





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()
UpperCamelCase__           =			logging.get_logger(__name__)





def      _UpperCamelCase  (a__ :Optional[int] ,      a__ :List[Any] ,      a__ :int ,      a__ :List[str]							):






							"""simple docstring"""





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

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





def      _UpperCamelCase  (a__ :List[str]							):






							"""simple docstring"""





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





def      _UpperCamelCase  ():






							"""simple docstring"""





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

							return image





@torch.no_grad()
def      _UpperCamelCase  (a__ :List[Any] ,      a__ :List[Any] ,      a__ :List[str]							):






							"""simple docstring"""





							UpperCamelCase__					  =      PoolFormerConfig()

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

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

							# load image processor
							UpperCamelCase__					  =      PoolFormerImageProcessor(crop_pct=a__							)

							# Prepare image
							UpperCamelCase__					  =      prepare_img()
							UpperCamelCase__					  =      image_processor(images=a__ ,      return_tensors="""pt"""							).pixel_values

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

							# load original state dict
							UpperCamelCase__					  =      torch.load(a__ ,      map_location=torch.device("""cpu"""							)							)

							# rename keys
							UpperCamelCase__					  =      rename_keys(a__							)

							# create HuggingFace model and load state dict
							UpperCamelCase__					  =      PoolFormerForImageClassification(a__							)
							model.load_state_dict(a__							)
							model.eval()

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

							# forward pass
							UpperCamelCase__					  =      model(a__							)
							UpperCamelCase__					  =      outputs.logits

							# define expected logit slices for different models
							if size == "s12":
														UpperCamelCase__					  =      torch.tensor([-0.3045, -0.6758, -0.4869]							)
							elif size == "s24":
														UpperCamelCase__					  =      torch.tensor([0.4402, -0.1374, -0.8045]							)
							elif size == "s36":
														UpperCamelCase__					  =      torch.tensor([-0.6080, -0.5133, -0.5898]							)
							elif size == "m36":
														UpperCamelCase__					  =      torch.tensor([0.3952, 0.2263, -1.2668]							)
							elif size == "m48":
														UpperCamelCase__					  =      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] ,      a__ ,      atol=1e-2							)

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


if __name__ == "__main__":
					UpperCamelCase__           =			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."
					)
					UpperCamelCase__           =			parser.parse_args()
					convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a ,		unittest.TestCase							):
							snake_case  :   List[str]      =							VQModel
							snake_case  :   Dict      =							"""sample"""






							@property
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase=(32, 32)	):
														UpperCamelCase__					  =      4
														UpperCamelCase__					  =      3

														UpperCamelCase__					  =      floats_tensor((batch_size, num_channels) + sizes	).to(__lowerCAmelCase	)

														return {"sample": image}






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      {
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														UpperCamelCase__					  =      self.dummy_input
														return init_dict, inputs_dict






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	,			output_loading_info=__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)
														self.assertEqual(len(loading_info["""missing_keys"""]	)	,			0	)

														model.to(__lowerCAmelCase	)
														UpperCamelCase__					  =      model(**self.dummy_input	)

														assert image is not None, "Make sure output is not None"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	)
														model.to(__lowerCAmelCase	).eval()

														torch.manual_seed(0	)
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0	)

														UpperCamelCase__					  =      torch.randn(1	,			model.config.in_channels	,			model.config.sample_size	,			model.config.sample_size	)
														UpperCamelCase__					  =      image.to(__lowerCAmelCase	)
														with torch.no_grad():
																					UpperCamelCase__					  =      model(__lowerCAmelCase	).sample

														UpperCamelCase__					  =      output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														UpperCamelCase__					  =      torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]	)
														# fmt: on
														self.assertTrue(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-3	)	)




