

'''simple docstring'''





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
							import transformers






def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  Optional[Any]:
						random.seed(__magic_name__						)
						np.random.seed(__magic_name__						)
						torch.manual_seed(__magic_name__						)
						torch.cuda.manual_seed_all(__magic_name__						)
						# ^^ safe to call this function even if cuda is not available







class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Optional[int]		,					UpperCAmelCase__     :		Iterable[torch.nn.Parameter]		,					UpperCAmelCase__     :		float = 0.99_99		,					UpperCAmelCase__     :		float = 0.0		,					UpperCAmelCase__     :		int = 0		,					UpperCAmelCase__     :		bool = False		,					UpperCAmelCase__     :		Union[float, int] = 1.0		,					UpperCAmelCase__     :		Union[float, int] = 2 / 3		,					UpperCAmelCase__     :		Optional[Any] = None		,					UpperCAmelCase__     :		Dict[str, Any] = None		,					**UpperCAmelCase__     :		Dict		,					):
										'''simple docstring'''

										if isinstance(UpperCAmelCase__		,					torch.nn.Module   ):
																lowercase :							Tuple    		=(
																    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
																    '''Please pass the parameters of the module instead.'''
																)
																deprecate(
																    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''		,					'''1.0.0'''		,					UpperCAmelCase__		,					standard_warn=UpperCAmelCase__		,					)
																lowercase :							Optional[int]    		=parameters.parameters()

																# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																lowercase :							str    		=True

										if kwargs.get('''max_value'''		,					UpperCAmelCase__   ) is not None:
																lowercase :							List[Any]    		='''The `max_value` argument is deprecated. Please use `decay` instead.'''
																deprecate('''max_value'''		,					'''1.0.0'''		,					UpperCAmelCase__		,					standard_warn=UpperCAmelCase__   )
																lowercase :							List[Any]    		=kwargs['''max_value''']

										if kwargs.get('''min_value'''		,					UpperCAmelCase__   ) is not None:
																lowercase :							int    		='''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
																deprecate('''min_value'''		,					'''1.0.0'''		,					UpperCAmelCase__		,					standard_warn=UpperCAmelCase__   )
																lowercase :							Optional[int]    		=kwargs['''min_value''']

										lowercase :							Any    		=list(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=[p.clone().detach() for p in parameters]

										if kwargs.get('''device'''		,					UpperCAmelCase__   ) is not None:
																lowercase :							Dict    		='''The `device` argument is deprecated. Please use `to` instead.'''
																deprecate('''device'''		,					'''1.0.0'''		,					UpperCAmelCase__		,					standard_warn=UpperCAmelCase__   )
																self.to(device=kwargs['''device''']   )

										lowercase :							Dict    		=None

										lowercase :							List[str]    		=decay
										lowercase :							List[str]    		=min_decay
										lowercase :							Union[str, Any]    		=update_after_step
										lowercase :							Optional[int]    		=use_ema_warmup
										lowercase :							Any    		=inv_gamma
										lowercase :							Tuple    		=power
										lowercase :							int    		=0
										lowercase :							str    		=None  # set in `step()`

										lowercase :							Union[str, Any]    		=model_cls
										lowercase :							Dict    		=model_config







				@classmethod
				def  lowerCamelCase_	(       cls     :		Optional[Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		List[Any]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Dict    		=model_cls.load_config(UpperCAmelCase__		,					return_unused_kwargs=UpperCAmelCase__   )
										lowercase :							int    		=model_cls.from_pretrained(UpperCAmelCase__   )

										lowercase :							int    		=cls(model.parameters()		,					model_cls=UpperCAmelCase__		,					model_config=model.config   )

										ema_model.load_state_dict(UpperCAmelCase__   )
										return ema_model







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		Optional[Any]   ):
										'''simple docstring'''
										if self.model_cls is None:
																raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''   )

										if self.model_config is None:
																raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''   )

										lowercase :							List[str]    		=self.model_cls.from_config(self.model_config   )
										lowercase :							Tuple    		=self.state_dict()
										state_dict.pop('''shadow_params'''		,					UpperCAmelCase__   )

