





'''simple docstring'''







from typing import Any
class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	List[str]:
      snake_case				=			data
      snake_case				=			None







     def __repr__( self    ) ->	str:
      return F"""Node({self.data})"""




class 			_lowercase						:







     def __init__( self    ) ->	Optional[int]:
      snake_case				=			None







     def __iter__( self    ) ->	Any:
      snake_case				=			self.head
      while node:
       yield node.data
       snake_case				=			node.next







     def __len__( self    ) ->	int:
      return sum(1 for _ in self    )







     def __repr__( self    ) ->	str:
      return "->".join([str(A__    ) for item in self]    )







     def __getitem__( self    ,   A__    ) ->	Any:
      if not 0 <= index < len(self    ):
       raise ValueError('''list index out of range.'''    )
      for i, node in enumerate(self    ):
       if i == index:
        return node
      return None







     def __setitem__( self    ,   A__    ,   A__    ) ->	None:
      if not 0 <= index < len(self    ):
       raise ValueError('''list index out of range.'''    )
      snake_case				=			self.head
      for _ in range(A__    ):
       snake_case				=			current.next
      snake_case				=			data







     def     UpperCamelCase			( self    ,   A__    ) ->	None:
      self.insert_nth(len(self    )    ,   A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	None:
      self.insert_nth(0    ,   A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	None:
      if not 0 <= index <= len(self    ):
       raise IndexError('''list index out of range'''    )
      snake_case				=			Node(A__    )
      if self.head is None:
       snake_case				=			new_node
      elif index == 0:
       snake_case				=			self.head  # link new_node to head
       snake_case				=			new_node
      else:
       snake_case				=			self.head
       for _ in range(index - 1    ):
        snake_case				=			temp.next
       snake_case				=			temp.next
       snake_case				=			new_node







     def     UpperCamelCase			( self    ) ->	None:  # print every node data
      print(self    )







     def     UpperCamelCase			( self    ) ->	Any:
      return self.delete_nth(0    )







     def     UpperCamelCase			( self    ) ->	Any:  # delete from tail
      return self.delete_nth(len(self    ) - 1    )







     def     UpperCamelCase			( self    ,   A__ = 0    ) ->	Any:
      if not 0 <= index <= len(self    ) - 1:  # test if index is valid
       raise IndexError('''List index out of range.'''    )
      snake_case				=			self.head  # default first node
      if index == 0:
       snake_case				=			self.head.next
      else:
       snake_case				=			self.head
       for _ in range(index - 1    ):
        snake_case				=			temp.next
       snake_case				=			temp.next
       snake_case				=			temp.next.next
      return delete_node.data







     def     UpperCamelCase			( self    ) ->	bool:
      return self.head is None







     def     UpperCamelCase			( self    ) ->	None:
      snake_case				=			None
      snake_case				=			self.head

      while current:
       # Store the current node's next node.
       snake_case				=			current.next
       # Make the current node's next point backwards
       snake_case				=			prev
       # Make the previous node be the current node
       snake_case				=			current
       # Make the current node the next node (to progress iteration)
       snake_case				=			next_node
      # Return prev in order to put the head at the end
      snake_case				=			prev




def    __UpperCamelCase	(	) ->None:
 snake_case				=			LinkedList()
 assert linked_list.is_empty() is True
 assert str(a     ) == ""

 try:
  linked_list.delete_head()
  raise AssertionError  # This should not happen.
 except IndexError:
  assert True  # This should happen.

 try:
  linked_list.delete_tail()
  raise AssertionError  # This should not happen.
 except IndexError:
  assert True  # This should happen.

