




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





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Tuple							       =      MobileNetVaConfig(layer_norm_eps=0.001						)

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

    _a				:							Dict							       =      re.match(r"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""   ,	UpperCAmelCase_						)
    if matches:
        _a				:							Optional[int]							       =      float(matches[1]						)
        _a				:							List[str]							       =      int(matches[2]						)

    if model_name.startswith("""deeplabv3_"""						):
        _a				:							Any							       =      8
        _a				:							Tuple							       =      21
        _a				:							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".
        _a				:							str							       =      1_001
        _a				:							List[str]							       =      """imagenet-1k-id2label.json"""

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

    if config.num_labels == 1_001:
        _a				:							Union[str, Any]							       =      {int(UpperCAmelCase_						) + 1: v for k, v in idalabel.items()}
        _a				:							Tuple							       =      """background"""
    else:
        _a				:							Dict							       =      {int(UpperCAmelCase_						): v for k, v in idalabel.items()}

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

    return config





def UpperCAmelCase    (       ):
    _a				:							List[Any]							       =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
    _a				:							List[Any]							       =      Image.open(requests.get(UpperCAmelCase_   ,	stream=UpperCAmelCase_						).raw						)
    return im





@torch.no_grad()
def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_=False						):
    _a				:							Any							       =      get_mobilenet_va_config(UpperCAmelCase_						)

    # Load 🤗 model
    if model_name.startswith("""deeplabv3_"""						):
        _a				:							Dict							       =      MobileNetVaForSemanticSegmentation(UpperCAmelCase_						).eval()
    else:
        _a				:							Optional[int]							       =      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
    _a				:							Optional[int]							       =      MobileNetVaImageProcessor(
        crop_size={"""width""": config.image_size, """height""": config.image_size}   ,	size={"""shortest_edge""": config.image_size + 32}   ,	)
    _a				:							List[Any]							       =      image_processor(images=prepare_img()   ,	return_tensors="""pt"""						)
    _a				:							Optional[int]							       =      model(**UpperCAmelCase_						)
    _a				:							int							       =      outputs.logits

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

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            _a				:							Union[str, 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]   ,	UpperCAmelCase_   ,	atol=1e-4						)
    else:
        assert logits.shape == (1, 1_001)

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


if __name__ == "__main__":
  a		:	Optional[int]       =      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		:	Tuple       =      parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )






from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

