



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_	       =   logging.get_logger(__name__)







def 				A			(		__UpperCAmelCase	)       ->       List[str]:



						'''simple docstring'''






						UpperCAmelCase_     							=     MobileNetVaConfig(layer_norm_eps=0.001	)

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

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

						if model_name.startswith('''deeplabv3_'''	):
												UpperCAmelCase_     							=     8
												UpperCAmelCase_     							=     21
												UpperCAmelCase_     							=     '''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_     							=     1001
												UpperCAmelCase_     							=     '''imagenet-1k-id2label.json'''

						UpperCAmelCase_     							=     '''huggingface/label-files'''
						UpperCAmelCase_     							=     json.load(open(hf_hub_download(__UpperCAmelCase							,					__UpperCAmelCase							,					repo_type='''dataset'''	)							,					'''r'''	)	)

						if config.num_labels == 1001:
												UpperCAmelCase_     							=     {int(__UpperCAmelCase	) + 1: v for k, v in idalabel.items()}
												UpperCAmelCase_     							=     '''background'''
						else:
												UpperCAmelCase_     							=     {int(__UpperCAmelCase	): v for k, v in idalabel.items()}

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

						return config







def 				A			(		)       ->       List[Any]:



						'''simple docstring'''






						UpperCAmelCase_     							=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase_     							=     Image.open(requests.get(__UpperCAmelCase							,					stream=__UpperCAmelCase	).raw	)
						return im







@torch.no_grad()
def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase=False	)       ->       Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_     							=     get_mobilenet_va_config(__UpperCAmelCase	)

						# Load 🤗 model
						if model_name.startswith('''deeplabv3_'''	):
												UpperCAmelCase_     							=     MobileNetVaForSemanticSegmentation(__UpperCAmelCase	).eval()
						else:
												UpperCAmelCase_     							=     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
						UpperCAmelCase_     							=     MobileNetVaImageProcessor(
						    crop_size={'''width''': config.image_size, '''height''': config.image_size}							,					size={'''shortest_edge''': config.image_size + 32}							,					)
						UpperCAmelCase_     							=     image_processor(images=prepare_img()							,					return_tensors='''pt'''	)
						UpperCAmelCase_     							=     model(**__UpperCAmelCase	)
						UpperCAmelCase_     							=     outputs.logits

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

												if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																		UpperCAmelCase_     							=     torch.tensor(
																		    [
																		        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
																		        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
																		        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
																		    ]	)

												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":
																		UpperCAmelCase_     							=     torch.tensor([0.0_181, -1.0_015, 0.4_688]	)
												elif model_name == "mobilenet_v2_1.0_224":
																		UpperCAmelCase_     							=     torch.tensor([0.2_445, -1.1_993, 0.1_905]	)
												elif model_name == "mobilenet_v2_0.75_160":
																		UpperCAmelCase_     							=     torch.tensor([0.2_482, 0.4_136, 0.6_669]	)
												elif model_name == "mobilenet_v2_0.35_96":
																		UpperCAmelCase_     							=     torch.tensor([0.1_451, -0.4_624, 0.7_192]	)
												else:
																		UpperCAmelCase_     							=     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...'''	)
												UpperCAmelCase_     							=     '''google/''' + model_name
												image_processor.push_to_hub(__UpperCAmelCase	)
												model.push_to_hub(__UpperCAmelCase	)


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








def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Tuple:



    """simple docstring"""






    if not root or root not in binary_tree_mirror_dictionary:
        return
    UpperCamelCase_   ,    UpperCamelCase_          =		binary_tree_mirror_dictionary[root][:2]
    UpperCamelCase_          =		[right_child, left_child]
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       )->		dict:



    """simple docstring"""






    if not binary_tree:
        raise ValueError("binary tree cannot be empty"       )
    if root not in binary_tree:
        UpperCamelCase_          =		f"root {root} is not present in the binary_tree"
        raise ValueError(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		dict(SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Dict      =			{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
 print(F'''Binary tree: {binary_tree}''')
 SCREAMING_SNAKE_CASE						:str      =			binary_tree_mirror(binary_tree, 5)
 print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')
