


'''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()
__lowercase	:						Dict       =	logging.get_logger(__name__)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Any   ):
					__a			:       List[str]					    =  original_name.split('.'   )[0]
					__a			:       Tuple					    =  key.split('.'   )
					__a			:       Any					    =  int(key_list[key_list.index(_SCREAMING_SNAKE_CASE   ) - 2]   )
					__a			:       Union[str, Any]					    =  int(key_list[key_list.index(_SCREAMING_SNAKE_CASE   ) - 1]   )
					__a			:       List[Any]					    =  orig_block_num - offset

					__a			:       List[Any]					    =  key.replace(F"""{orig_block_num}.{layer_num}.{original_name}"""						,      F"""block.{new_block_num}.{layer_num}.{new_name}"""   )
					return key






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       Optional[int]					    =  OrderedDict()
					__a    ,				__a			:       Union[str, Any]					    =  0, 0
					for key, value in state_dict.items():
										if key.startswith('network'   ):
															__a			:       Optional[int]					    =  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
															__a			:       Dict					    =  key[: key.find('proj'   )]
															__a			:       Union[str, Any]					    =  key.replace(_SCREAMING_SNAKE_CASE						,      F"""patch_embeddings.{total_embed_found}."""   )
															__a			:       List[str]					    =  key.replace('proj'						,      'projection'   )
															if key.endswith('bias'   ):
																				total_embed_found += 1
										if "patch_embeddings" in key:
															__a			:       str					    =  'poolformer.encoder.' + key
										if "mlp.fc1" in key:
															__a			:       Any					    =  replace_key_with_offset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      'mlp.fc1'						,      'output.conv1'   )
										if "mlp.fc2" in key:
															__a			:       str					    =  replace_key_with_offset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      'mlp.fc2'						,      'output.conv2'   )
										if "norm1" in key:
															__a			:       Tuple					    =  replace_key_with_offset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      'norm1'						,      'before_norm'   )
										if "norm2" in key:
															__a			:       str					    =  replace_key_with_offset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      'norm2'						,      'after_norm'   )
										if "layer_scale_1" in key:
															__a			:       Any					    =  replace_key_with_offset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      'layer_scale_1'						,      'layer_scale_1'   )
										if "layer_scale_2" in key:
															__a			:       Tuple					    =  replace_key_with_offset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      'layer_scale_2'						,      'layer_scale_2'   )
										if "head" in key:
															__a			:       List[str]					    =  key.replace('head'						,      'classifier'   )
										__a			:       int					    =  value
					return new_state_dict






def   lowerCamelCase							():
					__a			:       int					    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a			:       Optional[int]					    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,      stream=_SCREAMING_SNAKE_CASE   ).raw   )

					return image






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Tuple					    =  PoolFormerConfig()

					# set attributes based on model_name
					__a			:       str					    =  'huggingface/label-files'
					__a			:       str					    =  model_name[-3:]
					__a			:       Optional[int]					    =  1_000
					__a			:       Optional[int]					    =  'imagenet-1k-id2label.json'
					__a			:       Any					    =  (1, 1_000)

					# set config attributes
					__a			:       Optional[int]					    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      repo_type='dataset'   )						,      'r'   )   )
					__a			:       Optional[Any]					    =  {int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
					__a			:       Optional[Any]					    =  idalabel
					__a			:       Optional[Any]					    =  {v: k for k, v in idalabel.items()}
					if size == "s12":
										__a			:       int					    =  [2, 2, 6, 2]
										__a			:       str					    =  [64, 128, 320, 512]
										__a			:       Tuple					    =  4.0
										__a			:       List[Any]					    =  0.9
					elif size == "s24":
										__a			:       Union[str, Any]					    =  [4, 4, 12, 4]
										__a			:       str					    =  [64, 128, 320, 512]
										__a			:       Optional[Any]					    =  4.0
										__a			:       Tuple					    =  0.9
					elif size == "s36":
										__a			:       str					    =  [6, 6, 18, 6]
										__a			:       str					    =  [64, 128, 320, 512]
										__a			:       str					    =  4.0
										__a			:       Any					    =  1e-6
										__a			:       int					    =  0.9
					elif size == "m36":
										__a			:       Any					    =  [6, 6, 18, 6]
										__a			:       str					    =  [96, 192, 384, 768]
										__a			:       Dict					    =  4.0
										__a			:       Optional[Any]					    =  1e-6
										__a			:       List[str]					    =  0.9_5
					elif size == "m48":
										__a			:       Union[str, Any]					    =  [8, 8, 24, 8]
										__a			:       List[str]					    =  [96, 192, 384, 768]
										__a			:       Tuple					    =  4.0
										__a			:       List[Any]					    =  1e-6
										__a			:       int					    =  0.9_5
					else:
										raise ValueError(F"""Size {size} not supported"""   )

					# load image processor
					__a			:       List[Any]					    =  PoolFormerImageProcessor(crop_pct=_SCREAMING_SNAKE_CASE   )

					# Prepare image
					__a			:       Dict					    =  prepare_img()
					__a			:       Dict					    =  image_processor(images=_SCREAMING_SNAKE_CASE						,      return_tensors='pt'   ).pixel_values

					logger.info(F"""Converting model {model_name}..."""   )

					# load original state dict
					__a			:       Optional[int]					    =  torch.load(_SCREAMING_SNAKE_CASE						,      map_location=torch.device('cpu'   )   )

					# rename keys
					__a			:       Optional[int]					    =  rename_keys(_SCREAMING_SNAKE_CASE   )

					# create HuggingFace model and load state dict
					__a			:       Any					    =  PoolFormerForImageClassification(_SCREAMING_SNAKE_CASE   )
					model.load_state_dict(_SCREAMING_SNAKE_CASE   )
					model.eval()

					# Define image processor
					__a			:       Tuple					    =  PoolFormerImageProcessor(crop_pct=_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  image_processor(images=prepare_img()						,      return_tensors='pt'   ).pixel_values

					# forward pass
					__a			:       int					    =  model(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										__a			:       str					    =  torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]   )
					elif size == "s24":
										__a			:       Tuple					    =  torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]   )
					elif size == "s36":
										__a			:       List[Any]					    =  torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]   )
					elif size == "m36":
										__a			:       Any					    =  torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]   )
					elif size == "m48":
										__a			:       Dict					    =  torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]   )
					else:
										raise ValueError(F"""Size {size} not supported"""   )

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

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


if __name__ == "__main__":
	__lowercase	:						Tuple       =	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.'
	)
	__lowercase	:						Dict       =	parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Any:
  stooge(__lowerCamelCase ,					0 ,					len(__lowerCamelCase		) - 1		)
  return arr
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str		)     ->					int:
  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case     ,					_snake_case					     =       arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case					     =       (int)((h - i + 1) / 3		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)

    # Recursively sort last 2/3 elements
    stooge(__lowerCamelCase ,					i + t ,					(__lowerCamelCase)		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)


if __name__ == "__main__":
 UpperCAmelCase__          = input('Enter numbers separated by a comma:\n').strip()
 UpperCAmelCase__          = [int(item) for item in user_input.split(',')]
 print(stooge_sort(unsorted))



