


'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase     :  int											=    logging.get_logger(__name__)






def       lowercase					(_A ,    _A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							original_name.split('.'       )[0]
       _lowerCAmelCase     :				Tuple       =							key.split('.'       )
       _lowerCAmelCase     :				List[Any]       =							int(key_list[key_list.index(_A       ) - 2]       )
       _lowerCAmelCase     :				Optional[Any]       =							int(key_list[key_list.index(_A       ) - 1]       )
       _lowerCAmelCase     :				Any       =							orig_block_num - offset

       _lowerCAmelCase     :				Union[str, Any]       =							key.replace(f'{orig_block_num}.{layer_num}.{original_name}' ,    f'block.{new_block_num}.{layer_num}.{new_name}'       )
       return key






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							OrderedDict()
       _lowerCAmelCase      ,  _lowerCAmelCase     :				str       =							0, 0
       for key, value in state_dict.items():
              if key.startswith('network'       ):
                     _lowerCAmelCase     :				Union[str, Any]       =							key.replace('network' ,    'poolformer.encoder'       )
              if "proj" in key:
                     # Works for the first embedding as well as the internal embedding layers
                     if key.endswith('bias'       ) and "patch_embed" not in key:
                            patch_emb_offset += 1
                     _lowerCAmelCase     :				List[Any]       =							key[: key.find('proj'       )]
                     _lowerCAmelCase     :				Optional[int]       =							key.replace(_A ,    f'patch_embeddings.{total_embed_found}.'       )
                     _lowerCAmelCase     :				Optional[Any]       =							key.replace('proj' ,    'projection'       )
                     if key.endswith('bias'       ):
                            total_embed_found += 1
              if "patch_embeddings" in key:
                     _lowerCAmelCase     :				Optional[Any]       =							'poolformer.encoder.' + key
              if "mlp.fc1" in key:
                     _lowerCAmelCase     :				Dict       =							replace_key_with_offset(_A ,    _A ,    'mlp.fc1' ,    'output.conv1'       )
              if "mlp.fc2" in key:
                     _lowerCAmelCase     :				List[Any]       =							replace_key_with_offset(_A ,    _A ,    'mlp.fc2' ,    'output.conv2'       )
              if "norm1" in key:
                     _lowerCAmelCase     :				Tuple       =							replace_key_with_offset(_A ,    _A ,    'norm1' ,    'before_norm'       )
              if "norm2" in key:
                     _lowerCAmelCase     :				Dict       =							replace_key_with_offset(_A ,    _A ,    'norm2' ,    'after_norm'       )
              if "layer_scale_1" in key:
                     _lowerCAmelCase     :				Optional[Any]       =							replace_key_with_offset(_A ,    _A ,    'layer_scale_1' ,    'layer_scale_1'       )
              if "layer_scale_2" in key:
                     _lowerCAmelCase     :				Union[str, Any]       =							replace_key_with_offset(_A ,    _A ,    'layer_scale_2' ,    'layer_scale_2'       )
              if "head" in key:
                     _lowerCAmelCase     :				Optional[Any]       =							key.replace('head' ,    'classifier'       )
              _lowerCAmelCase     :				Dict       =							value
       return new_state_dict






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							'http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCAmelCase     :				str       =							Image.open(requests.get(_A ,    stream=_A       ).raw       )

       return image






@torch.no_grad()
def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Dict       =							PoolFormerConfig()

       # set attributes based on model_name
       _lowerCAmelCase     :				Dict       =							'huggingface/label-files'
       _lowerCAmelCase     :				str       =							model_name[-3:]
       _lowerCAmelCase     :				int       =							1_0_0_0
       _lowerCAmelCase     :				List[str]       =							'imagenet-1k-id2label.json'
       _lowerCAmelCase     :				Dict       =							(1, 1_0_0_0)