 for i in range(10     ):
  assert len(a     ) == i
  linked_list.insert_nth(a					,   i + 1     )
 assert str(a     ) == "->".join(str(a     ) for i in range(1					,   11     )     )

 linked_list.insert_head(0     )
 linked_list.insert_tail(11     )
 assert str(a     ) == "->".join(str(a     ) for i in range(0					,   12     )     )

 assert linked_list.delete_head() == 0
 assert linked_list.delete_nth(9     ) == 10
 assert linked_list.delete_tail() == 11
 assert len(a     ) == 9
 assert str(a     ) == "->".join(str(a     ) for i in range(1					,   10     )     )

 assert all(linked_list[i] == i + 1 for i in range(0					,   9     )     ) is True

 for i in range(0					,   9     ):
  snake_case				=			-i
 assert all(linked_list[i] == -i for i in range(0					,   9     )     ) is True

 linked_list.reverse()
 assert str(a     ) == "->".join(str(a     ) for i in range(-8					,   1     )     )

def    __UpperCamelCase	(	) ->None:
 snake_case				=			[
     -9,
     100,
     Node(7734_5112     ),
     '''dlrow olleH''',
     7,
     5555,
     0,
     -192.55555,
     '''Hello, world!''',
     77.9,
     Node(10     ),
     None,
     None,
     12.20,
 ]
 snake_case				=			LinkedList()

 for i in test_input:
  linked_list.insert_tail(a     )

 # Check if it's empty or not
 assert linked_list.is_empty() is False
 assert (
     str(a     ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
     "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
 )

 # Delete the head
 snake_case				=			linked_list.delete_head()
 assert result == -9
 assert (
     str(a     ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
     "Hello, world!->77.9->Node(10)->None->None->12.2"
 )

 # Delete the tail
 snake_case				=			linked_list.delete_tail()
 assert result == 12.2
 assert (
     str(a     ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
     "Hello, world!->77.9->Node(10)->None->None"
 )

 # Delete a node in specific location in linked list
 snake_case				=			linked_list.delete_nth(10     )
 assert result is None
 assert (
     str(a     ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
     "Hello, world!->77.9->Node(10)->None"
 )

 # Add a Node instance to its head
 linked_list.insert_head(Node('''Hello again, world!'''     )     )
 assert (
     str(a     )
     == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
     "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
 )

 # Add None to its tail
 linked_list.insert_tail(a     )
 assert (
     str(a     )
     == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
     "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
 )

 # Reverse the linked list
 linked_list.reverse()
 assert (
     str(a     )
     == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
     "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
 )

def    __UpperCamelCase	(	) ->Tuple:
 from doctest import testmod

 testmod()

 snake_case				=			LinkedList()
 linked_list.insert_head(input('''Inserting 1st at head '''     ).strip()     )
 linked_list.insert_head(input('''Inserting 2nd at head '''     ).strip()     )
 print('''\nPrint list:'''     )
 linked_list.print_list()
 linked_list.insert_tail(input('''\nInserting 1st at tail '''     ).strip()     )
 linked_list.insert_tail(input('''Inserting 2nd at tail '''     ).strip()     )
 print('''\nPrint list:'''     )
 linked_list.print_list()
 print('''\nDelete head'''     )
 linked_list.delete_head()
 print('''Delete tail'''     )
 linked_list.delete_tail()
 print('''\nPrint list:'''     )
 linked_list.print_list()
 print('''\nReverse linked list'''     )
 linked_list.reverse()
 print('''\nPrint list:'''     )
 linked_list.print_list()
 print('''\nString representation of linked list:'''     )
 print(a     )
 print('''\nReading/changing Node data using indexing:'''     )
 print(f"""Element at Position 1: {linked_list[1]}"""     )
 snake_case				=			input('''Enter New Value: '''     ).strip()
 print('''New list:'''     )
 print(a     )
 print(f"""length of linked_list is : {len(a     )}"""     )


if __name__ == "__main__":
   main()








