





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       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Optional[int]       ,       SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   List[str]						):





  """simple docstring"""



  a__	:					Optional[Any]										=original_name.split("."						)[0]
  a__	:					List[str]										=key.split("."						)
  a__	:					List[str]										=int(key_list[key_list.index(SCREAMING_SNAKE_CASE						) - 2]						)
  a__	:					List[str]										=int(key_list[key_list.index(SCREAMING_SNAKE_CASE						) - 1]						)
  a__	:					int										=orig_block_num - offset

  a__	:					List[str]										=key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''       ,       f'''block.{new_block_num}.{layer_num}.{new_name}'''						)
  return key
def      _A       (		SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  a__	:					List[str]										=OrderedDict()
  a__					,							a__	:					List[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__	:					Dict										=key[: key.find("proj"						)]
      a__	:					str										=key.replace(SCREAMING_SNAKE_CASE       ,       f'''patch_embeddings.{total_embed_found}.'''						)
      a__	:					Optional[Any]										=key.replace("proj"       ,       "projection"						)
      if key.endswith("bias"						):
        total_embed_found += 1
    if "patch_embeddings" in key:
      a__	:					int										="poolformer.encoder." + key
    if "mlp.fc1" in key:
      a__	:					str										=replace_key_with_offset(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       "mlp.fc1"       ,       "output.conv1"						)
    if "mlp.fc2" in key:
      a__	:					Tuple										=replace_key_with_offset(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       "mlp.fc2"       ,       "output.conv2"						)
    if "norm1" in key:
      a__	:					Any										=replace_key_with_offset(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       "norm1"       ,       "before_norm"						)
    if "norm2" in key:
      a__	:					Tuple										=replace_key_with_offset(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       "norm2"       ,       "after_norm"						)
    if "layer_scale_1" in key:
      a__	:					str										=replace_key_with_offset(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       "layer_scale_1"       ,       "layer_scale_1"						)
    if "layer_scale_2" in key:
      a__	:					str										=replace_key_with_offset(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       "layer_scale_2"       ,       "layer_scale_2"						)
    if "head" in key:
      a__	:					Optional[int]										=key.replace("head"       ,       "classifier"						)
    a__	:					Union[str, Any]										=value
  return new_state_dict
def      _A       (		):





  """simple docstring"""



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

  return image
@torch.no_grad()
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]       ,       SCREAMING_SNAKE_CASE     :   Any						):





  """simple docstring"""



  a__	:					Optional[int]										=PoolFormerConfig()

  # set attributes based on model_name
  a__	:					Tuple										="huggingface/label-files"
  a__	:					Dict										=model_name[-3:]
  a__	:					Optional[int]										=1_000
  a__	:					Optional[int]										="imagenet-1k-id2label.json"
  a__	:					int										=(1, 1_000)

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

  # load image processor
  a__	:					str										=PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE						)

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

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

  # load original state dict
  a__	:					Optional[int]										=torch.load(SCREAMING_SNAKE_CASE       ,       map_location=torch.device("cpu"						)						)

  # rename keys
  a__	:					Dict										=rename_keys(SCREAMING_SNAKE_CASE						)

  # create HuggingFace model and load state dict
  a__	:					Optional[int]										=PoolFormerForImageClassification(SCREAMING_SNAKE_CASE						)
  model.load_state_dict(SCREAMING_SNAKE_CASE						)
  model.eval()

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

  # forward pass
  a__	:					str										=model(SCREAMING_SNAKE_CASE						)
  a__	:					List[str]										=outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    a__	:					Any										=torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]						)
  elif size == "s24":
    a__	:					List[Any]										=torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]						)
  elif size == "s36":
    a__	:					Any										=torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]						)
  elif size == "m36":
    a__	:					Dict										=torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]						)
  elif size == "m48":
    a__	:					Optional[Any]										=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]       ,       SCREAMING_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(SCREAMING_SNAKE_CASE						).mkdir(exist_ok=SCREAMING_SNAKE_CASE						)
  model.save_pretrained(SCREAMING_SNAKE_CASE						)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     UpperCAmelCase	:	int		     =       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	:	Optional[Any]		     =       parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)













from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = "arrow"		,      **lowerCAmelCase__		,      )   ->						Optional[int]:



        '''simple docstring'''




        super().__init__(
            split=lowerCAmelCase__		,      features=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      keep_in_memory=lowerCAmelCase__		,      streaming=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        a__	:					int										=load_from_cache_file
        a__	:					Tuple										=file_format
        a__	:					List[Any]										=Spark(
            df=lowerCAmelCase__		,      features=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      working_dir=lowerCAmelCase__		,      **lowerCAmelCase__		,      )







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        if self.streaming:
          return self.builder.as_streaming_dataset(split=self.split							)
        a__	:					str										=None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
        self.builder.download_and_prepare(
            download_mode=lowerCAmelCase__		,      file_format=self._file_format		,      )
        return self.builder.as_dataset(split=self.split							)