       # set config attributes
       _lowerCAmelCase     :				Optional[Any]       =							json.load(open(hf_hub_download(_A ,    _A ,    repo_type='dataset'       ) ,    'r'       )       )
       _lowerCAmelCase     :				str       =							{int(_A       ): v for k, v in idalabel.items()}
       _lowerCAmelCase     :				Optional[int]       =							idalabel
       _lowerCAmelCase     :				Union[str, Any]       =							{v: k for k, v in idalabel.items()}
       if size == "s12":
              _lowerCAmelCase     :				Any       =							[2, 2, 6, 2]
              _lowerCAmelCase     :				Optional[int]       =							[6_4, 1_2_8, 3_2_0, 5_1_2]
              _lowerCAmelCase     :				int       =							4.0
              _lowerCAmelCase     :				List[str]       =							0.9
       elif size == "s24":
              _lowerCAmelCase     :				Union[str, Any]       =							[4, 4, 1_2, 4]
              _lowerCAmelCase     :				Tuple       =							[6_4, 1_2_8, 3_2_0, 5_1_2]
              _lowerCAmelCase     :				Optional[int]       =							4.0
              _lowerCAmelCase     :				int       =							0.9
       elif size == "s36":
              _lowerCAmelCase     :				int       =							[6, 6, 1_8, 6]
              _lowerCAmelCase     :				Tuple       =							[6_4, 1_2_8, 3_2_0, 5_1_2]
              _lowerCAmelCase     :				Optional[int]       =							4.0
              _lowerCAmelCase     :				Dict       =							1E-6
              _lowerCAmelCase     :				List[Any]       =							0.9
       elif size == "m36":
              _lowerCAmelCase     :				Optional[Any]       =							[6, 6, 1_8, 6]
              _lowerCAmelCase     :				int       =							[9_6, 1_9_2, 3_8_4, 7_6_8]
              _lowerCAmelCase     :				List[str]       =							4.0
              _lowerCAmelCase     :				List[str]       =							1E-6
              _lowerCAmelCase     :				Dict       =							0.95
       elif size == "m48":
              _lowerCAmelCase     :				List[Any]       =							[8, 8, 2_4, 8]
              _lowerCAmelCase     :				str       =							[9_6, 1_9_2, 3_8_4, 7_6_8]
              _lowerCAmelCase     :				Dict       =							4.0
              _lowerCAmelCase     :				Tuple       =							1E-6
              _lowerCAmelCase     :				str       =							0.95
       else:
              raise ValueError(f'Size {size} not supported'       )

       # load image processor
       _lowerCAmelCase     :				Dict       =							PoolFormerImageProcessor(crop_pct=_A       )

       # Prepare image
       _lowerCAmelCase     :				str       =							prepare_img()
       _lowerCAmelCase     :				Tuple       =							image_processor(images=_A ,    return_tensors='pt'       ).pixel_values

       logger.info(f'Converting model {model_name}...'       )

       # load original state dict
       _lowerCAmelCase     :				str       =							torch.load(_A ,    map_location=torch.device('cpu'       )       )

       # rename keys
       _lowerCAmelCase     :				int       =							rename_keys(_A       )

       # create HuggingFace model and load state dict
       _lowerCAmelCase     :				Tuple       =							PoolFormerForImageClassification(_A       )
       model.load_state_dict(_A       )
       model.eval()

       # Define image processor
       _lowerCAmelCase     :				Optional[int]       =							PoolFormerImageProcessor(crop_pct=_A       )
       _lowerCAmelCase     :				int       =							image_processor(images=prepare_img() ,    return_tensors='pt'       ).pixel_values

       # forward pass
       _lowerCAmelCase     :				List[str]       =							model(_A       )
       _lowerCAmelCase     :				List[Any]       =							outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              _lowerCAmelCase     :				Union[str, Any]       =							torch.tensor([-0.3_045, -0.6_758, -0.4_869]       )
       elif size == "s24":
              _lowerCAmelCase     :				int       =							torch.tensor([0.4_402, -0.1_374, -0.8_045]       )
       elif size == "s36":
              _lowerCAmelCase     :				str       =							torch.tensor([-0.6_080, -0.5_133, -0.5_898]       )
       elif size == "m36":
              _lowerCAmelCase     :				Tuple       =							torch.tensor([0.3_952, 0.2_263, -1.2_668]       )
       elif size == "m48":
              _lowerCAmelCase     :				Any       =							torch.tensor([0.1_167, -0.0_656, -0.3_423]       )
       else:
              raise ValueError(f'Size {size} not supported'       )

       # verify logits
       assert logits.shape == expected_shape
       assert torch.allclose(logits[0, :3] ,    _A ,    atol=1E-2       )

       # finally, save model and image processor
       logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'       )
       Path(_A       ).mkdir(exist_ok=_A       )
       model.save_pretrained(_A       )
       print(f'Saving image processor to {pytorch_dump_folder_path}'       )
       image_processor.save_pretrained(_A       )


if __name__ == "__main__":
       lowerCAmelCase     :  Optional[int]											=    argparse.ArgumentParser()

       parser.add_argument(
           """--model_name""",
           default="""poolformer_s12""",
           type=str,
           help="""Name of the model you'd like to convert.""",
       )
       parser.add_argument(
           """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
       )
       lowerCAmelCase     :  Union[str, Any]											=    parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							len(_A       )
       for i in range(length - 1       ):
              _lowerCAmelCase     :				List[Any]       =							i
              for k in range(i + 1 ,    _A       ):
                     if collection[k] < collection[least]:
                            _lowerCAmelCase     :				List[str]       =							k
              if least != i:
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							(collection[i], collection[least])
       return collection


if __name__ == "__main__":
       lowerCAmelCase     :  Optional[Any]											=    input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase     :  Tuple											=    [int(item) for item in user_input.split(""",""")]
       print(selection_sort(unsorted))

