


'''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()
__snake_case							   =  logging.get_logger(__name__)


def     a   (    __a				,      __a				,      __a				,      __a     )						->			int:



					'''simple docstring'''






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

					UpperCamelCase__     :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     a   (    __a     )						->			Tuple:



					'''simple docstring'''






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


def     a   (    )						->			Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__     :int							      =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					UpperCamelCase__     :str							      =				Image.open(requests.get(__a				,      stream=__a     ).raw     )

					return image


@torch.no_grad()
def     a   (    __a				,      __a				,      __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				PoolFormerConfig()

					# set attributes based on model_name
					UpperCamelCase__     :int							      =				'''huggingface/label-files'''
					UpperCamelCase__     :List[Any]							      =				model_name[-3:]
					UpperCamelCase__     :Optional[Any]							      =				1000
					UpperCamelCase__     :str							      =				'''imagenet-1k-id2label.json'''
					UpperCamelCase__     :Union[str, Any]							      =				(1, 1000)

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

					# load image processor
					UpperCamelCase__     :List[str]							      =				PoolFormerImageProcessor(crop_pct=__a     )

					# Prepare image
					UpperCamelCase__     :List[Any]							      =				prepare_img()
					UpperCamelCase__     :Dict							      =				image_processor(images=__a				,      return_tensors='''pt'''     ).pixel_values

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

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

					# rename keys
					UpperCamelCase__     :str							      =				rename_keys(__a     )

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

					# Define image processor
					UpperCamelCase__     :Optional[Any]							      =				PoolFormerImageProcessor(crop_pct=__a     )
					UpperCamelCase__     :Union[str, Any]							      =				image_processor(images=prepare_img()				,      return_tensors='''pt'''     ).pixel_values

					# forward pass
					UpperCamelCase__     :Union[str, Any]							      =				model(__a     )
					UpperCamelCase__     :Optional[Any]							      =				outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										UpperCamelCase__     :Dict							      =				torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]     )
					elif size == "s24":
										UpperCamelCase__     :List[str]							      =				torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]     )
					elif size == "s36":
										UpperCamelCase__     :Dict							      =				torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]     )
					elif size == "m36":
										UpperCamelCase__     :Optional[int]							      =				torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]     )
					elif size == "m48":
										UpperCamelCase__     :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]				,      __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__":
		__snake_case							   =  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.'''
		)
		__snake_case							   =  parser.parse_args()
		convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_ = ""					,     UpperCamelCase_ = False							):

							'''simple docstring'''


							UpperCamelCase__     :dict[str, RadixNode]							      =				{}

							# A node will be a leaf if the tree contains its word
							UpperCamelCase__     :List[Any]							      =				is_leaf

							UpperCamelCase__     :Dict							      =				prefix




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				0
							for q, w in zip(self.prefix					,     UpperCamelCase_							):
												if q != w:
																	break

												x += 1

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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							for word in words:
												self.insert(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.prefix == word:
												UpperCamelCase__     :Tuple							      =				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:
												UpperCamelCase__     :Optional[Any]							      =				RadixNode(prefix=UpperCamelCase_					,     is_leaf=UpperCamelCase_							)

							else:
												UpperCamelCase__     :Optional[Any]							      =				self.nodes[word[0]]
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				incoming_node.match(
												    UpperCamelCase_							)

												# 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(UpperCamelCase_							)

												# 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:
																	UpperCamelCase__     :str							      =				remaining_prefix

																	UpperCamelCase__     :Optional[Any]							      =				self.nodes[matching_string[0]]
																	UpperCamelCase__     :Dict							      =				RadixNode(UpperCamelCase_					,     UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				aux_node

																	if remaining_word == "":
																						UpperCamelCase__     :List[Any]							      =				True
																	else:
																						self.nodes[matching_string[0]].insert(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.nodes.get(word[0]					,     UpperCamelCase_							)
							if not incoming_node:
												return False
							else:
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				incoming_node.match(
												    UpperCamelCase_							)
												# 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(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.nodes.get(word[0]					,     UpperCamelCase_							)
							if not incoming_node:
												return False
							else:
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				incoming_node.match(
												    UpperCamelCase_							)
												# 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(UpperCamelCase_							)
												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:
																																UpperCamelCase__     :Tuple							      =				list(self.nodes.values()							)[0]
																																UpperCamelCase__     :Union[str, Any]							      =				merging_node.is_leaf
																																self.prefix += merging_node.prefix
																																UpperCamelCase__     :Dict							      =				merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																						elif len(incoming_node.nodes							) > 1:
																											UpperCamelCase__     :List[str]							      =				False
																						# If there is 1 edge, we merge it with its child
																						else:
																											UpperCamelCase__     :Optional[int]							      =				list(incoming_node.nodes.values()							)[0]
																											UpperCamelCase__     :int							      =				merging_node.is_leaf
																											incoming_node.prefix += merging_node.prefix
																											UpperCamelCase__     :Optional[int]							      =				merging_node.nodes

																						return True




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_ = 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   (    )						->			bool:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				'''banana bananas bandana band apple all beast'''.split()
					UpperCamelCase__     :Tuple							      =				RadixNode()
					root.insert_many(__a     )

					assert all(root.find(__a     ) 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   (    )						->			None:



					'''simple docstring'''






					assert test_trie()


def     a   (    )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				RadixNode()
					UpperCamelCase__     :List[Any]							      =				'''banana bananas bandanas bandana band apple all beast'''.split()
					root.insert_many(__a     )

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


if __name__ == "__main__":
		main()