


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__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





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

       _lowerCamelCase       :		Union[str, Any]		=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__					:      Union[str, Any]     ):
       '''simple docstring'''





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




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Any		='http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCamelCase       :		Any		=Image.open(requests.get(_UpperCamelCase		,			stream=_UpperCamelCase     ).raw     )

       return image




@torch.no_grad()
def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      List[str]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=PoolFormerConfig()

       # set attributes based on model_name
       _lowerCamelCase       :		str		='huggingface/label-files'
       _lowerCamelCase       :		List[Any]		=model_name[-3:]
       _lowerCamelCase       :		Optional[Any]		=1_000
       _lowerCamelCase       :		int		='imagenet-1k-id2label.json'
       _lowerCamelCase       :		Optional[int]		=(1, 1_000)

       # set config attributes
       _lowerCamelCase       :		Dict		=json.load(open(hf_hub_download(_UpperCamelCase		,			_UpperCamelCase		,			repo_type='dataset'     )		,			'r'     )     )
       _lowerCamelCase       :		List[Any]		={int(_UpperCamelCase     ): v for k, v in idalabel.items()}
       _lowerCamelCase       :		Any		=idalabel
       _lowerCamelCase       :		Optional[Any]		={v: k for k, v in idalabel.items()}
       if size == "s12":
              _lowerCamelCase       :		Optional[int]		=[2, 2, 6, 2]
              _lowerCamelCase       :		str		=[64, 128, 320, 512]
              _lowerCamelCase       :		List[str]		=4.0
              _lowerCamelCase       :		Optional[int]		=0.9
       elif size == "s24":
              _lowerCamelCase       :		Dict		=[4, 4, 12, 4]
              _lowerCamelCase       :		List[Any]		=[64, 128, 320, 512]
              _lowerCamelCase       :		Any		=4.0
              _lowerCamelCase       :		Optional[Any]		=0.9
       elif size == "s36":
              _lowerCamelCase       :		Dict		=[6, 6, 18, 6]
              _lowerCamelCase       :		Optional[Any]		=[64, 128, 320, 512]
              _lowerCamelCase       :		Tuple		=4.0
              _lowerCamelCase       :		List[str]		=1e-6
              _lowerCamelCase       :		Any		=0.9
       elif size == "m36":
              _lowerCamelCase       :		List[str]		=[6, 6, 18, 6]
              _lowerCamelCase       :		Optional[int]		=[96, 192, 384, 768]
              _lowerCamelCase       :		Any		=4.0
              _lowerCamelCase       :		Tuple		=1e-6
              _lowerCamelCase       :		str		=0.95
       elif size == "m48":
              _lowerCamelCase       :		str		=[8, 8, 24, 8]
              _lowerCamelCase       :		List[str]		=[96, 192, 384, 768]
              _lowerCamelCase       :		Any		=4.0
              _lowerCamelCase       :		Tuple		=1e-6
              _lowerCamelCase       :		Tuple		=0.95
       else:
              raise ValueError(F'''Size {size} not supported'''     )

       # load image processor
       _lowerCamelCase       :		Tuple		=PoolFormerImageProcessor(crop_pct=_UpperCamelCase     )

       # Prepare image
       _lowerCamelCase       :		str		=prepare_img()
       _lowerCamelCase       :		str		=image_processor(images=_UpperCamelCase		,			return_tensors='pt'     ).pixel_values

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

       # load original state dict
       _lowerCamelCase       :		List[Any]		=torch.load(_UpperCamelCase		,			map_location=torch.device('cpu'     )     )

       # rename keys
       _lowerCamelCase       :		Any		=rename_keys(_UpperCamelCase     )

       # create HuggingFace model and load state dict
       _lowerCamelCase       :		List[str]		=PoolFormerForImageClassification(_UpperCamelCase     )
       model.load_state_dict(_UpperCamelCase     )
       model.eval()

       # Define image processor
       _lowerCamelCase       :		List[str]		=PoolFormerImageProcessor(crop_pct=_UpperCamelCase     )
       _lowerCamelCase       :		Union[str, Any]		=image_processor(images=prepare_img()		,			return_tensors='pt'     ).pixel_values

       # forward pass
       _lowerCamelCase       :		Tuple		=model(_UpperCamelCase     )
       _lowerCamelCase       :		Optional[int]		=outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              _lowerCamelCase       :		List[Any]		=torch.tensor([-0.30_45, -0.67_58, -0.48_69]     )
       elif size == "s24":
              _lowerCamelCase       :		int		=torch.tensor([0.44_02, -0.13_74, -0.80_45]     )
       elif size == "s36":
              _lowerCamelCase       :		str		=torch.tensor([-0.60_80, -0.51_33, -0.58_98]     )
       elif size == "m36":
              _lowerCamelCase       :		List[str]		=torch.tensor([0.39_52, 0.22_63, -1.26_68]     )
       elif size == "m48":
              _lowerCamelCase       :		Any		=torch.tensor([0.11_67, -0.06_56, -0.34_23]     )
       else:
              raise ValueError(F'''Size {size} not supported'''     )

       # verify logits
       assert logits.shape == expected_shape
       assert torch.allclose(logits[0, :3]		,			_UpperCamelCase		,			atol=1e-2     )

       # finally, save model and image processor
       logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''     )
       Path(_UpperCamelCase     ).mkdir(exist_ok=_UpperCamelCase     )
       model.save_pretrained(_UpperCamelCase     )
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''     )
       image_processor.save_pretrained(_UpperCamelCase     )


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"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase						:    Optional[Any]				      ="""CLIPImageProcessor"""
							__UpperCAmelCase						:    Union[str, Any]				      =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")

							def __init__(     self    ,   __a=None    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."    ,   __a    ,   )
													__lowerCAmelCase				    =			kwargs.pop("feature_extractor"	)

										__lowerCAmelCase				    =			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("You need to specify an `image_processor`."	)
										if tokenizer is None:
													raise ValueError("You need to specify a `tokenizer`."	)

										super().__init__(__a    ,   __a	)

							def __call__(     self    ,   __a=None    ,   __a=None    ,   __a=None    ,   **__a	):

										if text is None and images is None:
													raise ValueError("You have to specify either text or images. Both cannot be none."	)

										if text is not None:
													__lowerCAmelCase				    =			self.tokenizer(__a    ,   return_tensors=__a    ,   **__a	)

										if images is not None:
													__lowerCAmelCase				    =			self.image_processor(__a    ,   return_tensors=__a    ,   **__a	)

										if text is not None and images is not None:
													__lowerCAmelCase				    =			image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										else:
													return BatchEncoding(data=dict(**__a	)    ,   tensor_type=__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.batch_decode(*__a    ,   **__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.decode(*__a    ,   **__a	)



							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer.model_input_names
										__lowerCAmelCase				    =			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)




