"""simple docstring"""






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase					: Tuple        =logging.get_logger(__name__)





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Dict													=			['pixel_values']

      def __init__(		self	, __lowerCAmelCase = True	, __lowerCAmelCase = None	, __lowerCAmelCase = PILImageResampling.BICUBIC	, __lowerCAmelCase = True	, __lowerCAmelCase = None	, __lowerCAmelCase = True	, __lowerCAmelCase = 1 / 255	, __lowerCAmelCase = True	, __lowerCAmelCase = IMAGENET_DEFAULT_MEAN	, __lowerCAmelCase = IMAGENET_DEFAULT_STD	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)
            lowercase														=			size if size is not None else {"""shortest_edge""": 224}
            lowercase														=			get_size_dict(__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)
            lowercase														=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
            lowercase														=			get_size_dict(__lowerCAmelCase	, param_name="""crop_size"""		)

            lowercase														=			do_resize
            lowercase														=			size
            lowercase														=			resample
            lowercase														=			do_center_crop
            lowercase														=			crop_size
            lowercase														=			do_rescale
            lowercase														=			rescale_factor
            lowercase														=			do_normalize
            lowercase														=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            lowercase														=			image_std if image_std is not None else IMAGENET_DEFAULT_STD

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = PILImageResampling.BICUBIC	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			get_size_dict(__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)
            # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
            if "shortest_edge" in size:
                  lowercase														=			int((256 / 224) * size["""shortest_edge"""]		)
                  lowercase														=			get_resize_output_image_size(__lowerCAmelCase	, size=__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)
                  lowercase														=			{"""height""": output_size[0], """width""": output_size[1]}
            if "height" not in size_dict or "width" not in size_dict:
                  raise ValueError(
                      f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'		)
            return resize(
                __lowerCAmelCase	, size=(size_dict["""height"""], size_dict["""width"""])	, resample=__lowerCAmelCase	, data_format=__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			get_size_dict(__lowerCAmelCase		)
            if "height" not in size or "width" not in size:
                  raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'		)
            return center_crop(__lowerCAmelCase	, size=(size["""height"""], size["""width"""])	, data_format=__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            return rescale(__lowerCAmelCase	, scale=__lowerCAmelCase	, data_format=__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            return normalize(__lowerCAmelCase	, mean=__lowerCAmelCase	, std=__lowerCAmelCase	, data_format=__lowerCAmelCase	, **__lowerCAmelCase		)







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = ChannelDimension.FIRST	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			do_resize if do_resize is not None else self.do_resize
            lowercase														=			resample if resample is not None else self.resample
            lowercase														=			do_center_crop if do_center_crop is not None else self.do_center_crop
            lowercase														=			do_rescale if do_rescale is not None else self.do_rescale
            lowercase														=			rescale_factor if rescale_factor is not None else self.rescale_factor
            lowercase														=			do_normalize if do_normalize is not None else self.do_normalize
            lowercase														=			image_mean if image_mean is not None else self.image_mean
            lowercase														=			image_std if image_std is not None else self.image_std

            lowercase														=			size if size is not None else self.size
            lowercase														=			get_size_dict(__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)
            lowercase														=			crop_size if crop_size is not None else self.crop_size
            lowercase														=			get_size_dict(__lowerCAmelCase	, param_name="""crop_size"""		)

            lowercase														=			make_list_of_images(__lowerCAmelCase		)

            if not valid_images(__lowerCAmelCase		):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray."""		)

            if do_resize and size is None:
                  raise ValueError("""Size must be specified if do_resize is True."""		)

            if do_center_crop and crop_size is None:
                  raise ValueError("""Crop size must be specified if do_center_crop is True."""		)

            if do_rescale and rescale_factor is None:
                  raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

            if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError("""Image mean and std must be specified if do_normalize is True."""		)

