






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()
A__			:    Tuple         =				logging.get_logger(__name__)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    original_name.split('''.'''       )[0]
				lowercase__     				=    key.split('''.'''       )
				lowercase__     				=    int(key_list[key_list.index(lowerCamelCase_       ) - 2]       )
				lowercase__     				=    int(key_list[key_list.index(lowerCamelCase_       ) - 1]       )
				lowercase__     				=    orig_block_num - offset

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






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    OrderedDict()
				lowercase__				,		lowercase__     				=    0, 0
				for key, value in state_dict.items():
								if key.startswith('''network'''       ):
												lowercase__     				=    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
												lowercase__     				=    key[: key.find('''proj'''       )]
												lowercase__     				=    key.replace(lowerCamelCase_	,     F"""patch_embeddings.{total_embed_found}."""       )
												lowercase__     				=    key.replace('''proj'''	,     '''projection'''       )
												if key.endswith('''bias'''       ):
																total_embed_found += 1
								if "patch_embeddings" in key:
												lowercase__     				=    '''poolformer.encoder.''' + key
								if "mlp.fc1" in key:
												lowercase__     				=    replace_key_with_offset(lowerCamelCase_	,     lowerCamelCase_	,     '''mlp.fc1'''	,     '''output.conv1'''       )
								if "mlp.fc2" in key:
												lowercase__     				=    replace_key_with_offset(lowerCamelCase_	,     lowerCamelCase_	,     '''mlp.fc2'''	,     '''output.conv2'''       )
								if "norm1" in key:
												lowercase__     				=    replace_key_with_offset(lowerCamelCase_	,     lowerCamelCase_	,     '''norm1'''	,     '''before_norm'''       )
								if "norm2" in key:
												lowercase__     				=    replace_key_with_offset(lowerCamelCase_	,     lowerCamelCase_	,     '''norm2'''	,     '''after_norm'''       )
								if "layer_scale_1" in key:
												lowercase__     				=    replace_key_with_offset(lowerCamelCase_	,     lowerCamelCase_	,     '''layer_scale_1'''	,     '''layer_scale_1'''       )
								if "layer_scale_2" in key:
												lowercase__     				=    replace_key_with_offset(lowerCamelCase_	,     lowerCamelCase_	,     '''layer_scale_2'''	,     '''layer_scale_2'''       )
								if "head" in key:
												lowercase__     				=    key.replace('''head'''	,     '''classifier'''       )
								lowercase__     				=    value
				return new_state_dict






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase__     				=    Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_       ).raw       )

				return image






@torch.no_grad()
def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    PoolFormerConfig()

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

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

				# load image processor
				lowercase__     				=    PoolFormerImageProcessor(crop_pct=lowerCamelCase_       )

				# Prepare image
				lowercase__     				=    prepare_img()
				lowercase__     				=    image_processor(images=lowerCamelCase_	,     return_tensors='''pt'''       ).pixel_values

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

				# load original state dict
				lowercase__     				=    torch.load(lowerCamelCase_	,     map_location=torch.device('''cpu'''       )       )

				# rename keys
				lowercase__     				=    rename_keys(lowerCamelCase_       )

				# create HuggingFace model and load state dict
				lowercase__     				=    PoolFormerForImageClassification(lowerCamelCase_       )
				model.load_state_dict(lowerCamelCase_       )
				model.eval()

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

				# forward pass
				lowercase__     				=    model(lowerCamelCase_       )
				lowercase__     				=    outputs.logits

				# define expected logit slices for different models
				if size == "s12":
								lowercase__     				=    torch.tensor([-0.30_45, -0.67_58, -0.48_69]       )
				elif size == "s24":
								lowercase__     				=    torch.tensor([0.44_02, -0.13_74, -0.80_45]       )
				elif size == "s36":
								lowercase__     				=    torch.tensor([-0.60_80, -0.51_33, -0.58_98]       )
				elif size == "m36":
								lowercase__     				=    torch.tensor([0.39_52, 0.22_63, -1.26_68]       )
				elif size == "m48":
								lowercase__     				=    torch.tensor([0.11_67, -0.06_56, -0.34_23]       )
				else:
								raise ValueError(F"""Size {size} not supported"""       )

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

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


if __name__ == "__main__":
					A__			:    Union[str, Any]         =				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.'
					)
					A__			:    Union[str, Any]         =				parser.parse_args()
					convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)












class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					str = "",						lowerCamelCase		:					bool = False						):

								'''simple docstring'''
								# Mapping from the first character of the prefix of the node
								lowercase__     				=    {}

