


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







def   lowerCamelCase(				a__):
				_SCREAMING_SNAKE_CASE     					=MobileNetVaConfig(layer_norm_eps=0.001)

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

				_SCREAMING_SNAKE_CASE     					=re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''		,a__)
				if matches:
								_SCREAMING_SNAKE_CASE     					=float(matches[1])
								_SCREAMING_SNAKE_CASE     					=int(matches[2])

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

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

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

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

				return config







def   lowerCamelCase(				):
				_SCREAMING_SNAKE_CASE     					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_SCREAMING_SNAKE_CASE     					=Image.open(requests.get(a__		,stream=a__).raw)
				return im







@torch.no_grad()
def   lowerCamelCase(				a__		,a__		,a__		,a__=False):
				_SCREAMING_SNAKE_CASE     					=get_mobilenet_va_config(a__)

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

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

								if model_name == "deeplabv3_mobilenet_v2_1.0_513":
												_SCREAMING_SNAKE_CASE     					=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, 1001)

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


if __name__ == "__main__":
							snake_case_  :     str							  =							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.'''
							)

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



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class   A__							:




						def __init__(    self   : List[Any]			,      _a   : List[str]			,      _a   : Optional[Any]=sys.maxsize					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''bilinear'''
										_SCREAMING_SNAKE_CASE     					=max_size
										_SCREAMING_SNAKE_CASE     					=short_edge_length




						def __call__(    self   : Any			,      _a   : Tuple					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=[]
										for img in imgs:
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=img.shape[:2]
														# later: provide list and randomly choose index for resize
														_SCREAMING_SNAKE_CASE     					=np.random.randint(self.short_edge_length[0]			,      self.short_edge_length[1] + 1					)
														if size == 0:
																		return img
														_SCREAMING_SNAKE_CASE     					=size * 1.0 / min(_a			,      _a					)
														if h < w:
																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=size, scale * w
														else:
																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=scale * h, size
														if max(_a			,      _a					) > self.max_size:
																		_SCREAMING_SNAKE_CASE     					=self.max_size * 1.0 / max(_a			,      _a					)
																		_SCREAMING_SNAKE_CASE     					=newh * scale
																		_SCREAMING_SNAKE_CASE     					=neww * scale
														_SCREAMING_SNAKE_CASE     					=int(neww + 0.5					)
														_SCREAMING_SNAKE_CASE     					=int(newh + 0.5					)

														if img.dtype == np.uinta:
																		_SCREAMING_SNAKE_CASE     					=Image.fromarray(_a					)
																		_SCREAMING_SNAKE_CASE     					=pil_image.resize((neww, newh)			,      PILImageResampling.BILINEAR					)
																		_SCREAMING_SNAKE_CASE     					=np.asarray(_a					)
														else:
																		_SCREAMING_SNAKE_CASE     					=img.permute(2			,      0			,      1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																		_SCREAMING_SNAKE_CASE     					=nn.functional.interpolate(
																		    _a			,      (newh, neww)			,      mode=self.interp_method			,      align_corners=_a					).squeeze(0					)
														img_augs.append(_a					)

										return img_augs

class   A__							:




						def __init__(    self   : Any			,      _a   : Tuple					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,      cfg.INPUT.MAX_SIZE_TEST					)
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.FORMAT
										_SCREAMING_SNAKE_CASE     					=cfg.SIZE_DIVISIBILITY
										_SCREAMING_SNAKE_CASE     					=cfg.PAD_VALUE
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.MAX_SIZE_TEST
										_SCREAMING_SNAKE_CASE     					=cfg.MODEL.DEVICE
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=lambda _a					: (x - self.pixel_mean) / self.pixel_std




						def 							__UpperCamelCase			(    self   : int			,      _a   : Optional[Any]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=tuple(max(_a					) for s in zip(*[img.shape for img in images]					)					)
										_SCREAMING_SNAKE_CASE     					=[im.shape[-2:] for im in images]
										_SCREAMING_SNAKE_CASE     					=[
										    nn.functional.pad(
										        _a			,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,      value=self.pad_value			,      )
										    for size, im in zip(_a			,      _a					)
										]

										return torch.stack(_a					), torch.tensor(_a					)




						def __call__(    self   : str			,      _a   : Union[str, Any]			,      _a   : Any=False					)							->       Tuple:


										"""simple docstring"""





										with torch.no_grad():
														if not isinstance(_a			,      _a					):
																		_SCREAMING_SNAKE_CASE     					=[images]
														if single_image:
																		assert len(_a					) == 1
														for i in range(len(_a					)					):
																		if isinstance(images[i]			,      torch.Tensor					):
																						images.insert(_a			,      images.pop(_a					).to(self.device					).float()					)
																		elif not isinstance(images[i]			,      torch.Tensor					):
																						images.insert(
																						    _a			,      torch.as_tensor(img_tensorize(images.pop(_a					)			,      input_format=self.input_format					)					)
																						    .to(self.device					)
																						    .float()			,      )
            # resize smallest edge
														_SCREAMING_SNAKE_CASE     					=torch.tensor([im.shape[:2] for im in images]					)
														_SCREAMING_SNAKE_CASE     					=self.aug(_a					)
														# transpose images and convert to torch tensors
														# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
														# now normalize before pad to avoid useless arithmetic
														_SCREAMING_SNAKE_CASE     					=[self.normalizer(_a					) for x in images]
														# now pad them to do the following operations
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.pad(_a					)
														# Normalize

														if self.size_divisibility > 0:
																		raise NotImplementedError()
														# pad
														_SCREAMING_SNAKE_CASE     					=torch.true_divide(_a			,      _a					)
														if single_image:
																		return images[0], sizes[0], scales_yx[0]
														else:
																		return images, sizes, scales_yx







def   lowerCamelCase(				a__		,a__):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def   lowerCamelCase(				a__		,a__):
				assert torch.isfinite(a__).all(), "Box tensor contains infinite or NaN!"
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=box_size
				tensor[:, 0].clamp_(min=0		,max=a__)
				tensor[:, 1].clamp_(min=0		,max=a__)
				tensor[:, 2].clamp_(min=0		,max=a__)
				tensor[:, 3].clamp_(min=0		,max=a__)