


"""simple docstring"""


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

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


__lowerCamelCase      :Optional[Any]	    =		logging.get_logger(__name__)

__lowerCamelCase      :List[str]	    =		{
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

__lowerCamelCase      :Dict	    =		{
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

__lowerCamelCase      :List[str]	    =		{
    'vinai/phobert-base': 256,
    'vinai/phobert-large': 256,
}




def   snake_case		(     UpperCamelCase__       :						Union[str, Any]    )   ->      Dict:
      lowerCamelCase					:					Union[str, Any]        =	set()
      lowerCamelCase					:					List[str]        =	word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)    )
            lowerCamelCase					:					Union[str, Any]        =	char

      lowerCamelCase					:					Optional[int]        =	set(UpperCamelCase__    )
      return pairs






class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Dict													=VOCAB_FILES_NAMES
     snake_case__	:					Dict													=PRETRAINED_VOCAB_FILES_MAP
     snake_case__	:					Any													=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(	self:						Optional[int]     ,		__a:						List[str]     ,		__a:						Any     ,		__a:						Union[str, Any]="<s>"     ,		__a:						List[Any]="</s>"     ,		__a:						str="</s>"     ,		__a:						str="<s>"     ,		__a:						str="<unk>"     ,		__a:						Dict="<pad>"     ,		__a:						List[str]="<mask>"     ,		**__a:						str     ,		)->    List[str]:
           super().__init__(
               bos_token=__a     ,		eos_token=__a     ,		unk_token=__a     ,		sep_token=__a     ,		cls_token=__a     ,		pad_token=__a     ,		mask_token=__a     ,		**__a     ,		)

           lowerCamelCase					:					Tuple        =	vocab_file
           lowerCamelCase					:					List[Any]        =	merges_file

           lowerCamelCase					:					Tuple        =	{}
           lowerCamelCase					:					List[str]        =	0
           lowerCamelCase					:					Union[str, Any]        =	1
           lowerCamelCase					:					Tuple        =	2
           lowerCamelCase					:					Dict        =	3

           self.add_from_file(__a	)

           lowerCamelCase					:					List[str]        =	{v: k for k, v in self.encoder.items()}

           with open(__a     ,		encoding="""utf-8"""	) as merges_handle:
                 lowerCamelCase					:					Optional[int]        =	merges_handle.read().split("""\n"""	)[:-1]
           lowerCamelCase					:					List[Any]        =	[tuple(merge.split()[:-1]	) for merge in merges]
           lowerCamelCase					:					List[Any]        =	dict(zip(__a     ,		range(len(__a	)	)	)	)
           lowerCamelCase					:					Union[str, Any]        =	{}

     def 		a__						(	self:						Optional[Any]     ,		__a:						List[int]     ,		__a:						Optional[List[int]] = None	)->    List[int]:

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

     def 		a__						(	self:						str     ,		__a:						List[int]     ,		__a:						Optional[List[int]] = None     ,		__a:						bool = False	)->    List[int]:

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

           if token_ids_a is None:
                 return [1] + ([0] * len(__a	)) + [1]
           return [1] + ([0] * len(__a	)) + [1, 1] + ([0] * len(__a	)) + [1]

     def 		a__						(	self:						str     ,		__a:						List[int]     ,		__a:						Optional[List[int]] = None	)->    List[int]:
           lowerCamelCase					:					int        =	[self.sep_token_id]
           lowerCamelCase					:					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 + sep + token_ids_a + sep	) * [0]

     @property
     def 		a__						(	self:						Optional[Any]	)->    Optional[int]:
           return len(self.encoder	)

     def 		a__						(	self:						Any	)->    List[str]:
           return dict(self.encoder     ,		**self.added_tokens_encoder	)

     def 		a__						(	self:						Union[str, Any]     ,		__a:						Union[str, Any]	)->    int:
           if token in self.cache:
                 return self.cache[token]
           lowerCamelCase					:					Tuple        =	tuple(__a	)
           lowerCamelCase					:					List[Any]        =	tuple(list(word[:-1]	) + [word[-1] + """</w>"""]	)
           lowerCamelCase					:					str        =	get_pairs(__a	)

           if not pairs:
                 return token

           while True:
                 lowerCamelCase					:					Union[str, Any]        =	min(__a     ,		key=lambda __a	: self.bpe_ranks.get(__a     ,		float("""inf"""	)	)	)
                 if bigram not in self.bpe_ranks:
                       break
                 lowerCamelCase					:					int        =	bigram
                 lowerCamelCase					:					List[str]        =	[]
                 lowerCamelCase					:					Optional[Any]        =	0
                 while i < len(__a	):
                       try:
                             lowerCamelCase					:					List[str]        =	word.index(__a     ,		__a	)
                       except ValueError:
                             new_word.extend(word[i:]	)
                             break
                       else:
                             new_word.extend(word[i:j]	)
                             lowerCamelCase					:					Union[str, Any]        =	j

