

"""simple docstring"""


import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
			pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  """align_text_model"""

				def __init__(     self							:    Optional[Any]					,  UpperCamelCase__							:    Optional[int]=3_0_5_2_2					,  UpperCamelCase__							:    List[str]=7_6_8					,  UpperCamelCase__							:    Optional[int]=1_2					,  UpperCamelCase__							:    List[str]=1_2					,  UpperCamelCase__							:    List[Any]=3_0_7_2					,  UpperCamelCase__							:    str="gelu"					,  UpperCamelCase__							:    Tuple=0.1					,  UpperCamelCase__							:    Tuple=0.1					,  UpperCamelCase__							:    List[Any]=5_1_2					,  UpperCamelCase__							:    Tuple=2					,  UpperCamelCase__							:    Union[str, Any]=0.02					,  UpperCamelCase__							:    List[Any]=1e-12					,  UpperCamelCase__							:    str=0					,  UpperCamelCase__							:    List[Any]="absolute"					,  UpperCamelCase__							:    Any=True					,  **UpperCamelCase__							:    Optional[int]					,  )->       str:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:	int   				=      vocab_size
						__lowerCAmelCase:	int   				=      hidden_size
						__lowerCAmelCase:	Optional[Any]   				=      num_hidden_layers
						__lowerCAmelCase:	Any   				=      num_attention_heads
						__lowerCAmelCase:	Optional[int]   				=      hidden_act
						__lowerCAmelCase:	Optional[int]   				=      intermediate_size
						__lowerCAmelCase:	int   				=      hidden_dropout_prob
						__lowerCAmelCase:	Optional[int]   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	int   				=      max_position_embeddings
						__lowerCAmelCase:	Union[str, Any]   				=      type_vocab_size
						__lowerCAmelCase:	Dict   				=      initializer_range
						__lowerCAmelCase:	int   				=      layer_norm_eps
						__lowerCAmelCase:	Optional[Any]   				=      position_embedding_type
						__lowerCAmelCase:	Tuple   				=      use_cache
						__lowerCAmelCase:	Tuple   				=      pad_token_id




				@classmethod
				def 							lowercase_					(     cls							:    Tuple					,  UpperCamelCase__							:    Union[str, os.PathLike]					,  **UpperCamelCase__							:    List[Any])->       "PretrainedConfig":



						'''simple docstring'''


						cls._set_token_in_kwargs(UpperCamelCase__)

						__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      cls.get_config_dict(UpperCamelCase__					,  **UpperCamelCase__)

						# get the text config dict if we are loading from AlignConfig
						if config_dict.get("model_type") == "align":
								__lowerCAmelCase:	Dict   				=      config_dict["text_config"]

						if "model_type" in config_dict and hasattr(cls					,  "model_type") and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
								    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors.")

						return cls.from_dict(UpperCamelCase__					,  **UpperCamelCase__)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  """align_vision_model"""

				def __init__(     self							:    List[str]					,  UpperCamelCase__							:    int = 3					,  UpperCamelCase__							:    int = 6_0_0					,  UpperCamelCase__							:    float = 2.0					,  UpperCamelCase__							:    float = 3.1					,  UpperCamelCase__							:    int = 8					,  UpperCamelCase__							:    List[int] = [3, 3, 5, 3, 5, 5, 3]					,  UpperCamelCase__							:    List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]					,  UpperCamelCase__							:    List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]					,  UpperCamelCase__							:    List[int] = []					,  UpperCamelCase__							:    List[int] = [1, 2, 2, 2, 1, 2, 1]					,  UpperCamelCase__							:    List[int] = [1, 2, 2, 3, 3, 4, 1]					,  UpperCamelCase__							:    List[int] = [1, 6, 6, 6, 6, 6, 6]					,  UpperCamelCase__							:    float = 0.25					,  UpperCamelCase__							:    str = "swish"					,  UpperCamelCase__							:    int = 2_5_6_0					,  UpperCamelCase__							:    str = "mean"					,  UpperCamelCase__							:    float = 0.02					,  UpperCamelCase__							:    float = 0.001					,  UpperCamelCase__							:    float = 0.99					,  UpperCamelCase__							:    float = 0.2					,  **UpperCamelCase__							:    Tuple					,  )->       List[str]:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:	int   				=      num_channels
						__lowerCAmelCase:	Any   				=      image_size
						__lowerCAmelCase:	Any   				=      width_coefficient
						__lowerCAmelCase:	List[Any]   				=      depth_coefficient
						__lowerCAmelCase:	Optional[int]   				=      depth_divisor
						__lowerCAmelCase:	List[Any]   				=      kernel_sizes
						__lowerCAmelCase:	Dict   				=      in_channels
						__lowerCAmelCase:	Optional[int]   				=      out_channels
						__lowerCAmelCase:	Optional[int]   				=      depthwise_padding
						__lowerCAmelCase:	Optional[int]   				=      strides
						__lowerCAmelCase:	Optional[int]   				=      num_block_repeats
						__lowerCAmelCase:	str   				=      expand_ratios
						__lowerCAmelCase:	Union[str, Any]   				=      squeeze_expansion_ratio
						__lowerCAmelCase:	Optional[Any]   				=      hidden_act
						__lowerCAmelCase:	Union[str, Any]   				=      hidden_dim
						__lowerCAmelCase:	List[str]   				=      pooling_type
						__lowerCAmelCase:	int   				=      initializer_range
						__lowerCAmelCase:	Any   				=      batch_norm_eps
						__lowerCAmelCase:	int   				=      batch_norm_momentum
						__lowerCAmelCase:	Union[str, Any]   				=      drop_connect_rate
						__lowerCAmelCase:	int   				=      sum(UpperCamelCase__) * 4




