'''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_								=							logging.get_logger(__name__)

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Union[str, Any]:




       '''simple docstring'''






       _A=	original_name.split('.'   )[0]
       _A=	key.split('.'   )
       _A=	int(key_list[key_list.index(lowerCAmelCase_   ) - 2]   )
       _A=	int(key_list[key_list.index(lowerCAmelCase_   ) - 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				(    lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       _A=	OrderedDict()
       _A,			_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(lowerCAmelCase_       ,					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(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'mlp.fc1'       ,					'output.conv1'   )
              if "mlp.fc2" in key:
                     _A=	replace_key_with_offset(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'mlp.fc2'       ,					'output.conv2'   )
              if "norm1" in key:
                     _A=	replace_key_with_offset(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'norm1'       ,					'before_norm'   )
              if "norm2" in key:
                     _A=	replace_key_with_offset(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'norm2'       ,					'after_norm'   )
              if "layer_scale_1" in key:
                     _A=	replace_key_with_offset(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'layer_scale_1'       ,					'layer_scale_1'   )
              if "layer_scale_2" in key:
                     _A=	replace_key_with_offset(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'layer_scale_2'       ,					'layer_scale_2'   )
              if "head" in key:
                     _A=	key.replace('head'       ,					'classifier'   )
              _A=	value
       return new_state_dict

def 							UpperCamelCase				(    )		->						Optional[int]:




       '''simple docstring'''






       _A=	'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A=	Image.open(requests.get(lowerCAmelCase_       ,					stream=lowerCAmelCase_   ).raw   )

       return image

@torch.no_grad()
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[str]:




       '''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(lowerCAmelCase_       ,					lowerCAmelCase_       ,					repo_type='dataset'   )       ,					'r'   )   )
       _A=	{int(lowerCAmelCase_   ): 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=lowerCAmelCase_   )

       # Prepare image
       _A=	prepare_img()
       _A=	image_processor(images=lowerCAmelCase_       ,					return_tensors='pt'   ).pixel_values

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

       # load original state dict
       _A=	torch.load(lowerCAmelCase_       ,					map_location=torch.device('cpu'   )   )

       # rename keys
       _A=	rename_keys(lowerCAmelCase_   )

       # create HuggingFace model and load state dict
       _A=	PoolFormerForImageClassification(lowerCAmelCase_   )
       model.load_state_dict(lowerCAmelCase_   )
       model.eval()

       # Define image processor
       _A=	PoolFormerImageProcessor(crop_pct=lowerCAmelCase_   )
       _A=	image_processor(images=prepare_img()       ,					return_tensors='pt'   ).pixel_values

       # forward pass
       _A=	model(lowerCAmelCase_   )
       _A=	outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              _A=	torch.tensor([-0.3_045, -0.6_758, -0.4_869]   )
       elif size == "s24":
              _A=	torch.tensor([0.4_402, -0.1_374, -0.8_045]   )
       elif size == "s36":
              _A=	torch.tensor([-0.6_080, -0.5_133, -0.5_898]   )
       elif size == "m36":
              _A=	torch.tensor([0.3_952, 0.2_263, -1.2_668]   )
       elif size == "m48":
              _A=	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]       ,					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_								=							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 warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''nvidia/segformer-b0-finetuned-ade-512-512''': (
        '''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Tuple		="""segformer"""



     def __init__(					self				,      lowerCAmelCase__=3				,      lowerCAmelCase__=4				,      lowerCAmelCase__=[2, 2, 2, 2]				,      lowerCAmelCase__=[8, 4, 2, 1]				,      lowerCAmelCase__=[32, 64, 160, 256]				,      lowerCAmelCase__=[7, 3, 3, 3]				,      lowerCAmelCase__=[4, 2, 2, 2]				,      lowerCAmelCase__=[1, 2, 5, 8]				,      lowerCAmelCase__=[4, 4, 4, 4]				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=1E-6				,      lowerCAmelCase__=256				,      lowerCAmelCase__=255				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )

            if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
                   warnings.warn(
                       'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                       ' removed, as the behaviour will default to that of reshape_last_stage = True.'				,      lowerCAmelCase__				,      )

            _A=	num_channels
            _A=	num_encoder_blocks
            _A=	depths
            _A=	sr_ratios
            _A=	hidden_sizes
            _A=	patch_sizes
            _A=	strides
            _A=	mlp_ratios
            _A=	num_attention_heads
            _A=	hidden_act
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	classifier_dropout_prob
            _A=	initializer_range
            _A=	drop_path_rate
            _A=	layer_norm_eps
            _A=	decoder_hidden_size
            _A=	kwargs.get('reshape_last_stage'				,      lowerCAmelCase__       )
            _A=	semantic_loss_ignore_index





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		=version.parse("""1.11"""			)



     @property
     def 			a__  (					self       ):
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ]       )



     @property
     def 			a__  (					self       ):
            return 1E-4





     @property
     def 			a__  (					self       ):
            return 12