            # All transformations expect numpy arrays.
            lowercase														=			[to_numpy_array(__lowerCAmelCase		) for image in images]

            if do_resize:
                  lowercase														=			[self.resize(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		) for image in images]

            if do_center_crop:
                  lowercase														=			[self.center_crop(__lowerCAmelCase	, __lowerCAmelCase		) for image in images]

            if do_rescale:
                  lowercase														=			[self.rescale(__lowerCAmelCase	, __lowerCAmelCase		) for image in images]

            if do_normalize:
                  lowercase														=			[self.normalize(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		) for image in images]

            lowercase														=			[to_channel_dimension_format(__lowerCAmelCase	, __lowerCAmelCase		) for image in images]

            lowercase														=			{"""pixel_values""": images}
            return BatchFeature(data=__lowerCAmelCase	, tensor_type=__lowerCAmelCase		)




"""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()
__lowerCAmelCase					: Optional[Any]        =logging.get_logger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Optional[int]	)				->					Any:
      '''simple docstring'''


      lowercase														=			original_name.split("""."""	)[0]
      lowercase														=			key.split("""."""	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 2]	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 1]	)
      lowercase														=			orig_block_num - offset

      lowercase														=			key.replace(f'{orig_block_num}.{layer_num}.{original_name}'      ,	f'block.{new_block_num}.{layer_num}.{new_name}'	)
      return key


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Union[str, Any]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


      lowercase														=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowercase														=			Image.open(requests.get(lowerCAmelCase__      ,	stream=lowerCAmelCase__	).raw	)

      return image


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			PoolFormerConfig()

      # set attributes based on model_name
      lowercase														=			"""huggingface/label-files"""
      lowercase														=			model_name[-3:]
      lowercase														=			1_0_0_0
      lowercase														=			"""imagenet-1k-id2label.json"""
      lowercase														=			(1, 1_0_0_0)

      # set config attributes
      lowercase														=			json.load(open(hf_hub_download(lowerCAmelCase__      ,	lowerCAmelCase__      ,	repo_type="""dataset"""	)      ,	"""r"""	)	)
      lowercase														=			{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
      lowercase														=			idalabel
      lowercase														=			{v: k for k, v in idalabel.items()}
      if size == "s12":
            lowercase														=			[2, 2, 6, 2]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s24":
            lowercase														=			[4, 4, 1_2, 4]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.9
      elif size == "m36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      elif size == "m48":
            lowercase														=			[8, 8, 2_4, 8]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      else:
            raise ValueError(f'Size {size} not supported'	)

      # load image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)

      # Prepare image
      lowercase														=			prepare_img()
      lowercase														=			image_processor(images=lowerCAmelCase__      ,	return_tensors="""pt"""	).pixel_values

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

      # load original state dict
      lowercase														=			torch.load(lowerCAmelCase__      ,	map_location=torch.device("""cpu"""	)	)

      # rename keys
      lowercase														=			rename_keys(lowerCAmelCase__	)

      # create HuggingFace model and load state dict
      lowercase														=			PoolFormerForImageClassification(lowerCAmelCase__	)
      model.load_state_dict(lowerCAmelCase__	)
      model.eval()

      # Define image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)
      lowercase														=			image_processor(images=prepare_img()      ,	return_tensors="""pt"""	).pixel_values

      # forward pass
      lowercase														=			model(lowerCAmelCase__	)
      lowercase														=			outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            lowercase														=			torch.tensor([-0.3_045, -0.6_758, -0.4_869]	)
      elif size == "s24":
            lowercase														=			torch.tensor([0.4_402, -0.1_374, -0.8_045]	)
      elif size == "s36":
            lowercase														=			torch.tensor([-0.6_080, -0.5_133, -0.5_898]	)
      elif size == "m36":
            lowercase														=			torch.tensor([0.3_952, 0.2_263, -1.2_668]	)
      elif size == "m48":
            lowercase														=			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__":
       __lowerCAmelCase					: List[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."""
       )
       __lowerCAmelCase					: int        =parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



