
'''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()
UpperCAmelCase_							: str =							logging.get_logger(__name__)







def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     Optional[Any]				):



      """simple docstring"""




      _lowerCamelCase					: Tuple		    = original_name.split("."				)[0]
      _lowerCamelCase					: List[Any]		    = key.split("."				)
      _lowerCamelCase					: Dict		    = int(key_list[key_list.index(_lowerCAmelCase				) - 2]				)
      _lowerCamelCase					: Union[str, Any]		    = int(key_list[key_list.index(_lowerCAmelCase				) - 1]				)
      _lowerCamelCase					: Optional[int]		    = orig_block_num - offset

      _lowerCamelCase					: Dict		    = key.replace(F'{orig_block_num}.{layer_num}.{original_name}'					,						F'block.{new_block_num}.{layer_num}.{new_name}'				)
      return key







def 		A_							(						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




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







def 		A_							(						):



      """simple docstring"""




      _lowerCamelCase					: Optional[int]		    = "http://images.cocodataset.org/val2017/000000039769.jpg"
      _lowerCamelCase					: List[str]		    = Image.open(requests.get(_lowerCAmelCase					,						stream=_lowerCAmelCase				).raw				)

      return image







@torch.no_grad()
def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = PoolFormerConfig()

      # set attributes based on model_name
      _lowerCamelCase					: Optional[int]		    = "huggingface/label-files"
      _lowerCamelCase					: Optional[Any]		    = model_name[-3:]
      _lowerCamelCase					: str		    = 1000
      _lowerCamelCase					: Union[str, Any]		    = "imagenet-1k-id2label.json"
      _lowerCamelCase					: Optional[Any]		    = (1, 1000)

      # set config attributes
      _lowerCamelCase					: Union[str, Any]		    = json.load(open(hf_hub_download(_lowerCAmelCase					,						_lowerCAmelCase					,						repo_type="dataset"				)					,						"r"				)				)
      _lowerCamelCase					: List[str]		    = {int(_lowerCAmelCase				): v for k, v in idalabel.items()}
      _lowerCamelCase					: Tuple		    = idalabel
      _lowerCamelCase					: int		    = {v: k for k, v in idalabel.items()}
      if size == "s12":
            _lowerCamelCase					: List[Any]		    = [2, 2, 6, 2]
            _lowerCamelCase					: Optional[int]		    = [64, 128, 320, 512]
            _lowerCamelCase					: Any		    = 4.0
            _lowerCamelCase					: int		    = 0.9
      elif size == "s24":
            _lowerCamelCase					: List[str]		    = [4, 4, 12, 4]
            _lowerCamelCase					: Tuple		    = [64, 128, 320, 512]
            _lowerCamelCase					: Union[str, Any]		    = 4.0
            _lowerCamelCase					: Dict		    = 0.9
      elif size == "s36":
            _lowerCamelCase					: List[str]		    = [6, 6, 18, 6]
            _lowerCamelCase					: int		    = [64, 128, 320, 512]
            _lowerCamelCase					: Optional[Any]		    = 4.0
            _lowerCamelCase					: Optional[int]		    = 1E-6
            _lowerCamelCase					: Union[str, Any]		    = 0.9
      elif size == "m36":
            _lowerCamelCase					: Optional[Any]		    = [6, 6, 18, 6]
            _lowerCamelCase					: Dict		    = [96, 192, 384, 768]
            _lowerCamelCase					: Optional[Any]		    = 4.0
            _lowerCamelCase					: Union[str, Any]		    = 1E-6
            _lowerCamelCase					: Tuple		    = 0.9_5
      elif size == "m48":
            _lowerCamelCase					: Optional[Any]		    = [8, 8, 24, 8]
            _lowerCamelCase					: Optional[Any]		    = [96, 192, 384, 768]
            _lowerCamelCase					: List[str]		    = 4.0
            _lowerCamelCase					: Union[str, Any]		    = 1E-6
            _lowerCamelCase					: str		    = 0.9_5
      else:
            raise ValueError(F'Size {size} not supported'				)

      # load image processor
      _lowerCamelCase					: Union[str, Any]		    = PoolFormerImageProcessor(crop_pct=_lowerCAmelCase				)

      # Prepare image
      _lowerCamelCase					: Dict		    = prepare_img()
      _lowerCamelCase					: List[str]		    = image_processor(images=_lowerCAmelCase					,						return_tensors="pt"				).pixel_values

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

      # load original state dict
      _lowerCamelCase					: Any		    = torch.load(_lowerCAmelCase					,						map_location=torch.device("cpu"				)				)

      # rename keys
      _lowerCamelCase					: Dict		    = rename_keys(_lowerCAmelCase				)

      # create HuggingFace model and load state dict
      _lowerCamelCase					: Optional[Any]		    = PoolFormerForImageClassification(_lowerCAmelCase				)
      model.load_state_dict(_lowerCAmelCase				)
      model.eval()

      # Define image processor
      _lowerCamelCase					: Optional[Any]		    = PoolFormerImageProcessor(crop_pct=_lowerCAmelCase				)
      _lowerCamelCase					: Optional[int]		    = image_processor(images=prepare_img()					,						return_tensors="pt"				).pixel_values

