




'''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()
lowercase__   :				Any      =   logging.get_logger(__name__)






def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			int			,							__snake_case  :			Union[str, Any]			,							__snake_case  :			int      )					->					List[str]:
     __A						:			Optional[int]								=	original_name.split('.'      )[0]
     __A						:			Dict								=	key.split('.'      )
     __A						:			List[Any]								=	int(key_list[key_list.index(__snake_case      ) - 2]      )
     __A						:			List[Any]								=	int(key_list[key_list.index(__snake_case      ) - 1]      )
     __A						:			int								=	orig_block_num - offset

     __A						:			Union[str, Any]								=	key.replace(f'{orig_block_num}.{layer_num}.{original_name}'			,							f'block.{new_block_num}.{layer_num}.{new_name}'      )
     return key






def 	_lowerCAmelCase      (  __snake_case  :			Any      )					->					int:
     __A						:			Dict								=	OrderedDict()
     __A						:			Optional[Any]								=	0, 0
     for key, value in state_dict.items():
          if key.startswith('network'      ):
               __A						:			Any								=	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						:			int								=	key[: key.find('proj'      )]
               __A						:			int								=	key.replace(__snake_case			,							f'patch_embeddings.{total_embed_found}.'      )
               __A						:			Tuple								=	key.replace('proj'			,							'projection'      )
               if key.endswith('bias'      ):
                    total_embed_found += 1
          if "patch_embeddings" in key:
               __A						:			Optional[Any]								=	'poolformer.encoder.' + key
          if "mlp.fc1" in key:
               __A						:			List[str]								=	replace_key_with_offset(__snake_case			,							__snake_case			,							'mlp.fc1'			,							'output.conv1'      )
          if "mlp.fc2" in key:
               __A						:			Dict								=	replace_key_with_offset(__snake_case			,							__snake_case			,							'mlp.fc2'			,							'output.conv2'      )
          if "norm1" in key:
               __A						:			List[Any]								=	replace_key_with_offset(__snake_case			,							__snake_case			,							'norm1'			,							'before_norm'      )
          if "norm2" in key:
               __A						:			List[Any]								=	replace_key_with_offset(__snake_case			,							__snake_case			,							'norm2'			,							'after_norm'      )
          if "layer_scale_1" in key:
               __A						:			Optional[int]								=	replace_key_with_offset(__snake_case			,							__snake_case			,							'layer_scale_1'			,							'layer_scale_1'      )
          if "layer_scale_2" in key:
               __A						:			Optional[Any]								=	replace_key_with_offset(__snake_case			,							__snake_case			,							'layer_scale_2'			,							'layer_scale_2'      )
          if "head" in key:
               __A						:			Dict								=	key.replace('head'			,							'classifier'      )
          __A						:			Optional[Any]								=	value
     return new_state_dict






def 	_lowerCAmelCase      (  )					->					str:
     __A						:			str								=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A						:			Any								=	Image.open(requests.get(__snake_case			,							stream=__snake_case      ).raw      )

     return image






@torch.no_grad()
def 	_lowerCAmelCase      (  __snake_case  :			Optional[Any]			,							__snake_case  :			List[Any]			,							__snake_case  :			Any      )					->					List[Any]:
     __A						:			Dict								=	PoolFormerConfig()

     # set attributes based on model_name
     __A						:			Dict								=	'huggingface/label-files'
     __A						:			List[str]								=	model_name[-3:]
     __A						:			Optional[int]								=	10_00
     __A						:			Tuple								=	'imagenet-1k-id2label.json'
     __A						:			Optional[Any]								=	(1, 10_00)

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

     # load image processor
     __A						:			List[Any]								=	PoolFormerImageProcessor(crop_pct=__snake_case      )

     # Prepare image
     __A						:			List[str]								=	prepare_img()
     __A						:			Union[str, Any]								=	image_processor(images=__snake_case			,							return_tensors='pt'      ).pixel_values

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

     # load original state dict
     __A						:			str								=	torch.load(__snake_case			,							map_location=torch.device('cpu'      )      )

     # rename keys
     __A						:			Dict								=	rename_keys(__snake_case      )

     # create HuggingFace model and load state dict
     __A						:			Dict								=	PoolFormerForImageClassification(__snake_case      )
     model.load_state_dict(__snake_case      )
     model.eval()

     # Define image processor
     __A						:			Union[str, Any]								=	PoolFormerImageProcessor(crop_pct=__snake_case      )
     __A						:			List[str]								=	image_processor(images=prepare_img()			,							return_tensors='pt'      ).pixel_values

     # forward pass
     __A						:			Optional[int]								=	model(__snake_case      )
     __A						:			Optional[int]								=	outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          __A						:			str								=	torch.tensor([-0.3_045, -0.6_758, -0.4_869]      )
     elif size == "s24":
          __A						:			int								=	torch.tensor([0.4_402, -0.1_374, -0.8_045]      )
     elif size == "s36":
          __A						:			Dict								=	torch.tensor([-0.6_080, -0.5_133, -0.5_898]      )
     elif size == "m36":
          __A						:			Tuple								=	torch.tensor([0.3_952, 0.2_263, -1.2_668]      )
     elif size == "m48":
          __A						:			Dict								=	torch.tensor([0.1_167, -0.0_656, -0.3_423]      )
     else:
          raise ValueError(f'Size {size} not supported'      )

     # verify logits
     assert logits.shape == expected_shape
     assert torch.allclose(logits[0, :3]			,							__snake_case			,							atol=1e-2      )

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


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






'''simple docstring'''




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


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )