





"""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()
__UpperCamelCase     :						str						       =  logging.get_logger(__name__)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple			):
 lowerCAmelCase    				= 'huggingface/label-files'
 lowerCAmelCase    				= 'imagenet-1k-id2label.json'
 lowerCAmelCase    				= json.load(open(hf_hub_download(_UpperCAmelCase  ,     _UpperCAmelCase  ,     repo_type='dataset'			)  ,     'r'			)			)
 lowerCAmelCase    				= {int(_UpperCAmelCase			): v for k, v in idalabel.items()}
 lowerCAmelCase    				= {v: k for k, v in idalabel.items()}

 lowerCAmelCase    				= '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"
 lowerCAmelCase    				= BitConfig(
     conv_layer=_UpperCAmelCase  ,     num_labels=1000  ,     idalabel=_UpperCAmelCase  ,     labelaid=_UpperCAmelCase  ,     )

 return config

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple			):
 if "stem.conv" in name:
  lowerCAmelCase    				= name.replace('stem.conv'  ,     'bit.embedder.convolution'			)
 if "blocks" in name:
  lowerCAmelCase    				= name.replace('blocks'  ,     'layers'			)
 if "head.fc" in name:
  lowerCAmelCase    				= name.replace('head.fc'  ,     'classifier.1'			)
 if name.startswith('norm'			):
  lowerCAmelCase    				= 'bit.' + name
 if "bit" not in name and "classifier" not in name:
  lowerCAmelCase    				= 'bit.encoder.' + name

 return name

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= 'http://images.cocodataset.org/val2017/000000039769.jpg'
 lowerCAmelCase    				= Image.open(requests.get(_UpperCAmelCase  ,     stream=_UpperCAmelCase			).raw			)
 return im

@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Dict  ,     _UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    str=False			):
 lowerCAmelCase    				= get_config(_UpperCAmelCase			)

 # load original model from timm
 lowerCAmelCase    				= create_model(_UpperCAmelCase  ,     pretrained=_UpperCAmelCase			)
 timm_model.eval()

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

 # load HuggingFace model
 lowerCAmelCase    				= BitForImageClassification(_UpperCAmelCase			)
 model.eval()
 model.load_state_dict(_UpperCAmelCase			)

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

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

 lowerCAmelCase    				= 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()  ,     )

 lowerCAmelCase    				= prepare_img()
 lowerCAmelCase    				= transform(_UpperCAmelCase			).unsqueeze(0			)
 lowerCAmelCase    				= processor(_UpperCAmelCase  ,     return_tensors='pt'			).pixel_values

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

 # verify logits
 with torch.no_grad():
  lowerCAmelCase    				= model(_UpperCAmelCase			)
  lowerCAmelCase    				= outputs.logits

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

       __UpperCamelCase     :						Optional[int]						       =  parser.parse_args()
       convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class       a							(   a__  ):
 snake_case__             =       42







class       a							(   a__   , a__  ):



 @register_to_config
 def __init__(     self				,       _snake_case = 3				,       _snake_case = 3				,       _snake_case = ("DownEncoderBlock2D",)				,       _snake_case = ("UpDecoderBlock2D",)				,       _snake_case = (64,)				,       _snake_case = 1				,       _snake_case = "silu"				,       _snake_case = 3				,       _snake_case = 32				,       _snake_case = 2_56				,       _snake_case = 32				,       _snake_case = None				,       _snake_case = 0.18_215				,       _snake_case = "group"				,       ):







  """simple docstring"""






  super().__init__()

  # pass init params to Encoder
  lowerCAmelCase    				= Encoder(
      in_channels=_snake_case				,       out_channels=_snake_case				,       down_block_types=_snake_case				,       block_out_channels=_snake_case				,       layers_per_block=_snake_case				,       act_fn=_snake_case				,       norm_num_groups=_snake_case				,       double_z=_snake_case				,       )

  lowerCAmelCase    				= vq_embed_dim if vq_embed_dim is not None else latent_channels

  lowerCAmelCase    				= nn.Convad(_snake_case				,       _snake_case				,       1     )
  lowerCAmelCase    				= VectorQuantizer(_snake_case				,       _snake_case				,       beta=0.25				,       remap=_snake_case				,       sane_index_shape=_snake_case     )
  lowerCAmelCase    				= nn.Convad(_snake_case				,       _snake_case				,       1     )

  # pass init params to Decoder
  lowerCAmelCase    				= Decoder(
      in_channels=_snake_case				,       out_channels=_snake_case				,       up_block_types=_snake_case				,       block_out_channels=_snake_case				,       layers_per_block=_snake_case				,       act_fn=_snake_case				,       norm_num_groups=_snake_case				,       norm_type=_snake_case				,       )



 @apply_forward_hook
 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = True     ):







  """simple docstring"""






  lowerCAmelCase    				= self.encoder(_snake_case     )
  lowerCAmelCase    				= self.quant_conv(_snake_case     )

  if not return_dict:
   return (h,)

  return VQEncoderOutput(latents=_snake_case     )



 @apply_forward_hook
 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = False				,       _snake_case = True     ):







  """simple docstring"""






  if not force_not_quantize:
   lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self.quantize(_snake_case     )
  else:
   lowerCAmelCase    				= h
  lowerCAmelCase    				= self.post_quant_conv(_snake_case     )
  lowerCAmelCase    				= self.decoder(_snake_case				,       quant if self.config.norm_type == 'spatial' else None     )

  if not return_dict:
   return (dec,)

  return DecoderOutput(sample=_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = True     ):







  """simple docstring"""






  lowerCAmelCase    				= sample
  lowerCAmelCase    				= self.encode(_snake_case     ).latents
  lowerCAmelCase    				= self.decode(_snake_case     ).sample

  if not return_dict:
   return (dec,)

  return DecoderOutput(sample=_snake_case     )





