





"""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()
A			      =	logging.get_logger(__name__)

def      __A   (      a_    :List[Any])		->		List[str]:
				__a			:    Dict      							=	'''huggingface/label-files'''
				__a			:    Dict      							=	'''imagenet-1k-id2label.json'''
				__a			:    List[str]      							=	json.load(open(hf_hub_download(a_ ,							a_ ,							repo_type='''dataset''') ,							'''r'''))
				__a			:    int      							=	{int(a_): v for k, v in idalabel.items()}
				__a			:    Optional[int]      							=	{v: k for k, v in idalabel.items()}

				__a			:    Any      							=	'''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			:    int      							=	BitConfig(
				    conv_layer=a_ ,							num_labels=10_00 ,							idalabel=a_ ,							labelaid=a_ ,							)

				return config

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

				return name

def      __A   (      )		->		Optional[Any]:
				__a			:    int      							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a			:    Any      							=	Image.open(requests.get(a_ ,							stream=a_).raw)
				return im

@torch.no_grad()
def      __A   (      a_    :Optional[int] ,							a_    :Union[str, Any] ,							a_    :Union[str, Any]=False)		->		Union[str, Any]:
				__a			:    List[str]      							=	get_config(a_)

				# load original model from timm
				__a			:    int      							=	create_model(a_ ,							pretrained=a_)
				timm_model.eval()

				# load state_dict of original model
				__a			:    List[Any]      							=	timm_model.state_dict()
				for key in state_dict.copy().keys():
								__a			:    Tuple      							=	state_dict.pop(a_)
								__a			:    Optional[int]      							=	val.squeeze() if '''head''' in key else val

				# load HuggingFace model
				__a			:    List[str]      							=	BitForImageClassification(a_)
				model.eval()
				model.load_state_dict(a_)

				# create image processor
				__a			:    Tuple      							=	create_transform(**resolve_data_config({} ,							model=a_))
				__a			:    Optional[Any]      							=	transform.transforms

				__a			:    Union[str, Any]      							=	{
				    '''bilinear''': PILImageResampling.BILINEAR,
				    '''bicubic''': PILImageResampling.BICUBIC,
				    '''nearest''': PILImageResampling.NEAREST,
				}

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

				__a			:    List[str]      							=	prepare_img()
				__a			:    Optional[Any]      							=	transform(a_).unsqueeze(0)
				__a			:    Union[str, Any]      							=	processor(a_ ,							return_tensors='''pt''').pixel_values

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

				# verify logits
				with torch.no_grad():
								__a			:    Tuple      							=	model(a_)
								__a			:    Any      							=	outputs.logits

				print('''Logits:''' ,							logits[0, :3])
				print('''Predicted class:''' ,							model.config.idalabel[logits.argmax(-1).item()])
				__a			:    Union[str, Any]      							=	timm_model(a_)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(a_ ,							outputs.logits ,							atol=1e-3)
				print('''Looks ok!''')

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

				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__":
		A			      =	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.''',
		)

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



'''simple docstring'''



# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A__						:   Any				=    re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")





@total_ordering
@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str
    lowerCamelCase    :   Optional[str]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						_str_to_version_tuple(self.version_str      )







    def __repr__(				self      )							->	Any:
         return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'







    @property
    def  lowercase_							(				self      )							->	int:
         return self.major, self.minor, self.patch







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return Version(SCREAMING_SNAKE_CASE_      )
         elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return other
         raise TypeError(f'{other} (type {type(SCREAMING_SNAKE_CASE_      )}) cannot be compared to version.'      )







    def __eq__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         try:
              __lowerCamelCase  :      Union[str, Any]														=						self._validate_operand(SCREAMING_SNAKE_CASE_      )
         except (TypeError, ValueError):
              return False
         else:
              return self.tuple == other.tuple







    def __lt__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         __lowerCamelCase  :      List[Any]														=						self._validate_operand(SCREAMING_SNAKE_CASE_      )
         return self.tuple < other.tuple







    def __hash__(				self      )							->	List[str]:
         return hash(_version_tuple_to_str(self.tuple      )      )







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase  :      str														=						{f.name for f in dataclasses.fields(cls      )}
         return cls(**{k: v for k, v in dic.items() if k in field_names}      )







    def  lowercase_							(				self      )							->	str:
         return self.version_str







def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						str:
     __lowerCamelCase  :      str														=						_VERSION_REG.match(UpperCAmelCase_				)
     if not res:
          raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'				)
     return tuple(int(UpperCAmelCase_				) for v in [res.group('major'				), res.group('minor'				), res.group('patch'				)]				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[str]				)	->						Dict:
     return ".".join(str(UpperCAmelCase_				) for v in version_tuple				)






