Node					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			__lowercase											=     node
			__lowercase											=     node.next

			if node.get_next() is None:
					__lowercase											=     node_to_insert
			else:
					__lowercase											=     node_to_insert

			__lowercase											=     node_to_insert




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     1
			__lowercase											=     Node(__lowerCamelCase   )
			__lowercase											=     self.head
			while node:
					if current_position == position:
							self.insert_before_node(__lowerCamelCase					,      __lowerCamelCase   )
							return
					current_position += 1
					__lowercase											=     node.next
			self.insert_after_node(self.tail					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     int   )       -> Node:

			'''simple docstring'''




			__lowercase											=     self.head
			while node:
					if node.get_data() == item:
							return node
					__lowercase											=     node.get_next()
			raise Exception('Node not found'   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     List[str]   )       -> Dict:

			'''simple docstring'''




			if (node := self.get_node(__lowerCamelCase   )) is not None:
					if node == self.head:
							__lowercase											=     self.head.get_next()

					if node == self.tail:
							__lowercase											=     self.tail.get_previous()

					self.remove_node_pointers(__lowerCamelCase   )




	@staticmethod
	def    UpperCAmelCase			(						__lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			if node.get_next():
					__lowercase											=     node.previous

			if node.get_previous():
					__lowercase											=     node.next

			__lowercase											=     None
			__lowercase											=     None






	def    UpperCAmelCase			(						self							:     str   )       -> List[str]:

			'''simple docstring'''




			return self.head is None





def  SCREAMING_SNAKE_CASE				(      )		->    None:
		pass


if __name__ == "__main__":
		import doctest

		doctest.testmod()











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()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

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

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

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

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

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

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

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

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

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

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

		if push_to_hub:
				print('Pushing to the hub...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