				@classmethod
				def 							lowercase_					(     cls							:    str					,  UpperCamelCase__							:    Union[str, os.PathLike]					,  **UpperCamelCase__							:    List[Any])->       "PretrainedConfig":



						'''simple docstring'''


						cls._set_token_in_kwargs(UpperCamelCase__)

						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      cls.get_config_dict(UpperCamelCase__					,  **UpperCamelCase__)

						# get the vision config dict if we are loading from AlignConfig
						if config_dict.get("model_type") == "align":
								__lowerCAmelCase:	int   				=      config_dict["vision_config"]

						if "model_type" in config_dict and hasattr(cls					,  "model_type") and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
								    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors.")

						return cls.from_dict(UpperCamelCase__					,  **UpperCamelCase__)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Any       				=  """align"""
				SCREAMING_SNAKE_CASE_       :				Any       				=  True

				def __init__(     self							:    str					,  UpperCamelCase__							:    Dict=None					,  UpperCamelCase__							:    Tuple=None					,  UpperCamelCase__							:    Union[str, Any]=6_4_0					,  UpperCamelCase__							:    Dict=1.0					,  UpperCamelCase__							:    Optional[Any]=0.02					,  **UpperCamelCase__							:    Optional[int]					,  )->       str:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						if text_config is None:
								__lowerCAmelCase:	Tuple   				=      {}
								logger.info("text_config is None. Initializing the AlignTextConfig with default values.")

						if vision_config is None:
								__lowerCAmelCase:	Tuple   				=      {}
								logger.info("vision_config is None. Initializing the AlignVisionConfig with default values.")

						__lowerCAmelCase:	int   				=      AlignTextConfig(**UpperCamelCase__)
						__lowerCAmelCase:	str   				=      AlignVisionConfig(**UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      projection_dim
						__lowerCAmelCase:	Dict   				=      temperature_init_value
						__lowerCAmelCase:	Any   				=      initializer_range

				@classmethod
				def 							lowercase_					(     cls							:    Tuple					,  UpperCamelCase__							:    AlignTextConfig					,  UpperCamelCase__							:    AlignVisionConfig					,  **UpperCamelCase__							:    Optional[int])->       List[Any]:



						'''simple docstring'''


						return cls(text_config=text_config.to_dict()					,  vision_config=vision_config.to_dict()					,  **UpperCamelCase__)




				def 							lowercase_					(     self							:    List[Any])->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      copy.deepcopy(self.__dict__)
						__lowerCAmelCase:	Optional[int]   				=      self.text_config.to_dict()
						__lowerCAmelCase:	str   				=      self.vision_config.to_dict()
						__lowerCAmelCase:	Optional[Any]   				=      self.__class__.model_type
						return output





"""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__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[str]:
		__lowerCAmelCase:	Dict   				=      original_name.split("."			)[0]
		__lowerCAmelCase:	Any   				=      key.split("."			)
		__lowerCAmelCase:	Union[str, Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 2]			)
		__lowerCAmelCase:	List[Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 1]			)
		__lowerCAmelCase:	List[str]   				=      orig_block_num - offset

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






def    a__   (   )     ->     Tuple:
		__lowerCAmelCase:	Union[str, Any]   				=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowerCAmelCase:	int   				=      Image.open(requests.get(__SCREAMING_SNAKE_CASE ,			stream=__SCREAMING_SNAKE_CASE			).raw			)

		return image






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		__lowerCAmelCase:	Any   				=      PoolFormerConfig()

		# set attributes based on model_name
		__lowerCAmelCase:	Any   				=      "huggingface/label-files"
		__lowerCAmelCase:	int   				=      model_name[-3:]
		__lowerCAmelCase:	List[Any]   				=      1_0_0_0
		__lowerCAmelCase:	Tuple   				=      "imagenet-1k-id2label.json"
		__lowerCAmelCase:	str   				=      (1, 1_0_0_0)

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

		# load image processor
		__lowerCAmelCase:	Union[str, Any]   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)

		# Prepare image
		__lowerCAmelCase:	int   				=      prepare_img()
		__lowerCAmelCase:	Tuple   				=      image_processor(images=__SCREAMING_SNAKE_CASE ,			return_tensors="pt"			).pixel_values

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

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

		# rename keys
		__lowerCAmelCase:	Any   				=      rename_keys(__SCREAMING_SNAKE_CASE			)

		# create HuggingFace model and load state dict
		__lowerCAmelCase:	str   				=      PoolFormerForImageClassification(__SCREAMING_SNAKE_CASE			)
		model.load_state_dict(__SCREAMING_SNAKE_CASE			)
		model.eval()

		# Define image processor
		__lowerCAmelCase:	Any   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      image_processor(images=prepare_img() ,			return_tensors="pt"			).pixel_values

		# forward pass
		__lowerCAmelCase:	int   				=      model(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.3045, -0.6758, -0.4869]			)
		elif size == "s24":
				__lowerCAmelCase:	Optional[int]   				=      torch.tensor([0.4402, -0.1374, -0.8045]			)
		elif size == "s36":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.6080, -0.5133, -0.5898]			)
		elif size == "m36":
				__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor([0.3952, 0.2263, -1.2668]			)
		elif size == "m48":
				__lowerCAmelCase:	List[str]   				=      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] ,			__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__":
			__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)


