
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_		:     List[Any]        =     logging.get_logger(__name__)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :int ,					UpperCAmelCase__ :Tuple ,					UpperCAmelCase__ :List[str]					):






 '''simple docstring'''

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

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any					):






 '''simple docstring'''

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


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

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

 return image


@torch.no_grad()
def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :Any ,					UpperCAmelCase__ :Dict					):






 '''simple docstring'''

 a						=		PoolFormerConfig()

 # set attributes based on model_name
 a						=		"""huggingface/label-files"""
 a						=		model_name[-3:]
 a						=		10_00
 a						=		"""imagenet-1k-id2label.json"""
 a						=		(1, 10_00)

 # set config attributes
 a						=		json.load(open(hf_hub_download(UpperCAmelCase__ ,					UpperCAmelCase__ ,					repo_type="dataset"					) ,					"r"					)					)
 a						=		{int(UpperCAmelCase__					): v for k, v in idalabel.items()}
 a						=		idalabel
 a						=		{v: k for k, v in idalabel.items()}
 if size == "s12":
  a						=		[2, 2, 6, 2]
  a						=		[64, 1_28, 3_20, 5_12]
  a						=		4.0
  a						=		0.9
 elif size == "s24":
  a						=		[4, 4, 12, 4]
  a						=		[64, 1_28, 3_20, 5_12]
  a						=		4.0
  a						=		0.9
 elif size == "s36":
  a						=		[6, 6, 18, 6]
  a						=		[64, 1_28, 3_20, 5_12]
  a						=		4.0
  a						=		1E-6
  a						=		0.9
 elif size == "m36":
  a						=		[6, 6, 18, 6]
  a						=		[96, 1_92, 3_84, 7_68]
  a						=		4.0
  a						=		1E-6
  a						=		0.95
 elif size == "m48":
  a						=		[8, 8, 24, 8]
  a						=		[96, 1_92, 3_84, 7_68]
  a						=		4.0
  a						=		1E-6
  a						=		0.95
 else:
  raise ValueError(F"""Size {size} not supported"""					)

 # load image processor
 a						=		PoolFormerImageProcessor(crop_pct=UpperCAmelCase__					)

 # Prepare image
 a						=		prepare_img()
 a						=		image_processor(images=UpperCAmelCase__ ,					return_tensors="pt"					).pixel_values

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

 # load original state dict
 a						=		torch.load(UpperCAmelCase__ ,					map_location=torch.device("cpu"					)					)

 # rename keys
 a						=		rename_keys(UpperCAmelCase__					)

 # create HuggingFace model and load state dict
 a						=		PoolFormerForImageClassification(UpperCAmelCase__					)
 model.load_state_dict(UpperCAmelCase__					)
 model.eval()

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

 # forward pass
 a						=		model(UpperCAmelCase__					)
 a						=		outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  a						=		torch.tensor([-0.3045, -0.6758, -0.4869]					)
 elif size == "s24":
  a						=		torch.tensor([0.4402, -0.1374, -0.8045]					)
 elif size == "s36":
  a						=		torch.tensor([-0.6080, -0.5133, -0.5898]					)
 elif size == "m36":
  a						=		torch.tensor([0.3952, 0.2263, -1.2668]					)
 elif size == "m48":
  a						=		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] ,					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_		:     str        =     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_		:     Tuple        =     parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel

