

'''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      					=     logging.get_logger(__name__)


def  a__       (    _SCREAMING_SNAKE_CASE   :       Union[str, Any]	,				_SCREAMING_SNAKE_CASE   :       List[str]	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]	,				_SCREAMING_SNAKE_CASE   :       str				)     ->					int:


	"""simple docstring"""





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

	UpperCAmelCase_     :      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   :       Tuple				)     ->					List[Any]:


	"""simple docstring"""





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


def  a__       (    )     ->					List[str]:


	"""simple docstring"""





	UpperCAmelCase_     :      Optional[int]						=  "http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase_     :      Union[str, Any]						=  Image.open(requests.get(_SCREAMING_SNAKE_CASE	,				stream=_SCREAMING_SNAKE_CASE				).raw				)

	return image


@torch.no_grad()
def  a__       (    _SCREAMING_SNAKE_CASE   :       List[str]	,				_SCREAMING_SNAKE_CASE   :       str	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]				)     ->					Optional[int]:


	"""simple docstring"""





	UpperCAmelCase_     :      Dict						=  PoolFormerConfig()

	# set attributes based on model_name
	UpperCAmelCase_     :      Any						=  "huggingface/label-files"
	UpperCAmelCase_     :      str						=  model_name[-3:]
	UpperCAmelCase_     :      Union[str, Any]						=  10_00
	UpperCAmelCase_     :      Union[str, Any]						=  "imagenet-1k-id2label.json"
	UpperCAmelCase_     :      Tuple						=  (1, 10_00)

	# set config attributes
	UpperCAmelCase_     :      int						=  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				repo_type="dataset"				)	,				"r"				)				)
	UpperCAmelCase_     :      str						=  {int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
	UpperCAmelCase_     :      Any						=  idalabel
	UpperCAmelCase_     :      List[str]						=  {v: k for k, v in idalabel.items()}
	if size == "s12":
		UpperCAmelCase_     :      Tuple						=  [2, 2, 6, 2]
		UpperCAmelCase_     :      int						=  [64, 1_28, 3_20, 5_12]
		UpperCAmelCase_     :      int						=  4.0
		UpperCAmelCase_     :      Optional[int]						=  0.9
	elif size == "s24":
		UpperCAmelCase_     :      str						=  [4, 4, 12, 4]
		UpperCAmelCase_     :      Union[str, Any]						=  [64, 1_28, 3_20, 5_12]
		UpperCAmelCase_     :      List[Any]						=  4.0
		UpperCAmelCase_     :      Optional[Any]						=  0.9
	elif size == "s36":
		UpperCAmelCase_     :      str						=  [6, 6, 18, 6]
		UpperCAmelCase_     :      str						=  [64, 1_28, 3_20, 5_12]
		UpperCAmelCase_     :      Union[str, Any]						=  4.0
		UpperCAmelCase_     :      Any						=  1E-6
		UpperCAmelCase_     :      Any						=  0.9
	elif size == "m36":
		UpperCAmelCase_     :      int						=  [6, 6, 18, 6]
		UpperCAmelCase_     :      Any						=  [96, 1_92, 3_84, 7_68]
		UpperCAmelCase_     :      Union[str, Any]						=  4.0
		UpperCAmelCase_     :      int						=  1E-6
		UpperCAmelCase_     :      Optional[Any]						=  0.95
	elif size == "m48":
		UpperCAmelCase_     :      str						=  [8, 8, 24, 8]
		UpperCAmelCase_     :      Optional[int]						=  [96, 1_92, 3_84, 7_68]
		UpperCAmelCase_     :      str						=  4.0
		UpperCAmelCase_     :      List[str]						=  1E-6
		UpperCAmelCase_     :      Optional[int]						=  0.95
	else:
		raise ValueError(F'''Size {size} not supported'''				)

	# load image processor
	UpperCAmelCase_     :      Dict						=  PoolFormerImageProcessor(crop_pct=_SCREAMING_SNAKE_CASE				)

	# Prepare image
	UpperCAmelCase_     :      Optional[Any]						=  prepare_img()
	UpperCAmelCase_     :      List[str]						=  image_processor(images=_SCREAMING_SNAKE_CASE	,				return_tensors="pt"				).pixel_values

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

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

	# rename keys
	UpperCAmelCase_     :      Tuple						=  rename_keys(_SCREAMING_SNAKE_CASE				)

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

	# Define image processor
	UpperCAmelCase_     :      List[str]						=  PoolFormerImageProcessor(crop_pct=_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Dict						=  image_processor(images=prepare_img()	,				return_tensors="pt"				).pixel_values

	# forward pass
	UpperCAmelCase_     :      Any						=  model(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      int						=  outputs.logits

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











"""simple docstring"""




from numpy import exp, pi, sqrt






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       float = 0.0       ,			_lowercase		:       float = 1.0   )					->int:






	'''simple docstring'''






	return 1 / sqrt(2 * pi * sigma**2   ) * exp(-((x - mu) ** 2) / (2 * sigma**2)   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


