






'''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()
_UpperCamelCase		:		int								=			logging.get_logger(__name__)
def __UpperCAmelCase (			A			:	Union[str, Any]						)  -> Union[str, Any]:
			UpperCAmelCase_     :   int       =   MobileNetVaConfig(layer_norm_eps=0.001						)

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

			UpperCAmelCase_     :   Union[str, Any]       =   re.match(r'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''	,   A						)
			if matches:
						UpperCAmelCase_     :   int       =   float(matches[1]						)
						UpperCAmelCase_     :   Union[str, Any]       =   int(matches[2]						)

			if model_name.startswith('''deeplabv3_'''						):
						UpperCAmelCase_     :   str       =   8
						UpperCAmelCase_     :   List[Any]       =   2_1
						UpperCAmelCase_     :   Tuple       =   '''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".
						UpperCAmelCase_     :   str       =   1_0_0_1
						UpperCAmelCase_     :   List[Any]       =   '''imagenet-1k-id2label.json'''

			UpperCAmelCase_     :   Tuple       =   '''huggingface/label-files'''
			UpperCAmelCase_     :   Union[str, Any]       =   json.load(open(hf_hub_download(A	,   A	,   repo_type='''dataset'''						)	,   '''r'''						)						)

			if config.num_labels == 1_0_0_1:
						UpperCAmelCase_     :   Tuple       =   {int(A						) + 1: v for k, v in idalabel.items()}
						UpperCAmelCase_     :   Optional[int]       =   '''background'''
			else:
						UpperCAmelCase_     :   Optional[Any]       =   {int(A						): v for k, v in idalabel.items()}

			UpperCAmelCase_     :   Union[str, Any]       =   idalabel
			UpperCAmelCase_     :   Optional[Any]       =   {v: k for k, v in idalabel.items()}

			return config
def __UpperCAmelCase (			)  -> str:
			UpperCAmelCase_     :   int       =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			UpperCAmelCase_     :   List[str]       =   Image.open(requests.get(A	,   stream=A						).raw						)
			return im
@torch.no_grad()
def __UpperCAmelCase (			A			:	str	,   A			:	Optional[int]	,   A			:	int	,   A			:	Tuple=False						)  -> Tuple:
			UpperCAmelCase_     :   int       =   get_mobilenet_va_config(A						)

			# Load 🤗 model
			if model_name.startswith('''deeplabv3_'''						):
						UpperCAmelCase_     :   Tuple       =   MobileNetVaForSemanticSegmentation(A						).eval()
			else:
						UpperCAmelCase_     :   Dict       =   MobileNetVaForImageClassification(A						).eval()

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

			# Check outputs on an image, prepared by MobileNetV2ImageProcessor
			UpperCAmelCase_     :   Tuple       =   MobileNetVaImageProcessor(
			    crop_size={'''width''': config.image_size, '''height''': config.image_size}	,   size={'''shortest_edge''': config.image_size + 3_2}	,   )
			UpperCAmelCase_     :   Tuple       =   image_processor(images=prepare_img()	,   return_tensors='''pt'''						)
			UpperCAmelCase_     :   List[str]       =   model(**A						)
			UpperCAmelCase_     :   Union[str, Any]       =   outputs.logits

			if model_name.startswith('''deeplabv3_'''						):
						assert logits.shape == (1, 2_1, 6_5, 6_5)

						if model_name == "deeplabv3_mobilenet_v2_1.0_513":
									UpperCAmelCase_     :   List[Any]       =   torch.tensor(
									    [
									        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
									        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
									        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
									    ]						)

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

						assert torch.allclose(logits[0, :3, :3, :3]	,   A	,   atol=1e-4						)
			else:
						assert logits.shape == (1, 1_0_0_1)

						if model_name == "mobilenet_v2_1.4_224":
									UpperCAmelCase_     :   Optional[Any]       =   torch.tensor([0.0181, -1.0015, 0.4688]						)
						elif model_name == "mobilenet_v2_1.0_224":
									UpperCAmelCase_     :   List[Any]       =   torch.tensor([0.2445, -1.1993, 0.1905]						)
						elif model_name == "mobilenet_v2_0.75_160":
									UpperCAmelCase_     :   List[Any]       =   torch.tensor([0.2482, 0.4136, 0.6669]						)
						elif model_name == "mobilenet_v2_0.35_96":
									UpperCAmelCase_     :   Tuple       =   torch.tensor([0.1451, -0.4624, 0.7192]						)
						else:
									UpperCAmelCase_     :   Dict       =   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...'''						)
						UpperCAmelCase_     :   Any       =   '''google/''' + model_name
						image_processor.push_to_hub(A						)
						model.push_to_hub(A						)


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

						_UpperCamelCase		:		Optional[Any]								=			parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)










'''simple docstring'''





def __UpperCAmelCase (			A			:	list[list[int]]						)  -> bool:
			# must be
			UpperCAmelCase_     :   int       =   [list(A						) for row in matrix]
			if matrix and isinstance(A	,   A						):
						if isinstance(matrix[0]	,   A						):
									UpperCAmelCase_     :   Optional[Any]       =   0
									for row in matrix:
												if prev_len == 0:
															UpperCAmelCase_     :   List[str]       =   len(A						)
															UpperCAmelCase_     :   Any       =   True
												else:
															UpperCAmelCase_     :   Tuple       =   prev_len == len(A						)
						else:
									UpperCAmelCase_     :   Any       =   True
			else:
						UpperCAmelCase_     :   Dict       =   False

			return result
def __UpperCAmelCase (			A			:	list[list[int]]						)  -> None:
			if check_matrix(A						) and len(A						) > 0:
						UpperCAmelCase_     :   Dict       =   [list(A						) for row in a]
						UpperCAmelCase_     :   List[Any]       =   len(A						)
						if isinstance(a[0]	,   A						):
									UpperCAmelCase_     :   Any       =   len(a[0]						)
						else:
									for dat in a:
												print(A						)
									return

						# horizotal printing increasing
						for i in range(0	,   A						):
									print(a[0][i]						)
						# vertical printing down
						for i in range(1	,   A						):
									print(a[i][mat_col - 1]						)
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2	,   -1	,   -1						):
												print(a[mat_row - 1][i]						)
        # vertical printing up
						for i in range(mat_row - 2	,   0	,   -1						):
									print(a[i][0]						)
						UpperCAmelCase_     :   Union[str, Any]       =   [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(A						) > 0:
									spiral_print_clockwise(A						)
						else:
									return
			else:
						print('''Not a valid matrix'''						)
						return
def __UpperCAmelCase (			A			:	list[list]						)  -> list[int]:
			if matrix:
						return list(matrix.pop(0						)						) + spiral_traversal(list(zip(*A						)						)[::-1]						)
			else:
						return []


# driver code
if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_UpperCamelCase		:		List[Any]								=			[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
						spiral_print_clockwise(a)


