

'''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()
__lowerCamelCase : List[str]											=			logging.get_logger(__name__)







def 							UpperCAmelCase_    (	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 							UpperCAmelCase_    (	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 							UpperCAmelCase_    (	):

				"""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 							UpperCAmelCase_    (	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__":
			__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 : Tuple											=			parser.parse_args()
			convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Tuple							,			*A__      :	Union[str, Any]							,			**A__      :	Optional[Any]				)					-> None:
						warnings.warn(
						    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use CLIPImageProcessor instead."							,			A__							,			)
						super().__init__(*A__							,			**A__				)