										model.register_to_config(**UpperCAmelCase__   )
										self.copy_to(model.parameters()   )
										model.save_pretrained(UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[str]    		=max(0		,					optimization_step - self.update_after_step - 1   )

										if step <= 0:
																return 0.0

										if self.use_ema_warmup:
																lowercase :							List[str]    		=1 - (1 + step / self.inv_gamma) ** -self.power
										else:
																lowercase :							List[str]    		=(1 + step) / (10 + step)

										lowercase :							Union[str, Any]    		=min(UpperCAmelCase__		,					self.decay   )
										# make sure decay is not smaller than min_decay
										lowercase :							Any    		=max(UpperCAmelCase__		,					self.min_decay   )
										return cur_decay_value







				@torch.no_grad()
				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		Iterable[torch.nn.Parameter]   ):
										'''simple docstring'''
										if isinstance(UpperCAmelCase__		,					torch.nn.Module   ):
																lowercase :							List[str]    		=(
																    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
																    '''Please pass the parameters of the module instead.'''
																)
																deprecate(
																    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''		,					'''1.0.0'''		,					UpperCAmelCase__		,					standard_warn=UpperCAmelCase__		,					)
																lowercase :							Union[str, Any]    		=parameters.parameters()

										lowercase :							str    		=list(UpperCAmelCase__   )

										self.optimization_step += 1

										# Compute the decay factor for the exponential moving average.
										lowercase :							str    		=self.get_decay(self.optimization_step   )
										lowercase :							Union[str, Any]    		=decay
										lowercase :							Dict    		=1 - decay

										lowercase :							Union[str, Any]    		=contextlib.nullcontext
										if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																import deepspeed

										for s_param, param in zip(self.shadow_params		,					UpperCAmelCase__   ):
																if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																						lowercase :							Tuple    		=deepspeed.zero.GatheredParameters(UpperCAmelCase__		,					modifier_rank=UpperCAmelCase__   )

																with context_manager():
																						if param.requires_grad:
																												s_param.sub_(one_minus_decay * (s_param - param)   )
																						else:
																												s_param.copy_(UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		Iterable[torch.nn.Parameter]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=list(UpperCAmelCase__   )
										for s_param, param in zip(self.shadow_params		,					UpperCAmelCase__   ):
																param.data.copy_(s_param.to(param.device   ).data   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Tuple=None		,					UpperCAmelCase__     :		List[str]=None   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=[
										    p.to(device=UpperCAmelCase__		,					dtype=UpperCAmelCase__   ) if p.is_floating_point() else p.to(device=UpperCAmelCase__   )
										    for p in self.shadow_params
										]







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										return {
										    "decay": self.decay,
										    "min_decay": self.min_decay,
										    "optimization_step": self.optimization_step,
										    "update_after_step": self.update_after_step,
										    "use_ema_warmup": self.use_ema_warmup,
										    "inv_gamma": self.inv_gamma,
										    "power": self.power,
										    "shadow_params": self.shadow_params,
										}







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		Iterable[torch.nn.Parameter]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=[param.detach().cpu().clone() for param in parameters]







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		Iterable[torch.nn.Parameter]   ):
										'''simple docstring'''
										if self.temp_stored_params is None:
																raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''   )
										for c_param, param in zip(self.temp_stored_params		,					UpperCAmelCase__   ):
																param.data.copy_(c_param.data   )

										# Better memory-wise.
										lowercase :							str    		=None







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		dict   ):
										'''simple docstring'''
										lowercase :							Tuple    		=copy.deepcopy(UpperCAmelCase__   )

										lowercase :							List[str]    		=state_dict.get('''decay'''		,					self.decay   )
										if self.decay < 0.0 or self.decay > 1.0:
																raise ValueError('''Decay must be between 0 and 1'''   )

										lowercase :							List[Any]    		=state_dict.get('''min_decay'''		,					self.min_decay   )
										if not isinstance(self.min_decay		,					UpperCAmelCase__   ):
																raise ValueError('''Invalid min_decay'''   )

