'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__     :	Tuple				=			logging.get_logger(__name__)

UpperCamelCase__     :	Dict				=			torch.device('cpu')







def __UpperCamelCase(   ):
			'''simple docstring'''
			UpperCAmelCase__							:				Tuple   				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			UpperCAmelCase__							:				Tuple   				=  Image.open(requests.get(__SCREAMING_SNAKE_CASE  ,			stream=__SCREAMING_SNAKE_CASE    ).raw    )
			return im







def __UpperCamelCase(   _A					:				Any    ):
			'''simple docstring'''
			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]    )

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]    )

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]    )

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]    )







def __UpperCamelCase(   _A					:				int  ,			_A					:				Tuple  ,			_A					:				Optional[Any]    ):
			'''simple docstring'''
			UpperCAmelCase__							:				str   				=  dct.pop(__SCREAMING_SNAKE_CASE    )
			UpperCAmelCase__							:				Any   				=  val







def __UpperCamelCase(   _A					:				List[str]    ):
			'''simple docstring'''
			UpperCAmelCase__							:				List[str]   				=  []
			for k in state_dict.keys():
						UpperCAmelCase__							:				Optional[Any]   				=  k
						if ".pwconv" in k:
									UpperCAmelCase__							:				Optional[Any]   				=  k_new.replace('''.pwconv'''  ,			'''.point_wise_conv'''    )
						if ".dwconv" in k:
									UpperCAmelCase__							:				Optional[Any]   				=  k_new.replace('''.dwconv'''  ,			'''.depth_wise_conv'''    )
						if ".Proj." in k:
									UpperCAmelCase__							:				Any   				=  k_new.replace('''.Proj.'''  ,			'''.proj.'''    )
						if "patch_embed" in k_new:
									UpperCAmelCase__							:				Optional[Any]   				=  k_new.replace('''patch_embed'''  ,			'''swiftformer.patch_embed.patch_embedding'''    )
						if "network" in k_new:
									UpperCAmelCase__							:				Tuple   				=  k_new.split('''.'''    )
									if ls[2].isdigit():
												UpperCAmelCase__							:				List[str]   				=  '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]    )
									else:
												UpperCAmelCase__							:				Union[str, Any]   				=  k_new.replace('''network'''  ,			'''swiftformer.encoder.network'''    )
						rename_keys.append((k, k_new)    )
			return rename_keys







@torch.no_grad()
def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				int  ,			_A					:				Union[str, Any]    ):
			'''simple docstring'''
			UpperCAmelCase__							:				Union[str, Any]   				=  SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			UpperCAmelCase__							:				Tuple   				=  10_00
			UpperCAmelCase__							:				str   				=  '''huggingface/label-files'''
			UpperCAmelCase__							:				Dict   				=  '''imagenet-1k-id2label.json'''
			UpperCAmelCase__							:				Any   				=  json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE  ,			repo_type='''dataset'''    )  ,			'''r'''    )    )
			UpperCAmelCase__							:				List[Any]   				=  {int(__SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
			UpperCAmelCase__							:				Optional[int]   				=  idalabel
			UpperCAmelCase__							:				Tuple   				=  {v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						UpperCAmelCase__							:				Optional[int]   				=  [3, 3, 6, 4]
						UpperCAmelCase__							:				Dict   				=  [48, 56, 1_12, 2_20]

			elif swiftformer_name == "swiftformer_s":
						UpperCAmelCase__							:				Any   				=  [3, 3, 9, 6]
						UpperCAmelCase__							:				str   				=  [48, 64, 1_68, 2_24]

			elif swiftformer_name == "swiftformer_l1":
						UpperCAmelCase__							:				List[Any]   				=  [4, 3, 10, 5]
						UpperCAmelCase__							:				str   				=  [48, 96, 1_92, 3_84]

			elif swiftformer_name == "swiftformer_l3":
						UpperCAmelCase__							:				str   				=  [4, 4, 12, 6]
						UpperCAmelCase__							:				Any   				=  [64, 1_28, 3_20, 5_12]

			# load state_dict of original model, remove and rename some keys
			if original_ckpt:
						if original_ckpt.startswith('''https'''    ):
									UpperCAmelCase__							:				int   				=  torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE  ,			map_location='''cpu'''  ,			check_hash=__SCREAMING_SNAKE_CASE    )
						else:
									UpperCAmelCase__							:				Optional[int]   				=  torch.load(__SCREAMING_SNAKE_CASE  ,			map_location='''cpu'''    )
			UpperCAmelCase__							:				Union[str, Any]   				=  checkpoint

