


"""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()
A     :   Optional[Any]			    =			logging.get_logger(__name__)


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			original_name.split("."      )[0]
			__lowerCAmelCase				    =			key.split("."      )
			__lowerCAmelCase				    =			int(key_list[key_list.index(_UpperCamelCase      ) - 2]      )
			__lowerCAmelCase				    =			int(key_list[key_list.index(_UpperCamelCase      ) - 1]      )
			__lowerCAmelCase				    =			orig_block_num - offset

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


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase				    =			Image.open(requests.get(_UpperCamelCase    , stream=_UpperCamelCase      ).raw      )

			return image


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			PoolFormerConfig()

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

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

			# load image processor
			__lowerCAmelCase				    =			PoolFormerImageProcessor(crop_pct=_UpperCamelCase      )

			# Prepare image
			__lowerCAmelCase				    =			prepare_img()
			__lowerCAmelCase				    =			image_processor(images=_UpperCamelCase    , return_tensors="pt"      ).pixel_values

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

			# load original state dict
			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location=torch.device("cpu"      )      )

			# rename keys
			__lowerCAmelCase				    =			rename_keys(_UpperCamelCase      )

			# create HuggingFace model and load state dict
			__lowerCAmelCase				    =			PoolFormerForImageClassification(_UpperCamelCase      )
			model.load_state_dict(_UpperCamelCase      )
			model.eval()

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

			# forward pass
			__lowerCAmelCase				    =			model(_UpperCamelCase      )
			__lowerCAmelCase				    =			outputs.logits

			# define expected logit slices for different models
			if size == "s12":
						__lowerCAmelCase				    =			torch.tensor([-0.30_45, -0.67_58, -0.48_69]      )
			elif size == "s24":
						__lowerCAmelCase				    =			torch.tensor([0.44_02, -0.13_74, -0.80_45]      )
			elif size == "s36":
						__lowerCAmelCase				    =			torch.tensor([-0.60_80, -0.51_33, -0.58_98]      )
			elif size == "m36":
						__lowerCAmelCase				    =			torch.tensor([0.39_52, 0.22_63, -1.26_68]      )
			elif size == "m48":
						__lowerCAmelCase				    =			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]    , _UpperCamelCase    , atol=1e-2      )

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


if __name__ == "__main__":
	A     :   List[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     :   int			    =			parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A     :   Union[str, Any]			    =			imread(R"digital_image_processing/image_data/lena_small.jpg")
A     :   Optional[Any]			    =			cvtColor(img, COLOR_BGR2GRAY)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			cn.convert_to_negative(_UpperCamelCase      )
			# assert negative_img array for at least one True
			assert negative_img.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			with Image.open("digital_image_processing/image_data/lena_small.jpg"      ) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(_UpperCamelCase    , 110      )      ).startswith(
						    "<PIL.Image.Image image mode=RGB size=100x100 at"      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			canny.gen_gaussian_kernel(9    , sigma=1.4      )
			# Assert ambiguous array
			assert resp.all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			imread("digital_image_processing/image_data/lena_small.jpg"    , 0      )
			# assert ambiguous array for all == True
			assert canny_img.all()
			__lowerCAmelCase				    =			canny.canny(_UpperCamelCase      )
			# assert canny array for at least one True
			assert canny_array.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert gg.gaussian_filter(_UpperCamelCase    , 5    , sigma=0.9      ).all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]      )
			__lowerCAmelCase				    =			conv.img_convolve(_UpperCamelCase    , _UpperCamelCase      ).astype(_UpperCamelCase      )
			assert res.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert med.median_filter(_UpperCamelCase    , 3      ).any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			sob.sobel_filter(_UpperCamelCase      )
			assert grad.any() and theta.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			sp.make_sepia(_UpperCamelCase    , 20      )
			assert sepia.all()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			bs.Burkes(imread(_UpperCamelCase    , 1      )    , 120      )
			burkes.process()
			assert burkes.output_img.any()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    , ):

			'''simple docstring'''




			__lowerCAmelCase				    =			rs.NearestNeighbour(imread(_UpperCamelCase    , 1      )    , 400    , 200      )
			nn.process()
			assert nn.output.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"digital_image_processing/image_data/lena.jpg"

			# Reading the image and converting it to grayscale.
			__lowerCAmelCase				    =			imread(_UpperCamelCase    , 0      )

			# Test for get_neighbors_pixel function() return not None
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			image[x_coordinate][y_coordinate]

			__lowerCAmelCase				    =			lbp.get_neighbors_pixel(
			    _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			__lowerCAmelCase				    =			np.zeros((image.shape[0], image.shape[1])      )

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0    , image.shape[0]      ):
						for j in range(0    , image.shape[1]      ):
									__lowerCAmelCase				    =			lbp.local_binary_value(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert lbp_image.any()