										lowercase :							Optional[Any]    		=state_dict.get('''optimization_step'''		,					self.optimization_step   )
										if not isinstance(self.optimization_step		,					UpperCAmelCase__   ):
																raise ValueError('''Invalid optimization_step'''   )

										lowercase :							int    		=state_dict.get('''update_after_step'''		,					self.update_after_step   )
										if not isinstance(self.update_after_step		,					UpperCAmelCase__   ):
																raise ValueError('''Invalid update_after_step'''   )

										lowercase :							Dict    		=state_dict.get('''use_ema_warmup'''		,					self.use_ema_warmup   )
										if not isinstance(self.use_ema_warmup		,					UpperCAmelCase__   ):
																raise ValueError('''Invalid use_ema_warmup'''   )

										lowercase :							Optional[Any]    		=state_dict.get('''inv_gamma'''		,					self.inv_gamma   )
										if not isinstance(self.inv_gamma		,					(float, int)   ):
																raise ValueError('''Invalid inv_gamma'''   )

										lowercase :							List[str]    		=state_dict.get('''power'''		,					self.power   )
										if not isinstance(self.power		,					(float, int)   ):
																raise ValueError('''Invalid power'''   )

										lowercase :							List[str]    		=state_dict.get('''shadow_params'''		,					UpperCAmelCase__   )
										if shadow_params is not None:
																lowercase :							Optional[int]    		=shadow_params
																if not isinstance(self.shadow_params		,					UpperCAmelCase__   ):
																						raise ValueError('''shadow_params must be a list'''   )
																if not all(isinstance(UpperCAmelCase__		,					torch.Tensor   ) for p in self.shadow_params   ):
																						raise ValueError('''shadow_params must all be Tensors'''   )










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()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	int:
    A__					=       '''huggingface/label-files'''
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						): v for k, v in idalabel.items()}
    A__					=       {v: k for k, v in idalabel.items()}

    A__					=       '''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"
    A__					=       BitConfig(
        conv_layer=UpperCamelCase_			,							num_labels=1_0_0_0			,							idalabel=UpperCamelCase_			,							labelaid=UpperCamelCase_			,							)

    return config



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]						)->	Optional[int]:
    if "stem.conv" in name:
        A__					=       name.replace('''stem.conv'''			,							'''bit.embedder.convolution'''						)
    if "blocks" in name:
        A__					=       name.replace('''blocks'''			,							'''layers'''						)
    if "head.fc" in name:
        A__					=       name.replace('''head.fc'''			,							'''classifier.1'''						)
    if name.startswith('''norm'''						):
        A__					=       '''bit.''' + name
    if "bit" not in name and "classifier" not in name:
        A__					=       '''bit.encoder.''' + name

    return name



def 		lowerCAmelCase__     (       )->	str:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Tuple			,							UpperCamelCase_					:							List[Any]=False						)->	Optional[int]:
    A__					=       get_config(UpperCamelCase_						)

    # load original model from timm
    A__					=       create_model(UpperCamelCase_			,							pretrained=UpperCamelCase_						)
    timm_model.eval()

    # load state_dict of original model
    A__					=       timm_model.state_dict()
    for key in state_dict.copy().keys():
        A__					=       state_dict.pop(UpperCamelCase_						)
        A__					=       val.squeeze() if '''head''' in key else val

    # load HuggingFace model
    A__					=       BitForImageClassification(UpperCamelCase_						)
    model.eval()
    model.load_state_dict(UpperCamelCase_						)

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

    A__					=       {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    A__					=       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()			,							)

    A__					=       prepare_img()
    A__					=       transform(UpperCamelCase_						).unsqueeze(0						)
    A__					=       processor(UpperCamelCase_			,							return_tensors='''pt'''						).pixel_values

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

    # verify logits
    with torch.no_grad():
        A__					=       model(UpperCamelCase_						)
        A__					=       outputs.logits

    print('''Logits:'''			,							logits[0, :3]						)
    print('''Predicted class:'''			,							model.config.idalabel[logits.argmax(-1						).item()]						)
    A__					=       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__":
   _lowercase    	=		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.",
   )

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