





'''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'''

from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return DownloadCommand(args.model		,							args.cache_dir		,							args.force		,							args.trust_remote_code							)


class       UpperCAmelCase (						_lowercase  ):




		@staticmethod
		def 				UpperCAmelCase__   (A__      :	ArgumentParser				)					-> List[Any]:
						lowercase         = parser.add_parser("download"				)
						download_parser.add_argument(
						    "--cache-dir"							,			type=A__							,			default=A__							,			help="Path to location to store the models"				)
						download_parser.add_argument(
						    "--force"							,			action="store_true"							,			help="Force the model to be download even if already in cache-dir"				)
						download_parser.add_argument(
						    "--trust-remote-code"							,			action="store_true"							,			help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"							,			)
						download_parser.add_argument("model"							,			type=A__							,			help="Name of the model to download"				)
						download_parser.set_defaults(func=A__				)




		def __init__(self      :	Tuple							,			A__      :	str							,			A__      :	str							,			A__      :	bool							,			A__      :	bool				)					-> str:
						lowercase         = model
						lowercase         = cache
						lowercase         = force
						lowercase         = trust_remote_code





		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)
						AutoTokenizer.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)