			UpperCAmelCase__							:				List[Any]   				=  create_rename_keys(__SCREAMING_SNAKE_CASE    )
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE    )

			# load HuggingFace model
			UpperCAmelCase__							:				int   				=  SwiftFormerForImageClassification(__SCREAMING_SNAKE_CASE    ).eval()
			hf_model.load_state_dict(__SCREAMING_SNAKE_CASE    )

			# prepare test inputs
			UpperCAmelCase__							:				Dict   				=  prepare_img()
			UpperCAmelCase__							:				List[str]   				=  ViTImageProcessor.from_pretrained('''preprocessor_config'''    )
			UpperCAmelCase__							:				Union[str, Any]   				=  processor(images=__SCREAMING_SNAKE_CASE  ,			return_tensors='''pt'''    )

			# compare outputs from both models
			UpperCAmelCase__							:				Optional[Any]   				=  get_expected_output(__SCREAMING_SNAKE_CASE    )
			UpperCAmelCase__							:				Dict   				=  hf_model(inputs['''pixel_values''']    ).logits

			assert hf_logits.shape == torch.Size([1, 10_00]    )
			assert torch.allclose(hf_logits[0, 0:5]  ,			__SCREAMING_SNAKE_CASE  ,			atol=1e-3    )

			Path(__SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE    )
			print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''    )
			hf_model.save_pretrained(__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
		UpperCamelCase__     :	Any				=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--swiftformer_name',
		    default='swiftformer_xs',
		    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
		    type=str,
		    help='Name of the SwiftFormer model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default='./converted_outputs/',
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

		UpperCamelCase__     :	Union[str, Any]				=			parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)





'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__     :	Union[str, Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	str				=			{'vocab_file': 'sentencepiece.model'}

UpperCamelCase__     :	int				=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCamelCase__     :	Any				=			{
    'google/rembert': 256,
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[Any]      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      List[Any]      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_=False					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_="[CLS]"					,lowerCamelCase_="[SEP]"					,lowerCamelCase_="[UNK]"					,lowerCamelCase_="[SEP]"					,lowerCamelCase_="[PAD]"					,lowerCamelCase_="[CLS]"					,lowerCamelCase_="[MASK]"					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         super().__init__(
             do_lower_case=lowerCamelCase_					,remove_space=lowerCamelCase_					,keep_accents=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,unk_token=lowerCamelCase_					,sep_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,cls_token=lowerCamelCase_					,mask_token=lowerCamelCase_					,**lowerCamelCase_					,)

         UpperCAmelCase__							:				Union[str, Any]   				=  do_lower_case
         UpperCAmelCase__							:				str   				=  remove_space
         UpperCAmelCase__							:				Optional[Any]   				=  keep_accents
         UpperCAmelCase__							:				List[Any]   				=  vocab_file

         UpperCAmelCase__							:				Dict   				=  spm.SentencePieceProcessor()
         self.sp_model.Load(lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         return len(self.sp_model							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  {self.convert_ids_to_tokens(lowerCamelCase_							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab




      def __getstate__(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.__dict__.copy()
         UpperCAmelCase__							:				Any   				=  None
         return state




      def __setstate__(				self					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  d
         UpperCAmelCase__							:				Any   				=  spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=False							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.sp_model.EncodeAsPieces(lowerCamelCase_							)
         return pieces




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.sp_model.PieceToId(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         return self.sp_model.IdToPiece(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.sp_model.decode_pieces(lowerCamelCase_							)
         return out_string




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[Any]   				=  [self.cls_token_id]
         if token_ids_a is None:
            return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



         '''simple docstring'''



         if already_has_special_tokens:
            if token_ids_a is not None:
               raise ValueError(
                   '''You should not supply a second sequence if the provided sequence of '''
                   '''ids is already formatted with special tokens for the model.'''							)
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
            return [1] + ([0] * len(lowerCamelCase_							)) + [1] + ([0] * len(lowerCamelCase_							)) + [1]
         return [1] + ([0] * len(lowerCamelCase_							)) + [1]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[Any]   				=  [self.cls_token_id]

         if token_ids_a is None:
            return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_							)							)
            return
         UpperCAmelCase__							:				Union[str, Any]   				=  os.path.join(
             lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCamelCase_							):
            copyfile(self.vocab_file					,lowerCamelCase_							)

         return (out_vocab_file,)




