


"""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()
snake_case__						:   Any			 = logging.get_logger(__name__)


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

      lowerCAmelCase    :		List[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"
      lowerCAmelCase    :		List[Any]				=      BitConfig(
          conv_layer=_snake_case       ,      num_labels=1000       ,      idalabel=_snake_case       ,      labelaid=_snake_case       ,      )

      return config


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

      return name


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


@torch.no_grad()
def    _snake_case  (  _snake_case :	List[Any]       ,      _snake_case :	str       ,      _snake_case :	List[str]=False	):
      lowerCAmelCase    :		Optional[Any]				=      get_config(_snake_case	)

      # load original model from timm
      lowerCAmelCase    :		List[Any]				=      create_model(_snake_case       ,      pretrained=_snake_case	)
      timm_model.eval()

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

      # load HuggingFace model
      lowerCAmelCase    :		Optional[int]				=      BitForImageClassification(_snake_case	)
      model.eval()
      model.load_state_dict(_snake_case	)

      # create image processor
      lowerCAmelCase    :		Union[str, Any]				=      create_transform(**resolve_data_config({}       ,      model=_snake_case	)	)
      lowerCAmelCase    :		int				=      transform.transforms

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

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

      lowerCAmelCase    :		List[str]				=      prepare_img()
      lowerCAmelCase    :		Dict				=      transform(_snake_case	).unsqueeze(0	)
      lowerCAmelCase    :		Tuple				=      processor(_snake_case       ,      return_tensors='''pt'''	).pixel_values

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

      # verify logits
      with torch.no_grad():
            lowerCAmelCase    :		Dict				=      model(_snake_case	)
            lowerCAmelCase    :		Optional[Any]				=      outputs.logits

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

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

      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__":
 snake_case__						:   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.''',
 )

 snake_case__						:   Any			 = parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array


def    _snake_case  (  _snake_case :	list[list[float]]	):
      lowerCAmelCase    :		str				=      Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(_snake_case	) == 2 and len(matrix[0]	) == 2 and len(matrix[1]	) == 2:
            # Calculate the determinant of the matrix
            lowerCAmelCase    :		int				=      float(
                d(matrix[0][0]	) * d(matrix[1][1]	) - d(matrix[1][0]	) * d(matrix[0][1]	)	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creates a copy of the matrix with swapped positions of the elements
            lowerCAmelCase    :		Optional[int]				=      [[0.0, 0.0], [0.0, 0.0]]
            lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      matrix[1][1], matrix[0][0]
            lowerCAmelCase,					lowerCAmelCase    :		Union[str, Any]				=      -matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(_snake_case	)	) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(_snake_case	) == 3
          and len(matrix[0]	) == 3
          and len(matrix[1]	) == 3
          and len(matrix[2]	) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            lowerCAmelCase    :		int				=      float(
                (
                    (d(matrix[0][0]	) * d(matrix[1][1]	) * d(matrix[2][2]	))
                    + (d(matrix[0][1]	) * d(matrix[1][2]	) * d(matrix[2][0]	))
                    + (d(matrix[0][2]	) * d(matrix[1][0]	) * d(matrix[2][1]	))
                )
                - (
                    (d(matrix[0][2]	) * d(matrix[1][1]	) * d(matrix[2][0]	))
                    + (d(matrix[0][1]	) * d(matrix[1][0]	) * d(matrix[2][2]	))
                    + (d(matrix[0][0]	) * d(matrix[1][2]	) * d(matrix[2][1]	))
                )	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creating cofactor matrix
            lowerCAmelCase    :		Dict				=      [
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
            ]
            lowerCAmelCase    :		List[str]				=      (d(matrix[1][1]	) * d(matrix[2][2]	)) - (
                d(matrix[1][2]	) * d(matrix[2][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[1][0]	) * d(matrix[2][2]	)) - (d(matrix[1][2]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		str				=      (d(matrix[1][0]	) * d(matrix[2][1]	)) - (
                d(matrix[1][1]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Any				=      -(
                (d(matrix[0][1]	) * d(matrix[2][2]	)) - (d(matrix[0][2]	) * d(matrix[2][1]	))
            )
            lowerCAmelCase    :		Any				=      (d(matrix[0][0]	) * d(matrix[2][2]	)) - (
                d(matrix[0][2]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Optional[int]				=      -(
                (d(matrix[0][0]	) * d(matrix[2][1]	)) - (d(matrix[0][1]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		Optional[int]				=      (d(matrix[0][1]	) * d(matrix[1][2]	)) - (
                d(matrix[0][2]	) * d(matrix[1][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[0][0]	) * d(matrix[1][2]	)) - (d(matrix[0][2]	) * d(matrix[1][0]	))
            )
            lowerCAmelCase    :		List[Any]				=      (d(matrix[0][0]	) * d(matrix[1][1]	)) - (
                d(matrix[0][1]	) * d(matrix[1][0]	)
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            lowerCAmelCase    :		str				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        lowerCAmelCase    :		Optional[Any]				=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            lowerCAmelCase    :		Tuple				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        inverse_matrix[i][j] /= d(_snake_case	)

        # Calculate the inverse of the matrix
            return [[float(d(_snake_case	)	) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''	)