      # forward pass
      _lowerCamelCase					: Union[str, Any]		    = model(_lowerCAmelCase				)
      _lowerCamelCase					: Any		    = outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            _lowerCamelCase					: Tuple		    = torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]				)
      elif size == "s24":
            _lowerCamelCase					: Tuple		    = torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]				)
      elif size == "s36":
            _lowerCamelCase					: Tuple		    = torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]				)
      elif size == "m36":
            _lowerCamelCase					: Dict		    = torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]				)
      elif size == "m48":
            _lowerCamelCase					: str		    = torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]				)
      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__":
 UpperCAmelCase_							: Optional[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.'
 )
 UpperCAmelCase_							: Tuple =							parser.parse_args()
 convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase__      			=						{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase__      			=						logging.get_logger(__name__)




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """mask2former"""
						snake_case__        =      ["""swin"""]
						snake_case__        =      {"""hidden_size""": """hidden_dim"""}



						def __init__(self:					Any ,					__UpperCAmelCase:					Optional[Dict] = None ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 1024 ,					__UpperCAmelCase:					str = "relu" ,					__UpperCAmelCase:					int = 6 ,					__UpperCAmelCase:					int = 10 ,					__UpperCAmelCase:					int = 8 ,					__UpperCAmelCase:					float = 0.0 ,					__UpperCAmelCase:					int = 2048 ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					int = 4 ,					__UpperCAmelCase:					int = 255 ,					__UpperCAmelCase:					int = 100 ,					__UpperCAmelCase:					float = 0.1 ,					__UpperCAmelCase:					float = 2.0 ,					__UpperCAmelCase:					float = 5.0 ,					__UpperCAmelCase:					float = 5.0 ,					__UpperCAmelCase:					int = 12544 ,					__UpperCAmelCase:					float = 3.0 ,					__UpperCAmelCase:					float = 0.75 ,					__UpperCAmelCase:					float = 0.02 ,					__UpperCAmelCase:					float = 1.0 ,					__UpperCAmelCase:					bool = True ,					__UpperCAmelCase:					List[int] = [4, 8, 16, 32] ,					__UpperCAmelCase:					bool = None ,					**__UpperCAmelCase:					Any ,					)    ->     int:




											'''simple docstring'''



											if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."   )
																__a					:						str      =					CONFIG_MAPPING["swin"](
																    image_size=224 ,					in_channels=3 ,					patch_size=4 ,					embed_dim=96 ,					depths=[2, 2, 18, 2] ,					num_heads=[3, 6, 12, 24] ,					window_size=7 ,					drop_path_rate=0.3 ,					use_absolute_embeddings=__UpperCAmelCase ,					out_features=["stage1", "stage2", "stage3", "stage4"] ,					)

											if isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ):
																__a					:						List[Any]      =					backbone_config.pop("model_type"   )
																__a					:						List[str]      =					CONFIG_MAPPING[backbone_model_type]
																__a					:						Dict      =					config_class.from_dict(__UpperCAmelCase   )

											# verify that the backbone is supported
											if backbone_config.model_type not in self.backbones_supported:
																logger.warning_once(
																    f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
																    f'Supported model types: {",".join(self.backbones_supported   )}'   )

											__a					:						Tuple      =					backbone_config
											__a					:						Any      =					feature_size
											__a					:						int      =					mask_feature_size
											__a					:						Optional[int]      =					hidden_dim
											__a					:						int      =					encoder_feedforward_dim
											__a					:						List[str]      =					activation_function
											__a					:						Optional[Any]      =					encoder_layers
											__a					:						List[Any]      =					decoder_layers
											__a					:						Union[str, Any]      =					num_attention_heads
											__a					:						Optional[Any]      =					dropout
											__a					:						Tuple      =					dim_feedforward
											__a					:						List[str]      =					pre_norm
											__a					:						int      =					enforce_input_projection
											__a					:						List[str]      =					common_stride
											__a					:						Any      =					ignore_value
											__a					:						Union[str, Any]      =					num_queries
											__a					:						int      =					no_object_weight
											__a					:						Tuple      =					class_weight
											__a					:						int      =					mask_weight
											__a					:						Union[str, Any]      =					dice_weight
											__a					:						int      =					train_num_points
											__a					:						Any      =					oversample_ratio
											__a					:						Any      =					importance_sample_ratio
											__a					:						List[str]      =					init_std
											__a					:						List[str]      =					init_xavier_std
											__a					:						Dict      =					use_auxiliary_loss
											__a					:						List[Any]      =					feature_strides
											__a					:						Tuple      =					output_auxiliary_logits
											__a					:						List[Any]      =					decoder_layers

											super().__init__(**__UpperCAmelCase   )



						@classmethod
						def        UpperCAmelCase__					(cls:					Tuple ,					__UpperCAmelCase:					PretrainedConfig ,					**__UpperCAmelCase:					List[str]   )    ->     Any:




											'''simple docstring'''



											return cls(
											    backbone_config=__UpperCAmelCase ,					**__UpperCAmelCase ,					)



						def        UpperCAmelCase__					(self:					str   )    ->     Dict[str, any]:




											'''simple docstring'''



											__a					:						Tuple      =					copy.deepcopy(self.__dict__   )
											__a					:						List[str]      =					self.backbone_config.to_dict()
											__a					:						Any      =					self.__class__.model_type
											return output





