



import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class    _SCREAMING_SNAKE_CASE						(enum.Enum			):
     lowerCAmelCase       =      """timm"""
     lowerCAmelCase       =      """transformers"""






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[Iterable[str]]		,						__lowerCamelCase :  Optional[Iterable[int]]		,						__lowerCamelCase :  Optional[Iterable[str]]  ):



 """simple docstring"""





 if stage_names is None:
  raise ValueError("Stage_names must be set for transformers backbones"  )

 if out_features is not None:
  if not isinstance(__lowerCamelCase		,						(list,)  ):
   raise ValueError(F"""out_features must be a list {type(__lowerCamelCase  )}"""  )
  if any(feat not in stage_names for feat in out_features  ):
   raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""  )

 if out_indices is not None:
  if not isinstance(__lowerCamelCase		,						(list, tuple)  ):
   raise ValueError(F"""out_indices must be a list or tuple, got {type(__lowerCamelCase  )}"""  )
  if any(idx >= len(__lowerCamelCase  ) for idx in out_indices  ):
   raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"  )

 if out_features is not None and out_indices is not None:
  if len(__lowerCamelCase  ) != len(__lowerCamelCase  ):
   raise ValueError("out_features and out_indices should have the same length if both are set"  )
  if out_features != [stage_names[idx] for idx in out_indices]:
   raise ValueError("out_features and out_indices should correspond to the same stages if both are set"  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[List[str]]		,						__lowerCamelCase :  Optional[Union[List[int], Tuple[int]]]		,						__lowerCamelCase :  List[str]		,						):



 """simple docstring"""





 if out_indices is None and out_features is None:
  __SCREAMING_SNAKE_CASE							:     List[str]							=						[len(__lowerCamelCase  ) - 1]
  __SCREAMING_SNAKE_CASE							:     Dict							=						[stage_names[-1]]
 elif out_indices is None and out_features is not None:
  __SCREAMING_SNAKE_CASE							:     int							=						[stage_names.index(__lowerCamelCase  ) for layer in out_features]
 elif out_features is None and out_indices is not None:
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[stage_names[idx] for idx in out_indices]
 return out_features, out_indices






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[List[str]]		,						__lowerCamelCase :  Optional[Union[List[int], Tuple[int]]]		,						__lowerCamelCase :  List[str]		,						):



 """simple docstring"""





 verify_out_features_out_indices(out_features=__lowerCamelCase		,						out_indices=__lowerCamelCase		,						stage_names=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     int							=						_align_output_features_output_indices(
     out_features=__lowerCamelCase		,						out_indices=__lowerCamelCase		,						stage_names=__lowerCamelCase  )
 # Verify that the aligned out_features and out_indices are valid
 verify_out_features_out_indices(out_features=__lowerCamelCase		,						out_indices=__lowerCamelCase		,						stage_names=__lowerCamelCase  )
 return output_features, output_indices

class    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      None





     def 						__snake_case     (							self			:				Optional[int]  ,	UpperCamelCase			:				Optional[int]					)->None:
      if getattr(self  ,	"_backbone"  ,	UpperCamelCase					) is None:
       raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

      # These will diagree with the defaults for the transformers models e.g. for resnet50
      # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
      # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
      __SCREAMING_SNAKE_CASE							:     Tuple							=						[stage["module"] for stage in self._backbone.feature_info.info]
      __SCREAMING_SNAKE_CASE							:     Any							=						[stage["num_chs"] for stage in self._backbone.feature_info.info]
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self._backbone.feature_info.out_indices
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						out_features, out_indices





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				Tuple					)->None:
      __SCREAMING_SNAKE_CASE							:     Dict							=						getattr(UpperCamelCase  ,	"stage_names"					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						getattr(UpperCamelCase  ,	"out_features"  ,	UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						getattr(UpperCamelCase  ,	"out_indices"  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Dict							=						stage_names
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[int]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=UpperCamelCase					)
      # Number of channels for each stage. This is set in the transformer backbone model init
      __SCREAMING_SNAKE_CASE							:     str							=						None





     def 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				Optional[int]					)->None:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						config

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						getattr(UpperCamelCase  ,	"use_timm_backbone"  ,	UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     int							=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

      if self.backbone_type == BackboneType.TIMM:
       self._init_timm_backbone(UpperCamelCase					)
      elif self.backbone_type == BackboneType.TRANSFORMERS:
       self._init_transformers_backbone(UpperCamelCase					)
      else:
       raise ValueError(F"""backbone_type {self.backbone_type} not supported."""					)





     @property
     def 						__snake_case     (							self			:				str					)->List[Any]:
      return self._out_features





     @out_features.setter
     def 						__snake_case     (							self			:				List[str]  ,	UpperCamelCase			:				List[str]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)





     @property
     def 						__snake_case     (							self			:				Optional[Any]					)->int:
      return self._out_indices





     @out_indices.setter
     def 						__snake_case     (							self			:				Union[str, Any]  ,	UpperCamelCase			:				Union[Tuple[int], List[int]]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)





     @property
     def 						__snake_case     (							self			:				Union[str, Any]					)->int:
      # the current backbones will output the number of channels for each stage
      # even if that stage is not in the out_features list.
      return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}





