
"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Union[str, Any]:







       """simple docstring"""
       UpperCamelCase					 =		"huggingface/label-files"
       UpperCamelCase					 =		"imagenet-1k-id2label.json"
       UpperCamelCase					 =		json.load(open(hf_hub_download(UpperCAmelCase_		,      UpperCAmelCase_		,      repo_type="dataset"				)		,      "r"				)				)
       UpperCamelCase					 =		{int(UpperCAmelCase_				): v for k, v in idalabel.items()}
       UpperCamelCase					 =		{v: k for k, v in idalabel.items()}

       UpperCamelCase					 =		"std_conv" if "bit" in model_name else False

       # note that when using BiT as backbone for ViT-hybrid checkpoints,
       # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
       # config.conv_layer = "std_conv_same"
       UpperCamelCase					 =		BitConfig(
           conv_layer=UpperCAmelCase_		,      num_labels=10_00		,      idalabel=UpperCAmelCase_		,      labelaid=UpperCAmelCase_		,      )

       return config



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       if "stem.conv" in name:
              UpperCamelCase					 =		name.replace("stem.conv"		,      "bit.embedder.convolution"				)
       if "blocks" in name:
              UpperCamelCase					 =		name.replace("blocks"		,      "layers"				)
       if "head.fc" in name:
              UpperCamelCase					 =		name.replace("head.fc"		,      "classifier.1"				)
       if name.startswith("norm"				):
              UpperCamelCase					 =		"bit." + name
       if "bit" not in name and "classifier" not in name:
              UpperCamelCase					 =		"bit.encoder." + name

       return name



def 	lowerCamelCase__			(							)->							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 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_=False				)->							int:







       """simple docstring"""
       UpperCamelCase					 =		get_config(UpperCAmelCase_				)

       # load original model from timm
       UpperCamelCase					 =		create_model(UpperCAmelCase_		,      pretrained=UpperCAmelCase_				)
       timm_model.eval()

       # load state_dict of original model
       UpperCamelCase					 =		timm_model.state_dict()
       for key in state_dict.copy().keys():
              UpperCamelCase					 =		state_dict.pop(UpperCAmelCase_				)
              UpperCamelCase					 =		val.squeeze() if "head" in key else val

       # load HuggingFace model
       UpperCamelCase					 =		BitForImageClassification(UpperCAmelCase_				)
       model.eval()
       model.load_state_dict(UpperCAmelCase_				)

       # create image processor
       UpperCamelCase					 =		create_transform(**resolve_data_config({}		,      model=UpperCAmelCase_				)				)
       UpperCamelCase					 =		transform.transforms

       UpperCamelCase					 =		{
           "bilinear": PILImageResampling.BILINEAR,
           "bicubic": PILImageResampling.BICUBIC,
           "nearest": PILImageResampling.NEAREST,
       }

       UpperCamelCase					 =		BitImageProcessor(
           do_resize=UpperCAmelCase_		,      size={"shortest_edge": timm_transforms[0].size}		,      resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,      do_center_crop=UpperCAmelCase_		,      crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}		,      do_normalize=UpperCAmelCase_		,      image_mean=timm_transforms[-1].mean.tolist()		,      image_std=timm_transforms[-1].std.tolist()		,      )

       UpperCamelCase					 =		prepare_img()
       UpperCamelCase					 =		transform(UpperCAmelCase_				).unsqueeze(0				)
       UpperCamelCase					 =		processor(UpperCAmelCase_		,      return_tensors="pt"				).pixel_values

       # verify pixel values
       assert torch.allclose(UpperCAmelCase_		,      UpperCAmelCase_				)

       # verify logits
       with torch.no_grad():
              UpperCamelCase					 =		model(UpperCAmelCase_				)
              UpperCamelCase					 =		outputs.logits

       print("Logits:"		,      logits[0, :3]				)
       print("Predicted class:"		,      model.config.idalabel[logits.argmax(-1				).item()]				)
       UpperCamelCase					 =		timm_model(UpperCAmelCase_				)
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(UpperCAmelCase_		,      outputs.logits		,      atol=1E-3				)
       print("Looks ok!"				)

       if pytorch_dump_folder_path is not None:
              Path(UpperCAmelCase_				).mkdir(exist_ok=UpperCAmelCase_				)
              print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"				)
              model.save_pretrained(UpperCAmelCase_				)
              processor.save_pretrained(UpperCAmelCase_				)

       if push_to_hub:
              print(F"Pushing model {model_name} and processor to the hub"				)
              model.push_to_hub(F"ybelkada/{model_name}"				)
              processor.push_to_hub(F"ybelkada/{model_name}"				)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE      =     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""resnetv2_50x1_bitm""",
          type=str,
          help="""Name of the BiT timm model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""",
          action="""store_true""",
          help="""Whether to push the model to the hub.""",
      )

      SCREAMING_SNAKE_CASE      =     parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  :
       UpperCamelCase_		:					str
       UpperCamelCase_		:					str   =			None






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Union[str, Any]:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							str			)->  Any:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  List[str]:







              """simple docstring"""
              if not self.is_available():
                     raise RuntimeError(
                         f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."			)






       @classmethod
       def 	_SCREAMING_SNAKE_CASE       (  cls :							Union[str, Any]			)->  int:







              """simple docstring"""
              return f"`pip install {cls.pip_package or cls.name}`"





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[str]   =			'''optuna'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Union[str, Any]:







              """simple docstring"""
              return is_optuna_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Optional[Any]			)->  Union[str, Any]:







              """simple docstring"""
              return run_hp_search_optuna(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Union[str, Any]			)->  Union[str, Any]:







              """simple docstring"""
              return default_hp_space_optuna(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[int]   =			'''ray'''
       UpperCamelCase_		:					Tuple   =			'''\'ray[tune]\''''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Any:







              """simple docstring"""
              return is_ray_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							int			)->  int:







              """simple docstring"""
              return run_hp_search_ray(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							str			)->  Union[str, Any]:







              """simple docstring"""
              return default_hp_space_ray(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[str, Any]   =			'''sigopt'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  List[str]:







              """simple docstring"""
              return is_sigopt_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Tuple			)->  Tuple:







              """simple docstring"""
              return run_hp_search_sigopt(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Tuple			)->  Optional[Any]:







              """simple docstring"""
              return default_hp_space_sigopt(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Any   =			'''wandb'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  List[Any]:







              """simple docstring"""
              return is_wandb_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							List[str]			)->  str:







              """simple docstring"""
              return run_hp_search_wandb(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							List[Any]			)->  List[Any]:







              """simple docstring"""
              return default_hp_space_wandb(UpperCAmelCase_			)





SCREAMING_SNAKE_CASE      =     {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}



def 	lowerCamelCase__			(							)->							str:







       """simple docstring"""
       UpperCamelCase					 =		[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
       if len(UpperCAmelCase_				) > 0:
              UpperCamelCase					 =		available_backends[0].name
              if len(UpperCAmelCase_				) > 1:
                     logger.info(
                         F"{len(UpperCAmelCase_				)} hyperparameter search backends available. Using {name} as the default."				)
              return name
       raise RuntimeError(
           "No hyperparameter search backend available.\n"
           + "\n".join(
               F" - To install {backend.name} run {backend.pip_install()}"
               for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()				)				)





