






'''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      					=logging.get_logger(__name__)



def 				snake_case       (  a_				:			Optional[int]						)							->					str:





      """simple docstring"""

      UpperCamelCase_					: List[str]            =				"""huggingface/label-files"""
      UpperCamelCase_					: Optional[int]            =				"""imagenet-1k-id2label.json"""
      UpperCamelCase_					: List[Any]            =				json.load(open(hf_hub_download(a_     , a_     , repo_type="""dataset"""						)     , """r"""						)						)
      UpperCamelCase_					: Tuple            =				{int(a_						): v for k, v in idalabel.items()}
      UpperCamelCase_					: List[Any]            =				{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_					: Optional[int]            =				BitConfig(
          conv_layer=a_     , num_labels=1_000     , idalabel=a_     , labelaid=a_     , )

      return config



def 				snake_case       (  a_				:			Optional[int]						)							->					Optional[Any]:





      """simple docstring"""

      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_					: Any            =				name.replace("""head.fc"""     , """classifier.1"""						)
      if name.startswith("""norm"""						):
            UpperCamelCase_					: Any            =				"""bit.""" + name
      if "bit" not in name and "classifier" not in name:
            UpperCamelCase_					: List[str]            =				"""bit.encoder.""" + name

      return name



def 				snake_case       (  )							->					str:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase_					: str            =				Image.open(requests.get(a_     , stream=a_						).raw						)
      return im



@torch.no_grad()
def 				snake_case       (  a_				:			Dict     , a_				:			str     , a_				:			Dict=False						)							->					Optional[Any]:





      """simple docstring"""

      UpperCamelCase_					: Any            =				get_config(a_						)

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

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

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

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

      UpperCamelCase_					: Optional[int]            =				{
          """bilinear""": PILImageResampling.BILINEAR,
          """bicubic""": PILImageResampling.BICUBIC,
          """nearest""": PILImageResampling.NEAREST,
      }

      UpperCamelCase_					: Optional[int]            =				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()     , )

      UpperCamelCase_					: List[Any]            =				prepare_img()
      UpperCamelCase_					: str            =				transform(a_						).unsqueeze(0						)
      UpperCamelCase_					: Any            =				processor(a_     , return_tensors="""pt"""						).pixel_values

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

      # verify logits
      with torch.no_grad():
            UpperCamelCase_					: Optional[int]            =				model(a_						)
            UpperCamelCase_					: Any            =				outputs.logits

      print("""Logits:"""     , logits[0, :3]						)
      print("""Predicted class:"""     , model.config.idalabel[logits.argmax(-1						).item()]						)
      UpperCamelCase_					: List[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__":
      UpperCamelCase      					=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      					=parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)














'''simple docstring'''

from __future__ import annotations



def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]     , a_				:			list[int]     , a_				:			list[list[str]]     , a_				:			int     , )							->					None:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				len(a_						)

      # If row is equal to the size of the board it means there are a queen in each row in
      # the current board (possible_board)
      if row == n:
            # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
            # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
            boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]						)
            return

      # We iterate each column in the row to find all possible results in each row
      for col in range(a_						):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
            # 135º: y + x = b or row + col = b.
            #
            # And we verify if the results of this two formulas not exist in their variables
            # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
            #
            # If any or these are True it means there is a collision so we continue to the
            # next value in the for loop.
            if (
                col in possible_board
                or row - col in diagonal_right_collisions
                or row + col in diagonal_left_collisions
            ):
                  continue

            # If it is False we call dfs function again and we update the inputs
            depth_first_search(
                [*possible_board, col]     , [*diagonal_right_collisions, row - col]     , [*diagonal_left_collisions, row + col]     , a_     , a_     , )



def 				snake_case       (  a_				:			int						)							->					None:





      """simple docstring"""

      UpperCamelCase_					: list[list[str]]            =				[]
      depth_first_search([]     , []     , []     , a_     , a_						)

      # Print all the boards
      for board in boards:
            for column in board:
                  print(a_						)
            print(""""""						)

      print(len(a_						)     , """solutions were found."""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      n_queens_solution(4)






