

'''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()
A_       						=							logging.get_logger(__name__)





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					original_name.split("." )[0]
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					key.split("." )
		SCREAMING_SNAKE_CASE:Tuple						      =					int(key_list[key_list.index(snake_case ) - 2] )
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					int(key_list[key_list.index(snake_case ) - 1] )
		SCREAMING_SNAKE_CASE:List[Any]						      =					orig_block_num - offset

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





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





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

		return image





@torch.no_grad()
def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:str						      =					PoolFormerConfig()

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

		# set config attributes
		SCREAMING_SNAKE_CASE:List[str]						      =					json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type="dataset" )      ,       "r" ) )
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					{int(snake_case ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE:str						      =					idalabel
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					{v: k for k, v in idalabel.items()}
		if size == "s12":
				SCREAMING_SNAKE_CASE:int						      =					[2, 2, 6, 2]
				SCREAMING_SNAKE_CASE:Any						      =					[64, 128, 320, 512]
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					4.0
				SCREAMING_SNAKE_CASE:List[Any]						      =					0.9
		elif size == "s24":
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[4, 4, 12, 4]
				SCREAMING_SNAKE_CASE:List[Any]						      =					[64, 128, 320, 512]
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					4.0
				SCREAMING_SNAKE_CASE:Optional[int]						      =					0.9
		elif size == "s36":
				SCREAMING_SNAKE_CASE:Any						      =					[6, 6, 18, 6]
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					[64, 128, 320, 512]
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					4.0
				SCREAMING_SNAKE_CASE:int						      =					1e-6
				SCREAMING_SNAKE_CASE:List[str]						      =					0.9
		elif size == "m36":
				SCREAMING_SNAKE_CASE:List[Any]						      =					[6, 6, 18, 6]
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					[96, 192, 384, 768]
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					4.0
				SCREAMING_SNAKE_CASE:Tuple						      =					1e-6
				SCREAMING_SNAKE_CASE:Tuple						      =					0.95
		elif size == "m48":
				SCREAMING_SNAKE_CASE:List[str]						      =					[8, 8, 24, 8]
				SCREAMING_SNAKE_CASE:int						      =					[96, 192, 384, 768]
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					4.0
				SCREAMING_SNAKE_CASE:Optional[int]						      =					1e-6
				SCREAMING_SNAKE_CASE:int						      =					0.95
		else:
				raise ValueError(F'''Size {size} not supported''' )

		# load image processor
		SCREAMING_SNAKE_CASE:Dict						      =					PoolFormerImageProcessor(crop_pct=snake_case )

		# Prepare image
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					prepare_img()
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_processor(images=snake_case      ,       return_tensors="pt" ).pixel_values

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

		# load original state dict
		SCREAMING_SNAKE_CASE:Tuple						      =					torch.load(snake_case      ,       map_location=torch.device("cpu" ) )

		# rename keys
		SCREAMING_SNAKE_CASE:Tuple						      =					rename_keys(snake_case )

		# create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE:int						      =					PoolFormerForImageClassification(snake_case )
		model.load_state_dict(snake_case )
		model.eval()

		# Define image processor
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					PoolFormerImageProcessor(crop_pct=snake_case )
		SCREAMING_SNAKE_CASE:str						      =					image_processor(images=prepare_img()      ,       return_tensors="pt" ).pixel_values

		# forward pass
		SCREAMING_SNAKE_CASE:Dict						      =					model(snake_case )
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs.logits

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









'''simple docstring'''

def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					word.split()

  def justify(snake_case      ,       snake_case      ,       snake_case ) -> str:
    SCREAMING_SNAKE_CASE:str						      =					max_width - width
    SCREAMING_SNAKE_CASE:Dict						      =					len(snake_case )
    if len(snake_case ) == 1:
      # if there is only word in line
      # just insert overall_spaces_count for the remainder of line
      return line[0] + " " * overall_spaces_count
    else:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					words_count - 1
      # num_spaces_between_words_list[i] : tells you to insert
      # num_spaces_between_words_list[i] spaces
      # after word on line[i]
      SCREAMING_SNAKE_CASE:List[Any]						      =					spaces_to_insert_between_words * [
          overall_spaces_count // spaces_to_insert_between_words
      ]
      SCREAMING_SNAKE_CASE:List[Any]						      =					(
          overall_spaces_count % spaces_to_insert_between_words
      )
      # distribute spaces via round robin to the left words
      for i in range(snake_case ):
        num_spaces_between_words_list[i] += 1
      SCREAMING_SNAKE_CASE:str						      =					[]
      for i in range(snake_case ):
        # add the word
        aligned_words_list.append(line[i] )
        # add the spaces to insert
        aligned_words_list.append(num_spaces_between_words_list[i] * " " )
      # just add the last word to the sentence
      aligned_words_list.append(line[-1] )
      # join the aligned words list to form a justified line
      return "".join(snake_case )

  SCREAMING_SNAKE_CASE:Any						      =					[]
  SCREAMING_SNAKE_CASE:list[str]						      =					[]
  SCREAMING_SNAKE_CASE:List[str]						      =					0
  for word in words:
    if width + len(snake_case ) + len(snake_case ) <= max_width:
      # keep adding words until we can fill out max_width
      # width = sum of length of all words (without overall_spaces_count)
      # len(word) = length of current word
      # len(line) = number of overall_spaces_count to insert between words
      line.append(snake_case )
      width += len(snake_case )
    else:
      # justify the line and add it to result
      answer.append(justify(snake_case      ,       snake_case      ,       snake_case ) )
      # reset new line and new width
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					[word], len(snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					max_width - width - len(snake_case )
  answer.append(" ".join(snake_case ) + (remaining_spaces + 1) * " " )
  return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()






