
'''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()
UpperCAmelCase_							: str =							logging.get_logger(__name__)







def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     Optional[Any]				):



      """simple docstring"""




      _lowerCamelCase					: Tuple		    = original_name.split("."				)[0]
      _lowerCamelCase					: List[Any]		    = key.split("."				)
      _lowerCamelCase					: Dict		    = int(key_list[key_list.index(_lowerCAmelCase				) - 2]				)
      _lowerCamelCase					: Union[str, Any]		    = int(key_list[key_list.index(_lowerCAmelCase				) - 1]				)
      _lowerCamelCase					: Optional[int]		    = orig_block_num - offset

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







def 		A_							(						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




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







def 		A_							(						):



      """simple docstring"""




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

      return image







@torch.no_grad()
def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = PoolFormerConfig()

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

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

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

      # Prepare image
      _lowerCamelCase					: Dict		    = prepare_img()
      _lowerCamelCase					: List[str]		    = image_processor(images=_lowerCAmelCase					,						return_tensors="pt"				).pixel_values

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

      # load original state dict
      _lowerCamelCase					: Any		    = torch.load(_lowerCAmelCase					,						map_location=torch.device("cpu"				)				)

      # rename keys
      _lowerCamelCase					: Dict		    = rename_keys(_lowerCAmelCase				)

      # create HuggingFace model and load state dict
      _lowerCamelCase					: Optional[Any]		    = PoolFormerForImageClassification(_lowerCAmelCase				)
      model.load_state_dict(_lowerCAmelCase				)
      model.eval()

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

      # forward pass
      _lowerCamelCase					: Union[str, Any]		    = model(_lowerCAmelCase				)
      _lowerCamelCase					: Any		    = outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            _lowerCamelCase					: Tuple		    = torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]				)
      elif size == "s24":
            _lowerCamelCase					: Tuple		    = torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]				)
      elif size == "s36":
            _lowerCamelCase					: Tuple		    = torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]				)
      elif size == "m36":
            _lowerCamelCase					: Dict		    = torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]				)
      elif size == "m48":
            _lowerCamelCase					: str		    = 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]					,						_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__":
 UpperCAmelCase_							: Optional[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.'
 )
 UpperCAmelCase_							: Tuple =							parser.parse_args()
 convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[Any]										=						'''mobilenet_v2'''


	def __init__(    self				,     lowercase__=3				,     lowercase__=2_2_4				,     lowercase__=1.0				,     lowercase__=8				,     lowercase__=8				,     lowercase__=6				,     lowercase__=3_2				,     lowercase__=True				,     lowercase__=True				,     lowercase__="relu6"				,     lowercase__=True				,     lowercase__=0.8				,     lowercase__=0.0_2				,     lowercase__=0.0_0_1				,     lowercase__=2_5_5				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

							if depth_multiplier <= 0:
													raise ValueError('''depth_multiplier must be greater than zero.''')

							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Dict	    =	depth_multiplier
							__UpperCAmelCase			:							Optional[Any]	    =	depth_divisible_by
							__UpperCAmelCase			:							Union[str, Any]	    =	min_depth
							__UpperCAmelCase			:							Dict	    =	expand_ratio
							__UpperCAmelCase			:							int	    =	output_stride
							__UpperCAmelCase			:							Tuple	    =	first_layer_is_expansion
							__UpperCAmelCase			:							Union[str, Any]	    =	finegrained_output
							__UpperCAmelCase			:							int	    =	hidden_act
							__UpperCAmelCase			:							Tuple	    =	tf_padding
							__UpperCAmelCase			:							List[str]	    =	classifier_dropout_prob
							__UpperCAmelCase			:							Optional[Any]	    =	initializer_range
							__UpperCAmelCase			:							List[str]	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	semantic_loss_ignore_index






class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						version.parse('''1.11''' )


	@property
	def  A(    self):
							return OrderedDict([('''pixel_values''', {0: '''batch'''})])


	@property
	def  A(    self):
							if self.task == "image-classification":
													return OrderedDict([('''logits''', {0: '''batch'''})])
							else:
													return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])


	@property
	def  A(    self):
							return 1e-4

