

import doctest
from collections import deque

import numpy as np






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[Any]      )	->							None:
       SCREAMING_SNAKE_CASE__					=					[2, 1, 2, -1]
       SCREAMING_SNAKE_CASE__					=					[1, 2, 3, 4]







  def    lowercase_	(							self		:	List[str]      )	->							list[float]:
       SCREAMING_SNAKE_CASE__					=					len(self.first_signal      )
       SCREAMING_SNAKE_CASE__					=					len(self.second_signal      )

       SCREAMING_SNAKE_CASE__					=					max(__lowerCamelCase			,     __lowerCamelCase      )

       # create a zero matrix of max_length x max_length
       SCREAMING_SNAKE_CASE__					=					[[0] * max_length for i in range(__lowerCamelCase      )]

       # fills the smaller signal with zeros to make both signals of same length
       if length_first_signal < length_second_signal:
            self.first_signal += [0] * (max_length - length_first_signal)
       elif length_first_signal > length_second_signal:
            self.second_signal += [0] * (max_length - length_second_signal)
       for i in range(__lowerCamelCase      ):
            SCREAMING_SNAKE_CASE__					=					deque(self.second_signal      )
            rotated_signal.rotate(__lowerCamelCase      )
            for j, item in enumerate(__lowerCamelCase      ):
                 matrix[i][j] += item

        # multiply the matrix with the first signal
       SCREAMING_SNAKE_CASE__					=					np.matmul(np.transpose(__lowerCamelCase      )			,     np.transpose(self.first_signal      )      )

       # rounding-off to two decimal places
       return [round(__lowerCamelCase			,     2      ) for i in final_signal]


if __name__ == "__main__":
       doctest.testmod()





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()
_SCREAMING_SNAKE_CASE  :						Dict         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					original_name.split('''.'''       )[0]
     SCREAMING_SNAKE_CASE__					=					key.split('''.'''       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 2]       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 1]       )
     SCREAMING_SNAKE_CASE__					=					orig_block_num - offset

     SCREAMING_SNAKE_CASE__					=					key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''   ,	F'''block.{new_block_num}.{layer_num}.{new_name}'''       )
     return key
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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





     '''simple docstring'''




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

     return image
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					PoolFormerConfig()

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

     # set config attributes
     SCREAMING_SNAKE_CASE__					=					json.load(open(hf_hub_download(_A   ,	_A   ,	repo_type='''dataset'''       )   ,	'''r'''       )       )
     SCREAMING_SNAKE_CASE__					=					{int(_A       ): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__					=					idalabel
     SCREAMING_SNAKE_CASE__					=					{v: k for k, v in idalabel.items()}
     if size == "s12":
          SCREAMING_SNAKE_CASE__					=					[2, 2, 6, 2]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s24":
          SCREAMING_SNAKE_CASE__					=					[4, 4, 12, 4]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "m36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     elif size == "m48":
          SCREAMING_SNAKE_CASE__					=					[8, 8, 24, 8]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # load image processor
     SCREAMING_SNAKE_CASE__					=					PoolFormerImageProcessor(crop_pct=_A       )

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

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

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

     # rename keys
     SCREAMING_SNAKE_CASE__					=					rename_keys(_A       )

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

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

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

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


