from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)






class 		a__						(					snake_case_					):

					def __init__(    self      :       int,_A      :       Distribution,_A      :       Optional[int]=None,_A      :       Union[str, Any]=None,_A      :       Any=0    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    1.0 if scale is None else scale
							SCREAMING_SNAKE_CASE_  :		int				      =    0.0 if loc is None else loc

							super().__init__(_A,[AffineTransform(loc=self.loc,scale=self.scale,event_dim=_A    )]    )

					@property
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							return self.base_dist.mean * self.scale + self.loc

					@property
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							return self.base_dist.variance * self.scale**2






					@property
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							return self.variance.sqrt()







class 		a__						(					nn.Module					):

					def __init__(    self      :       Dict,_A      :       int,_A      :       Dict[str, int],_A      :       Callable[..., Tuple[torch.Tensor]],**_A      :       List[str]    ):







							"""simple docstring"""






							super().__init__(**_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    args_dim
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    nn.ModuleList([nn.Linear(_A,_A    ) for dim in args_dim.values()]    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    domain_map






					def 				__UpperCamelCase   (    self      :       List[str],_A      :       torch.Tensor    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [proj(_A    ) for proj in self.proj]

							return self.domain_map(*_A    )







class 		a__						(					nn.Module					):

					def __init__(    self      :       Dict,_A      :       Optional[int]    ):







							"""simple docstring"""






							super().__init__()
							SCREAMING_SNAKE_CASE_  :		str				      =    function






					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Union[str, Any],*_A      :       Tuple    ):







							"""simple docstring"""






							return self.function(_A,*_A    )







class 		a__						:
					A	       =							42
					A	       =							42
					A	       =							42

					def __init__(    self      :       Optional[int],_A      :       int = 1    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    dim
							SCREAMING_SNAKE_CASE_  :		Dict				      =    {k: dim * self.args_dim[k] for k in self.args_dim}

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       List[str]    ):







							"""simple docstring"""






							if self.dim == 1:
									return self.distribution_class(*_A    )
							else:
									return Independent(self.distribution_class(*_A    ),1    )

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       List[Any],_A      :       Optional[torch.Tensor] = None,_A      :       Optional[torch.Tensor] = None,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    self._base_distribution(_A    )
							if loc is None and scale is None:
									return distr
							else:
									return AffineTransformed(_A,loc=_A,scale=_A,event_dim=self.event_dim    )

					@property
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							return () if self.dim == 1 else (self.dim,)

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							return len(self.event_shape    )

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return 0.0

					def 				__UpperCamelCase   (    self      :       Any,_A      :       int    ):







							"""simple docstring"""






							return ParameterProjection(
							    in_features=_A,args_dim=self.args_dim,domain_map=LambdaLayer(self.domain_map    ),)

					def 				__UpperCamelCase   (    self      :       Union[str, Any],*_A      :       torch.Tensor    ):







							"""simple docstring"""






							raise NotImplementedError()






					@staticmethod
					def 				__UpperCamelCase   (    _A      :       torch.Tensor    ):







							"""simple docstring"""






							return (x + torch.sqrt(torch.square(_A    ) + 4.0    )) / 2.0







class 		a__						(					snake_case_					):
					A	       =							{'df': 1, 'loc': 1, 'scale': 1}
					A	       =							StudentT

					@classmethod
					def 				__UpperCamelCase   (    cls      :       int,_A      :       torch.Tensor,_A      :       torch.Tensor,_A      :       torch.Tensor    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    cls.squareplus(_A    ).clamp_min(torch.finfo(scale.dtype    ).eps    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    2.0 + cls.squareplus(_A    )
							return df.squeeze(-1    ), loc.squeeze(-1    ), scale.squeeze(-1    )







class 		a__						(					snake_case_					):
					A	       =							{'loc': 1, 'scale': 1}
					A	       =							Normal

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Dict,_A      :       torch.Tensor,_A      :       torch.Tensor    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    cls.squareplus(_A    ).clamp_min(torch.finfo(scale.dtype    ).eps    )
							return loc.squeeze(-1    ), scale.squeeze(-1    )







class 		a__						(					snake_case_					):
					A	       =							{'total_count': 1, 'logits': 1}
					A	       =							NegativeBinomial

					@classmethod
					def 				__UpperCamelCase   (    cls      :       str,_A      :       torch.Tensor,_A      :       torch.Tensor    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    cls.squareplus(_A    )
							return total_count.squeeze(-1    ), logits.squeeze(-1    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    distr_args
							if self.dim == 1:
									return self.distribution_class(total_count=_A,logits=_A    )
							else:
									return Independent(self.distribution_class(total_count=_A,logits=_A    ),1    )






					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       List[str],_A      :       Optional[torch.Tensor] = None,_A      :       Optional[torch.Tensor] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    distr_args

							if scale is not None:
									# See scaling property of Gamma.
									logits += scale.log()

							return self._base_distribution((total_count, logits)    )

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

 UpperCamelCase_					:						str =					'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_					:						int =					BitConfig(
     conv_layer=lowerCamelCase  ,  num_labels=1000  ,  idalabel=lowerCamelCase  ,  labelaid=lowerCamelCase  ,  )

 return config





def    __lowercase     (						lowerCamelCase					:     int	):
 if "stem.conv" in name:
  UpperCamelCase_					:						str =					name.replace('stem.conv'  ,  'bit.embedder.convolution'	)
 if "blocks" in name:
  UpperCamelCase_					:						str =					name.replace('blocks'  ,  'layers'	)
 if "head.fc" in name:
  UpperCamelCase_					:						Dict =					name.replace('head.fc'  ,  'classifier.1'	)
 if name.startswith('norm'	):
  UpperCamelCase_					:						List[str] =					'bit.' + name
 if "bit" not in name and "classifier" not in name:
  UpperCamelCase_					:						int =					'bit.encoder.' + name

 return name





def    __lowercase     (						):
 UpperCamelCase_					:						Tuple =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCamelCase_					:						List[Any] =					Image.open(requests.get(lowerCamelCase  ,  stream=lowerCamelCase	).raw	)
 return im





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     List[str]=False	):
 UpperCamelCase_					:						Optional[Any] =					get_config(lowerCamelCase	)

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

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

 # load HuggingFace model
 UpperCamelCase_					:						Dict =					BitForImageClassification(lowerCamelCase	)
 model.eval()
 model.load_state_dict(lowerCamelCase	)

 # create image processor
 UpperCamelCase_					:						int =					create_transform(**resolve_data_config({}  ,  model=lowerCamelCase	)	)
 UpperCamelCase_					:						List[Any] =					transform.transforms

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

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

 UpperCamelCase_					:						Dict =					prepare_img()
 UpperCamelCase_					:						Any =					transform(lowerCamelCase	).unsqueeze(0	)
 UpperCamelCase_					:						Union[str, Any] =					processor(lowerCamelCase  ,  return_tensors='pt'	).pixel_values

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

 # verify logits
 with torch.no_grad():
  UpperCamelCase_					:						Dict =					model(lowerCamelCase	)
  UpperCamelCase_					:						Tuple =					outputs.logits

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

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

 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)



