




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()
lowerCamelCase       :	List[Any]									=     logging.get_logger(__name__)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					'huggingface/label-files'
      lowerCamelCase_	  =					'imagenet-1k-id2label.json'
      lowerCamelCase_	  =					json.load(open(hf_hub_download(lowercase			,							lowercase			,							repo_type='dataset'  )			,							'r'  )  )
      lowerCamelCase_	  =					{int(lowercase  ): 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=lowercase			,							num_labels=10_00			,							idalabel=lowercase			,							labelaid=lowercase			,							)

      return config

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict  ):


      '''simple docstring'''



      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    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase_	  =					Image.open(requests.get(lowercase			,							stream=lowercase  ).raw  )
      return im

@torch.no_grad()
def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]			,							lowercase			: Dict			,							lowercase			: Dict=False  ):


      '''simple docstring'''



      lowerCamelCase_	  =					get_config(lowercase  )

      # load original model from timm
      lowerCamelCase_	  =					create_model(lowercase			,							pretrained=lowercase  )
      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(lowercase  )
            lowerCamelCase_	  =					val.squeeze() if 'head' in key else val

      # load HuggingFace model
      lowerCamelCase_	  =					BitForImageClassification(lowercase  )
      model.eval()
      model.load_state_dict(lowercase  )

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

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

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

      lowerCamelCase_	  =					prepare_img()
      lowerCamelCase_	  =					transform(lowercase  ).unsqueeze(0  )
      lowerCamelCase_	  =					processor(lowercase			,							return_tensors='pt'  ).pixel_values

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

      # verify logits
      with torch.no_grad():
            lowerCamelCase_	  =					model(lowercase  )
            lowerCamelCase_	  =					outputs.logits

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

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

      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__":
 lowerCamelCase       :	List[Any]									=     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.",
 )

 lowerCamelCase       :	Union[str, Any]									=     parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      return EnvironmentCommand()







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @staticmethod
 def        a__				(       A_      :						ArgumentParser						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					parser.add_parser('env'						)
       download_parser.set_defaults(func=A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					huggingface_hub.__version__

       lowerCamelCase_	  =					'not installed'
       lowerCamelCase_	  =					'NA'
       if is_torch_available():
             import torch

             lowerCamelCase_	  =					torch.__version__
             lowerCamelCase_	  =					torch.cuda.is_available()

       lowerCamelCase_	  =					'not installed'
       if is_transformers_available():
             import transformers

             lowerCamelCase_	  =					transformers.__version__

       lowerCamelCase_	  =					'not installed'
       if is_accelerate_available():
             import accelerate

             lowerCamelCase_	  =					accelerate.__version__

       lowerCamelCase_	  =					'not installed'
       if is_xformers_available():
             import xformers

             lowerCamelCase_	  =					xformers.__version__

       lowerCamelCase_	  =					{
           '`diffusers` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'PyTorch version (GPU?)': f"""{pt_version} ({pt_cuda_available})""",
           'Huggingface_hub version': hub_version,
           'Transformers version': transformers_version,
           'Accelerate version': accelerate_version,
           'xFormers version': xformers_version,
           'Using GPU in script?': '<fill in>',
           'Using distributed or parallel set-up in script?': '<fill in>',
       }

       print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'						)
       print(self.format_dict(A_						)						)

       return info





 @staticmethod
 def        a__				(       A_      :						Dict						)     ->			Any:

       """simple docstring"""






       return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"



