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   :				Optional[int]     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Any					) ->    Tuple:
					SCREAMING_SNAKE_CASE           =    original_name.split("."					)[0]
					SCREAMING_SNAKE_CASE           =    key.split("."					)
					SCREAMING_SNAKE_CASE           =    int(key_list[key_list.index(snake_case_					) - 2]					)
					SCREAMING_SNAKE_CASE           =    int(key_list[key_list.index(snake_case_					) - 1]					)
					SCREAMING_SNAKE_CASE           =    orig_block_num - offset

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							str					) ->    str:
					SCREAMING_SNAKE_CASE           =    OrderedDict()
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    0, 0
					for key, value in state_dict.items():
										if key.startswith("network"					):
															SCREAMING_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
															SCREAMING_SNAKE_CASE           =    key[: key.find("proj"					)]
															SCREAMING_SNAKE_CASE           =    key.replace(snake_case_ ,	F"patch_embeddings.{total_embed_found}."					)
															SCREAMING_SNAKE_CASE           =    key.replace("proj" ,	"projection"					)
															if key.endswith("bias"					):
																				total_embed_found += 1
										if "patch_embeddings" in key:
															SCREAMING_SNAKE_CASE           =    "poolformer.encoder." + key
										if "mlp.fc1" in key:
															SCREAMING_SNAKE_CASE           =    replace_key_with_offset(snake_case_ ,	snake_case_ ,	"mlp.fc1" ,	"output.conv1"					)
										if "mlp.fc2" in key:
															SCREAMING_SNAKE_CASE           =    replace_key_with_offset(snake_case_ ,	snake_case_ ,	"mlp.fc2" ,	"output.conv2"					)
										if "norm1" in key:
															SCREAMING_SNAKE_CASE           =    replace_key_with_offset(snake_case_ ,	snake_case_ ,	"norm1" ,	"before_norm"					)
										if "norm2" in key:
															SCREAMING_SNAKE_CASE           =    replace_key_with_offset(snake_case_ ,	snake_case_ ,	"norm2" ,	"after_norm"					)
										if "layer_scale_1" in key:
															SCREAMING_SNAKE_CASE           =    replace_key_with_offset(snake_case_ ,	snake_case_ ,	"layer_scale_1" ,	"layer_scale_1"					)
										if "layer_scale_2" in key:
															SCREAMING_SNAKE_CASE           =    replace_key_with_offset(snake_case_ ,	snake_case_ ,	"layer_scale_2" ,	"layer_scale_2"					)
										if "head" in key:
															SCREAMING_SNAKE_CASE           =    key.replace("head" ,	"classifier"					)
										SCREAMING_SNAKE_CASE           =    value
					return new_state_dict





def 		__lowerCamelCase							() ->    Any:
					SCREAMING_SNAKE_CASE           =    "http://images.cocodataset.org/val2017/000000039769.jpg"
					SCREAMING_SNAKE_CASE           =    Image.open(requests.get(snake_case_ ,	stream=snake_case_					).raw					)

					return image





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int]					) ->    Optional[int]:
					SCREAMING_SNAKE_CASE           =    PoolFormerConfig()

					# set attributes based on model_name
					SCREAMING_SNAKE_CASE           =    "huggingface/label-files"
					SCREAMING_SNAKE_CASE           =    model_name[-3:]
					SCREAMING_SNAKE_CASE           =    1_0_0_0
					SCREAMING_SNAKE_CASE           =    "imagenet-1k-id2label.json"
					SCREAMING_SNAKE_CASE           =    (1, 1_0_0_0)

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

					# load image processor
					SCREAMING_SNAKE_CASE           =    PoolFormerImageProcessor(crop_pct=snake_case_					)

					# Prepare image
					SCREAMING_SNAKE_CASE           =    prepare_img()
					SCREAMING_SNAKE_CASE           =    image_processor(images=snake_case_ ,	return_tensors="pt"					).pixel_values

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

					# load original state dict
					SCREAMING_SNAKE_CASE           =    torch.load(snake_case_ ,	map_location=torch.device("cpu"					)					)

					# rename keys
					SCREAMING_SNAKE_CASE           =    rename_keys(snake_case_					)

					# create HuggingFace model and load state dict
					SCREAMING_SNAKE_CASE           =    PoolFormerForImageClassification(snake_case_					)
					model.load_state_dict(snake_case_					)
					model.eval()

					# Define image processor
					SCREAMING_SNAKE_CASE           =    PoolFormerImageProcessor(crop_pct=snake_case_					)
					SCREAMING_SNAKE_CASE           =    image_processor(images=prepare_img() ,	return_tensors="pt"					).pixel_values

					# forward pass
					SCREAMING_SNAKE_CASE           =    model(snake_case_					)
					SCREAMING_SNAKE_CASE           =    outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										SCREAMING_SNAKE_CASE           =    torch.tensor([-0.3045, -0.6758, -0.4869]					)
					elif size == "s24":
										SCREAMING_SNAKE_CASE           =    torch.tensor([0.4402, -0.1374, -0.8045]					)
					elif size == "s36":
										SCREAMING_SNAKE_CASE           =    torch.tensor([-0.6080, -0.5133, -0.5898]					)
					elif size == "m36":
										SCREAMING_SNAKE_CASE           =    torch.tensor([0.3952, 0.2263, -1.2668]					)
					elif size == "m48":
										SCREAMING_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] ,	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(snake_case_					).mkdir(exist_ok=snake_case_					)
					model.save_pretrained(snake_case_					)
					print(F"Saving image processor to {pytorch_dump_folder_path}"					)
					image_processor.save_pretrained(snake_case_					)


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   :				Optional[int]     			=						parser.parse_args()
							convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowerCamelCase   :				Union[str, Any]     			=						TypeVar('''T''')


class   lowercase      (       Generic[T]       ):




							def __init__(  self				:   int		,							_UpperCamelCase				:   bool = True )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {}  # dictionary of lists
												SCREAMING_SNAKE_CASE           =    directed




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   T		,							_UpperCamelCase				:   T )   ->			GraphAdjacencyList[T]:



												'''simple docstring'''

												if not self.directed:  # For undirected graphs
																	# if both source vertex and destination vertex are both present in the
																	# adjacency list, add destination vertex to source vertex list of adjacent
																	# vertices and add source vertex to destination vertex list of adjacent
																	# vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination vertex
																	# to source vertex list of adjacent vertices, then create a new vertex with
																	# destination vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
																	# if only destination vertex is present in adjacency list, add source vertex
																	# to destination vertex list of adjacent vertices, then create a new vertex
																	# with source vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif destination_vertex in self.adj_list:
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and assign a list
																	# containing the destination vertex as it's first adjacent vertex also
																	# create a new vertex with destination vertex as key and assign a list
																	# containing the source vertex as it's first adjacent vertex.
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
												else:  # For directed graphs
																	# if both source vertex and destination vertex are present in adjacency
																	# list, add destination vertex to source vertex list of adjacent vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination
																	# vertex to source vertex list of adjacent vertices and create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    []
																	# if only destination vertex is present in adjacency list, create a new
																	# vertex with source vertex as key and assign a list containing destination
																	# vertex as first adjacent vertex
																	elif destination_vertex in self.adj_list:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and a list containing
																	# destination vertex as it's first adjacent vertex. Then create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    []

												return self





							def __repr__(  self				:   Union[str, Any] )   ->			str:



												'''simple docstring'''

												return pformat(self.adj_list )