     @property
     def 						__snake_case     (							self			:				Tuple					)->Optional[Any]:
      return [self.out_feature_channels[name] for name in self.out_features]





     def 						__snake_case     (							self			:				Optional[int]  ,	*UpperCamelCase			:				str  ,	**UpperCamelCase			:				Union[str, Any]					)->str:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						dict(inspect.signature(self.forward					).parameters					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						{k: v for k, v in kwargs.items() if k in signature}
      return self(*UpperCamelCase  ,	**UpperCamelCase					)





     def 						__snake_case     (							self			:				int  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Optional[bool] = None  ,	UpperCamelCase			:				Optional[bool] = None  ,	UpperCamelCase			:				Optional[bool] = None  ,	)->Dict:
      raise NotImplementedError("This method should be implemented by the derived class."					)






     def 						__snake_case     (							self			:				List[str]					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						super().to_dict()
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						output.pop("_out_features"					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.pop("_out_indices"					)
      return output






class    _SCREAMING_SNAKE_CASE						:





     @property
     def 						__snake_case     (							self			:				List[str]					)->Any:
      return self._out_features





     @out_features.setter
     def 						__snake_case     (							self			:				Optional[Any]  ,	UpperCamelCase			:				List[str]					)->str:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[int]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)





     @property
     def 						__snake_case     (							self			:				Dict					)->List[str]:
      return self._out_indices





     @out_indices.setter
     def 						__snake_case     (							self			:				str  ,	UpperCamelCase			:				Union[Tuple[int], List[int]]					)->str:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)






     def 						__snake_case     (							self			:				int					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						super().to_dict()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.pop("_out_features"					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						output.pop("_out_indices"					)
      return output











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()
_lowerCamelCase         =	logging.get_logger(__name__)






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						MobileNetVaConfig(layer_norm_eps=0.001  )

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

 __SCREAMING_SNAKE_CASE							:     Tuple							=						re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"		,						__lowerCamelCase  )
 if matches:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						float(matches[1]  )
  __SCREAMING_SNAKE_CASE							:     str							=						int(matches[2]  )

 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						8
  __SCREAMING_SNAKE_CASE							:     Dict							=						21
  __SCREAMING_SNAKE_CASE							:     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".
  __SCREAMING_SNAKE_CASE							:     int							=						1001
  __SCREAMING_SNAKE_CASE							:     Dict							=						"imagenet-1k-id2label.json"

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"huggingface/label-files"
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						json.load(open(hf_hub_download(__lowerCamelCase		,						__lowerCamelCase		,						repo_type="dataset"  )		,						"r"  )  )

 if config.num_labels == 1001:
  __SCREAMING_SNAKE_CASE							:     int							=						{int(__lowerCamelCase  ) + 1: v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE							:     int							=						"background"
 else:
  __SCREAMING_SNAKE_CASE							:     Any							=						{int(__lowerCamelCase  ): v for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE							:     str							=						idalabel
 __SCREAMING_SNAKE_CASE							:     Tuple							=						{v: k for k, v in idalabel.items()}

 return config






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						"http://images.cocodataset.org/val2017/000000039769.jpg"
 __SCREAMING_SNAKE_CASE							:     Any							=						Image.open(requests.get(__lowerCamelCase		,						stream=__lowerCamelCase  ).raw  )
 return im






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Tuple=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						get_mobilenet_va_config(__lowerCamelCase  )

 # Load 🤗 model
 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						MobileNetVaForSemanticSegmentation(__lowerCamelCase  ).eval()
 else:
  __SCREAMING_SNAKE_CASE							:     str							=						MobileNetVaForImageClassification(__lowerCamelCase  ).eval()

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

 # Check outputs on an image, prepared by MobileNetV2ImageProcessor
 __SCREAMING_SNAKE_CASE							:     Tuple							=						MobileNetVaImageProcessor(
     crop_size={"width": config.image_size, "height": config.image_size}		,						size={"shortest_edge": config.image_size + 32}		,						)
 __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(images=prepare_img()		,						return_tensors="pt"  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model(**__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						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							:     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]		,						__lowerCamelCase		,						atol=1E-4  )
 else:
  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v2_1.4_224":
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						torch.tensor([0.0181, -1.0015, 0.4688]  )
  elif model_name == "mobilenet_v2_1.0_224":
   __SCREAMING_SNAKE_CASE							:     str							=						torch.tensor([0.2445, -1.1993, 0.1905]  )
  elif model_name == "mobilenet_v2_0.75_160":
   __SCREAMING_SNAKE_CASE							:     int							=						torch.tensor([0.2482, 0.4136, 0.6669]  )
  elif model_name == "mobilenet_v2_0.35_96":
   __SCREAMING_SNAKE_CASE							:     Any							=						torch.tensor([0.1451, -0.4624, 0.7192]  )
  else:
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						None

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

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

 if push_to_hub:
  print("Pushing to the hub..."  )
  __SCREAMING_SNAKE_CASE							:     Tuple							=						"google/" + model_name
  image_processor.push_to_hub(__lowerCamelCase  )
  model.push_to_hub(__lowerCamelCase  )


if __name__ == "__main__":
      _lowerCamelCase         =	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."""
      )

      _lowerCamelCase         =	parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )






