


'''simple docstring'''







import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
   from ...tokenization_utils_base import TextInput
from ...utils import logging


A				:					Tuple    						=			logging.get_logger(__name__)

A				:					Optional[Any]    						=			{"""vocab_file""": """spiece.model"""}

A				:					int    						=			{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A				:					Optional[int]    						=			{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

A				:					Dict    						=			"""▁"""


class 			__lowerCamelCase			(	lowerCAmelCase_	):
    """simple docstring"""
    a													=   VOCAB_FILES_NAMES
    a													=   PRETRAINED_VOCAB_FILES_MAP
    a													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a													=   ["""input_ids""", """attention_mask"""]


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							str="</s>"				,							SCREAMING_SNAKE_CASE      :							str="<unk>"				,							SCREAMING_SNAKE_CASE      :							int="<pad>"				,							SCREAMING_SNAKE_CASE      :							Tuple=100				,							SCREAMING_SNAKE_CASE      :							List[str]=None				,							SCREAMING_SNAKE_CASE      :							Any = None				,							SCREAMING_SNAKE_CASE      :							List[Any]=True				,							**SCREAMING_SNAKE_CASE      :							int				,							):

           if extra_ids > 0 and additional_special_tokens is None:
                  _A					:     Dict     = [F'<extra_id_{i}>' for i in range(__SCREAMING_SNAKE_CASE)]
           elif extra_ids > 0 and additional_special_tokens is not None:
                  # Check that we have the right number of extra_id special tokens
                  _A					:     Optional[Any]     = len(set(filter(lambda SCREAMING_SNAKE_CASE: bool('extra_id' in str(__SCREAMING_SNAKE_CASE))				,							__SCREAMING_SNAKE_CASE)))
                  if extra_tokens != extra_ids:
                         raise ValueError(
                             F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                             ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                             ' tokens')
           if legacy:
                  logger.warning_once(
                      F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
                      ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565')

           _A					:     Tuple     = legacy
           _A					:     Tuple     = {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=__SCREAMING_SNAKE_CASE				,							unk_token=__SCREAMING_SNAKE_CASE				,							pad_token=__SCREAMING_SNAKE_CASE				,							extra_ids=__SCREAMING_SNAKE_CASE				,							additional_special_tokens=__SCREAMING_SNAKE_CASE				,							sp_model_kwargs=self.sp_model_kwargs				,							legacy=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE				,							)

           _A					:     Tuple     = vocab_file
           _A					:     int     = extra_ids

           _A					:     int     = spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE)


    @staticmethod
    def        A   (  SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):

           if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
                  _A					:     Optional[Any]     = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
                  if init_max_model_length is not None and init_max_model_length != max_model_length:
                         return init_max_model_length
                  elif init_max_model_length is None:
                         warnings.warn(
                             'This tokenizer was incorrectly instantiated with a model max length of'
                             F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                             ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                             ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                             F' {pretrained_model_name_or_path} automatically truncating your input to'
                             F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                             F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                             ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                             ' instantiate this tokenizer with `model_max_length` set to your preferred value.'				,							__SCREAMING_SNAKE_CASE				,							)

           return max_model_length


    @property
    def        A   (  self      :							Optional[Any]):
           return self.sp_model.get_piece_size() + self._extra_ids


    def        A   (  self      :							Tuple):
           _A					:     int     = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							List[Any] = None				,							SCREAMING_SNAKE_CASE      :							List[str] = False):

           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=__SCREAMING_SNAKE_CASE				,							token_ids_a=__SCREAMING_SNAKE_CASE				,							already_has_special_tokens=__SCREAMING_SNAKE_CASE)

           # normal case: some special tokens
           if token_ids_a is None:
                  return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]
           return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]


    def        A   (  self      :							List[str]):
           return list(
               set(filter(lambda SCREAMING_SNAKE_CASE: bool(re.search(R'<extra_id_\d+>'				,							__SCREAMING_SNAKE_CASE)) is not None				,							self.additional_special_tokens)))


    def        A   (  self      :							int):
           return [self._convert_token_to_id(__SCREAMING_SNAKE_CASE) for token in self.get_sentinel_tokens()]


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Optional[int]):

           if len(__SCREAMING_SNAKE_CASE) > 0 and token_ids[-1] == self.eos_token_id:
                  warnings.warn(
                      F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                      ' eos tokens being added.')
                  return token_ids
           else:
                  return token_ids + [self.eos_token_id]


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[int] = None):
           _A					:     Any     = [self.eos_token_id]

           if token_ids_a is None:
                  return len(token_ids_a + eos) * [0]
           return len(token_ids_a + eos + token_ids_a + eos) * [0]


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Tuple = None):
           _A					:     Any     = self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE)
           if token_ids_a is None:
                  return token_ids_a
           else:
                  _A					:     Union[str, Any]     = self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE)
                  return token_ids_a + token_ids_a


    def __getstate__(  self      :							Any):
           _A					:     int     = self.__dict__.copy()
           _A					:     Union[str, Any]     = None
           return state


    def __setstate__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     int     = d

           # for backward compatibility
           if not hasattr(self				,							'sp_model_kwargs'):
                  _A					:     Optional[int]     = {}