								# A node will be a leaf if the tree contains its word
								lowercase__     				=    is_leaf

								lowercase__     				=    prefix




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    0
								for q, w in zip(self.prefix,						lowerCamelCase						):
												if q != w:
																break

												x += 1

								return self.prefix[:x], self.prefix[x:], word[x:]




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					list[str]						):

								'''simple docstring'''
								for word in words:
												self.insert(lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str						):

								'''simple docstring'''
								# Case 1: If the word is the prefix of the node
								# Solution: We set the current node as leaf
								if self.prefix == word:
												lowercase__     				=    True

								# Case 2: The node has no edges that have a prefix to the word
								# Solution: We create an edge from the current node to a new one
								# containing the word
								elif word[0] not in self.nodes:
												lowercase__     				=    RadixNode(prefix=lowerCamelCase,						is_leaf=lowerCamelCase						)

								else:
												lowercase__     				=    self.nodes[word[0]]
												lowercase__				,		lowercase__				,		lowercase__     				=    incoming_node.match(
												    lowerCamelCase						)

												# Case 3: The node prefix is equal to the matching
												# Solution: We insert remaining word on the next node
												if remaining_prefix == "":
																self.nodes[matching_string[0]].insert(lowerCamelCase						)

												# Case 4: The word is greater equal to the matching
												# Solution: Create a node in between both nodes, change
												# prefixes and add the new node for the remaining word
												else:
																lowercase__     				=    remaining_prefix

																lowercase__     				=    self.nodes[matching_string[0]]
																lowercase__     				=    RadixNode(lowerCamelCase,						lowerCamelCase						)
																lowercase__     				=    aux_node

																if remaining_word == "":
																				lowercase__     				=    True
																else:
																				self.nodes[matching_string[0]].insert(lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.nodes.get(word[0],						lowerCamelCase						)
								if not incoming_node:
												return False
								else:
												lowercase__				,		lowercase__				,		lowercase__     				=    incoming_node.match(
												    lowerCamelCase						)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																return False
												# This applies when the word and the prefix are equal
												elif remaining_word == "":
																return incoming_node.is_leaf
												# We have word remaining so we check the next node
												else:
																return incoming_node.find(lowerCamelCase						)




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.nodes.get(word[0],						lowerCamelCase						)
								if not incoming_node:
												return False
								else:
												lowercase__				,		lowercase__				,		lowercase__     				=    incoming_node.match(
												    lowerCamelCase						)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																return False
												# We have word remaining so we check the next node
												elif remaining_word != "":
																return incoming_node.delete(lowerCamelCase						)
												else:
																# If it is not a leaf, we don't have to delete
																if not incoming_node.is_leaf:
																				return False
																else:
																				# We delete the nodes if no edges go from it
																				if len(incoming_node.nodes						) == 0:
																								del self.nodes[word[0]]
																								# We merge the current node with its only child
																								if len(self.nodes						) == 1 and not self.is_leaf:
																												lowercase__     				=    list(self.nodes.values()						)[0]
																												lowercase__     				=    merging_node.is_leaf
																												self.prefix += merging_node.prefix
																												lowercase__     				=    merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																				elif len(incoming_node.nodes						) > 1:
																								lowercase__     				=    False
																				# If there is 1 edge, we merge it with its child
																				else:
																								lowercase__     				=    list(incoming_node.nodes.values()						)[0]
																								lowercase__     				=    merging_node.is_leaf
																								incoming_node.prefix += merging_node.prefix
																								lowercase__     				=    merging_node.nodes

																				return True




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int = 0						):

								'''simple docstring'''
								if self.prefix != "":
												print('''-''' * height,						self.prefix,						'''  (leaf)''' if self.is_leaf else ''''''						)

								for value in self.nodes.values():
												value.print_tree(height + 1						)






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    '''banana bananas bandana band apple all beast'''.split()
				lowercase__     				=    RadixNode()
				root.insert_many(lowerCamelCase_       )

				assert all(root.find(lowerCamelCase_       ) for word in words       )
				assert not root.find('''bandanas'''       )
				assert not root.find('''apps'''       )
				root.delete('''all'''       )
				assert not root.find('''all'''       )
				root.delete('''banana'''       )
				assert not root.find('''banana'''       )
				assert root.find('''bananas'''       )

				return True






def 						a    (		):






				'''simple docstring'''
				assert test_trie()






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    RadixNode()
				lowercase__     				=    '''banana bananas bandanas bandana band apple all beast'''.split()
				root.insert_many(lowerCamelCase_       )

				print('''Words:'''	,     lowerCamelCase_       )
				print('''Tree:'''       )
				root.print_tree()


if __name__ == "__main__":
							main()