'''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       						=			logging.get_logger(__name__)

def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[int]					,   a    : Dict					,   a    : Dict     ) ->Union[str, Any]:
 snake_case				=			original_name.split('''.'''     )[0]
 snake_case				=			key.split('''.'''     )
 snake_case				=			int(key_list[key_list.index(a     ) - 2]     )
 snake_case				=			int(key_list[key_list.index(a     ) - 1]     )
 snake_case				=			orig_block_num - offset

 snake_case				=			key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""					,   f"""block.{new_block_num}.{layer_num}.{new_name}"""     )
 return key

def    __UpperCamelCase	(	a    : Tuple     ) ->Dict:
 snake_case				=			OrderedDict()
 snake_case				,		snake_case				=			0, 0
 for key, value in state_dict.items():
  if key.startswith('''network'''     ):
   snake_case				=			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
   snake_case				=			key[: key.find('''proj'''     )]
   snake_case				=			key.replace(a					,   f"""patch_embeddings.{total_embed_found}."""     )
   snake_case				=			key.replace('''proj'''					,   '''projection'''     )
   if key.endswith('''bias'''     ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   snake_case				=			'''poolformer.encoder.''' + key
  if "mlp.fc1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc1'''					,   '''output.conv1'''     )
  if "mlp.fc2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc2'''					,   '''output.conv2'''     )
  if "norm1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm1'''					,   '''before_norm'''     )
  if "norm2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm2'''					,   '''after_norm'''     )
  if "layer_scale_1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_1'''					,   '''layer_scale_1'''     )
  if "layer_scale_2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_2'''					,   '''layer_scale_2'''     )
  if "head" in key:
   snake_case				=			key.replace('''head'''					,   '''classifier'''     )
  snake_case				=			value
 return new_state_dict

def    __UpperCamelCase	(	) ->Optional[int]:
 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )

 return image

@torch.no_grad()
def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[Any]					,   a    : Tuple     ) ->List[str]:
 snake_case				=			PoolFormerConfig()

 # set attributes based on model_name
 snake_case				=			'''huggingface/label-files'''
 snake_case				=			model_name[-3:]
 snake_case				=			1000
 snake_case				=			'''imagenet-1k-id2label.json'''
 snake_case				=			(1, 1000)

 # set config attributes
 snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
 snake_case				=			{int(a     ): v for k, v in idalabel.items()}
 snake_case				=			idalabel
 snake_case				=			{v: k for k, v in idalabel.items()}
 if size == "s12":
  snake_case				=			[2, 2, 6, 2]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			0.9
 elif size == "s24":
  snake_case				=			[4, 4, 12, 4]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			0.9
 elif size == "s36":
  snake_case				=			[6, 6, 18, 6]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.9
 elif size == "m36":
  snake_case				=			[6, 6, 18, 6]
  snake_case				=			[96, 192, 384, 768]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.95
 elif size == "m48":
  snake_case				=			[8, 8, 24, 8]
  snake_case				=			[96, 192, 384, 768]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.95
 else:
  raise ValueError(f"""Size {size} not supported"""     )

 # load image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )

 # Prepare image
 snake_case				=			prepare_img()
 snake_case				=			image_processor(images=a					,   return_tensors='''pt'''     ).pixel_values

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

 # load original state dict
 snake_case				=			torch.load(a					,   map_location=torch.device('''cpu'''     )     )

 # rename keys
 snake_case				=			rename_keys(a     )

 # create HuggingFace model and load state dict
 snake_case				=			PoolFormerForImageClassification(a     )
 model.load_state_dict(a     )
 model.eval()

 # Define image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )
 snake_case				=			image_processor(images=prepare_img()					,   return_tensors='''pt'''     ).pixel_values

 # forward pass
 snake_case				=			model(a     )
 snake_case				=			outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  snake_case				=			torch.tensor([-0.3045, -0.6758, -0.4869]     )
 elif size == "s24":
  snake_case				=			torch.tensor([0.4402, -0.1374, -0.8045]     )
 elif size == "s36":
  snake_case				=			torch.tensor([-0.6080, -0.5133, -0.5898]     )
 elif size == "m36":
  snake_case				=			torch.tensor([0.3952, 0.2263, -1.2668]     )
 elif size == "m48":
  snake_case				=			torch.tensor([0.1167, -0.0656, -0.3423]     )
 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__":
   _lowercase       						=			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       						=			parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