           _A					:     str     = spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):

           if not self.legacy:
                  _A					:     Optional[Any]     = SPIECE_UNDERLINE + text.replace(__SCREAMING_SNAKE_CASE				,							' ')
           return super().tokenize(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							Any):

           if not self.legacy:
                  _A					:     Any     = text.startswith(__SCREAMING_SNAKE_CASE)
                  if is_first:
                         _A					:     Optional[Any]     = text[1:]

           _A					:     Dict     = self.sp_model.encode(__SCREAMING_SNAKE_CASE				,							out_type=__SCREAMING_SNAKE_CASE)

           if not self.legacy and not is_first and not text.startswith(' ') and tokens[0].startswith(__SCREAMING_SNAKE_CASE):
                  _A					:     Dict     = ([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
           return tokens


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):

           if token.startswith('<extra_id_'):
                  _A					:     Dict     = re.match(R'<extra_id_(\d+)>'				,							__SCREAMING_SNAKE_CASE)
                  _A					:     str     = int(match.group(1))
                  return self.vocab_size - num - 1
           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							int):

           if index < self.sp_model.get_piece_size():
                  _A					:     Union[str, Any]     = self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)
           else:
                  _A					:     List[str]     = F'<extra_id_{self.vocab_size - 1 - index}>'
           return token


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           _A					:     Dict     = []
           _A					:     Union[str, Any]     = ''
           _A					:     Optional[Any]     = False
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         if not prev_is_special:
                                out_string += " "
                         out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE) + token
                         _A					:     Union[str, Any]     = True
                         _A					:     Dict     = []
                  else:
                         current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
                         _A					:     Tuple     = False
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE)
           return out_string.strip()


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Dict = None):

           if not os.path.isdir(__SCREAMING_SNAKE_CASE):
                  logger.error(F'Vocabulary path ({save_directory}) should be a directory')
                  return
           _A					:     List[str]     = os.path.join(
               __SCREAMING_SNAKE_CASE				,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

           if os.path.abspath(self.vocab_file) != os.path.abspath(__SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
                  copyfile(self.vocab_file				,							__SCREAMING_SNAKE_CASE)
           elif not os.path.isfile(self.vocab_file):
                  with open(__SCREAMING_SNAKE_CASE				,							'wb') as fi:
                         _A					:     Optional[Any]     = self.sp_model.serialized_model_proto()
                         fi.write(__SCREAMING_SNAKE_CASE)

           return (out_vocab_file,)




'''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()
A				:					Optional[int]    						=			logging.get_logger(__name__)

A				:					Union[str, Any]    						=			torch.device('''cpu''')






def 				lowerCAmelCase__	(    ):
       _A					:     Tuple     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A					:     Dict     = Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase       ).raw       )
       return im






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]       ):
       if swiftformer_name == "swiftformer_xs":
              return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]       )

       elif swiftformer_name == "swiftformer_s":
              return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]       )

       elif swiftformer_name == "swiftformer_l1":
              return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]       )

       elif swiftformer_name == "swiftformer_l3":
              return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Dict       ):
       _A					:     Union[str, Any]     = dct.pop(lowerCamelCase       )
       _A					:     List[str]     = val






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






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      str  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = SwiftFormerConfig()

       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       _A					:     Any     = 1000
       _A					:     int     = 'huggingface/label-files'
       _A					:     List[Any]     = 'imagenet-1k-id2label.json'
       _A					:     Optional[int]     = json.load(open(hf_hub_download(lowerCamelCase  ,lowerCamelCase  ,repo_type='dataset'       )  ,'r'       )       )
       _A					:     Dict     = {int(lowerCamelCase       ): v for k, v in idalabel.items()}
       _A					:     Optional[int]     = idalabel
       _A					:     Any     = {v: k for k, v in idalabel.items()}

       # size of the architecture
       if swiftformer_name == "swiftformer_xs":
              _A					:     Optional[Any]     = [3, 3, 6, 4]
              _A					:     Optional[int]     = [48, 56, 112, 220]

       elif swiftformer_name == "swiftformer_s":
              _A					:     List[Any]     = [3, 3, 9, 6]
              _A					:     Tuple     = [48, 64, 168, 224]

       elif swiftformer_name == "swiftformer_l1":
              _A					:     int     = [4, 3, 10, 5]
              _A					:     int     = [48, 96, 192, 384]

       elif swiftformer_name == "swiftformer_l3":
              _A					:     Optional[Any]     = [4, 4, 12, 6]
              _A					:     Any     = [64, 128, 320, 512]

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

       _A					:     List[str]     = create_rename_keys(lowerCamelCase       )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )

       # load HuggingFace model
       _A					:     str     = SwiftFormerForImageClassification(lowerCamelCase       ).eval()
       hf_model.load_state_dict(lowerCamelCase       )

       # prepare test inputs
       _A					:     Any     = prepare_img()
       _A					:     Optional[int]     = ViTImageProcessor.from_pretrained('preprocessor_config'       )
       _A					:     Any     = processor(images=lowerCamelCase  ,return_tensors='pt'       )

       # compare outputs from both models
       _A					:     int     = get_expected_output(lowerCamelCase       )
       _A					:     Optional[int]     = hf_model(inputs['pixel_values']       ).logits

       assert hf_logits.shape == torch.Size([1, 1000]       )
       assert torch.allclose(hf_logits[0, 0:5]  ,lowerCamelCase  ,atol=1E-3       )

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


if __name__ == "__main__":
   A				:					Dict    						=			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.''')

   A				:					List[str]    						=			parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






