






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 :      Dict										=				logging.get_logger(__name__)







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

	snake_case__			:				Optional[int]       =  key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''					,  F'''block.{new_block_num}.{layer_num}.{new_name}'''       )
	return key







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







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

	return image







@torch.no_grad()
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str					,  snake_case_ :							Dict					,  snake_case_ :							List[str]       ):
	snake_case__			:				str       =  PoolFormerConfig()

	# set attributes based on model_name
	snake_case__			:				Optional[int]       =  "huggingface/label-files"
	snake_case__			:				Any       =  model_name[-3:]
	snake_case__			:				int       =  1000
	snake_case__			:				Dict       =  "imagenet-1k-id2label.json"
	snake_case__			:				Tuple       =  (1, 1000)

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

	# load image processor
	snake_case__			:				Any       =  PoolFormerImageProcessor(crop_pct=snake_case_       )

	# Prepare image
	snake_case__			:				Union[str, Any]       =  prepare_img()
	snake_case__			:				Dict       =  image_processor(images=snake_case_					,  return_tensors="pt"       ).pixel_values

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

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

	# rename keys
	snake_case__			:				Optional[Any]       =  rename_keys(snake_case_       )

	# create HuggingFace model and load state dict
	snake_case__			:				List[Any]       =  PoolFormerForImageClassification(snake_case_       )
	model.load_state_dict(snake_case_       )
	model.eval()

	# Define image processor
	snake_case__			:				Optional[int]       =  PoolFormerImageProcessor(crop_pct=snake_case_       )
	snake_case__			:				Optional[int]       =  image_processor(images=prepare_img()					,  return_tensors="pt"       ).pixel_values

	# forward pass
	snake_case__			:				Tuple       =  model(snake_case_       )
	snake_case__			:				Optional[Any]       =  outputs.logits

	# define expected logit slices for different models
	if size == "s12":
		snake_case__			:				Optional[int]       =  torch.tensor([-0.30_45, -0.67_58, -0.48_69]       )
	elif size == "s24":
		snake_case__			:				Any       =  torch.tensor([0.44_02, -0.13_74, -0.80_45]       )
	elif size == "s36":
		snake_case__			:				Union[str, Any]       =  torch.tensor([-0.60_80, -0.51_33, -0.58_98]       )
	elif size == "m36":
		snake_case__			:				str       =  torch.tensor([0.39_52, 0.22_63, -1.26_68]       )
	elif size == "m48":
		snake_case__			:				str       =  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]					,  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 :      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."""
	)
	__lowerCamelCase :      Optional[Any]										=				parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











from __future__ import annotations







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	if days_between_payments <= 0:
		raise ValueError("days_between_payments must be > 0"       )
	if daily_interest_rate < 0:
		raise ValueError("daily_interest_rate must be >= 0"       )
	if principal <= 0:
		raise ValueError("principal must be > 0"       )
	return principal * daily_interest_rate * days_between_payments







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float					,  ):
	if number_of_compounding_periods <= 0:
		raise ValueError("number_of_compounding_periods must be > 0"       )
	if nominal_annual_interest_rate_percentage < 0:
		raise ValueError("nominal_annual_interest_rate_percentage must be >= 0"       )
	if principal <= 0:
		raise ValueError("principal must be > 0"       )

	return principal * (
	    (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
	    - 1
	)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float					,  ):
	if number_of_years <= 0:
		raise ValueError("number_of_years must be > 0"       )
	if nominal_annual_percentage_rate < 0:
		raise ValueError("nominal_annual_percentage_rate must be >= 0"       )
	if principal <= 0:
		raise ValueError("principal must be > 0"       )

	return compound_interest(
	    snake_case_					,  nominal_annual_percentage_rate / 365					,  number_of_years * 365       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