                       if word[i] == first and i < len(__a	) - 1 and word[i + 1] == second:
                             new_word.append(first + second	)
                             i += 2
                       else:
                             new_word.append(word[i]	)
                             i += 1
                 lowerCamelCase					:					List[str]        =	tuple(__a	)
                 lowerCamelCase					:					int        =	new_word
                 if len(__a	) == 1:
                       break
                 else:
                       lowerCamelCase					:					List[str]        =	get_pairs(__a	)
           lowerCamelCase					:					Tuple        =	"""@@ """.join(__a	)
           lowerCamelCase					:					List[Any]        =	word[:-4]
           lowerCamelCase					:					Union[str, Any]        =	word
           return word

     def 		a__						(	self:						Tuple     ,		__a:						int	)->    Dict:
           lowerCamelCase					:					Optional[Any]        =	[]

           lowerCamelCase					:					Optional[Any]        =	re.findall(r"""\S+\n?"""     ,		__a	)

           for token in words:
                 split_tokens.extend(list(self.bpe(__a	).split(""" """	)	)	)
           return split_tokens

     def 		a__						(	self:						List[str]     ,		__a:						Optional[Any]	)->    Dict:
           return self.encoder.get(__a     ,		self.encoder.get(self.unk_token	)	)

     def 		a__						(	self:						str     ,		__a:						Optional[Any]	)->    Optional[Any]:
           return self.decoder.get(__a     ,		self.unk_token	)

     def 		a__						(	self:						Any     ,		__a:						List[str]	)->    int:
           lowerCamelCase					:					int        =	""" """.join(__a	).replace("""@@ """     ,		""""""	).strip()
           return out_string

     def 		a__						(	self:						Dict     ,		__a:						str     ,		__a:						Optional[str] = None	)->    Tuple[str]:
           if not os.path.isdir(__a	):
                 logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
                 return
           lowerCamelCase					:					int        =	os.path.join(
               __a     ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)
           lowerCamelCase					:					Optional[int]        =	os.path.join(
               __a     ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]	)

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

           if os.path.abspath(self.merges_file	) != os.path.abspath(__a	):
                 copyfile(self.merges_file     ,		__a	)

           return out_vocab_file, out_merge_file







     def 		a__						(	self:						List[str]     ,		__a:						Optional[Any]	)->    Union[str, Any]:
           if isinstance(__a     ,		__a	):
                 try:
                       with open(__a     ,		"""r"""     ,		encoding="""utf-8"""	) as fd:
                             self.add_from_file(__a	)
                 except FileNotFoundError as fnfe:
                       raise fnfe
                 except UnicodeError:
                       raise Exception(f'Incorrect encoding detected in {f}, please rebuild the dataset'	)
                 return

           lowerCamelCase					:					Any        =	f.readlines()
           for lineTmp in lines:
                 lowerCamelCase					:					Dict        =	lineTmp.strip()
                 lowerCamelCase					:					Union[str, Any]        =	line.rfind(""" """	)
                 if idx == -1:
                       raise ValueError("""Incorrect dictionary format, expected '<token> <cnt>'"""	)
                 lowerCamelCase					:					Union[str, Any]        =	line[:idx]
                 lowerCamelCase					:					List[str]        =	len(self.encoder	)








"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase      :Dict	    =		logging.get_logger()




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						LevitConfig			,				UpperCamelCase__       :						Path			,				UpperCamelCase__       :						bool = True    )   ->      Dict:
      print(F'Converting {name}...'    )

      with torch.no_grad():
            if hidden_sizes == 128:
                  if name[-1] == "S":
                        lowerCamelCase					:					Optional[Any]        =	timm.create_model("""levit_128s"""			,				pretrained=UpperCamelCase__    )
                  else:
                        lowerCamelCase					:					Dict        =	timm.create_model("""levit_128"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 192:
                  lowerCamelCase					:					Tuple        =	timm.create_model("""levit_192"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 256:
                  lowerCamelCase					:					Optional[int]        =	timm.create_model("""levit_256"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 384:
                  lowerCamelCase					:					Dict        =	timm.create_model("""levit_384"""			,				pretrained=UpperCamelCase__    )

            from_model.eval()
            lowerCamelCase					:					Optional[Any]        =	LevitForImageClassificationWithTeacher(UpperCamelCase__    ).eval()
            lowerCamelCase					:					Tuple        =	OrderedDict()

            lowerCamelCase					:					Optional[Any]        =	from_model.state_dict()
            lowerCamelCase					:					str        =	list(from_model.state_dict().keys()    )
            lowerCamelCase					:					List[Any]        =	list(our_model.state_dict().keys()    )
            print(len(UpperCamelCase__    )			,				len(UpperCamelCase__    )    )
            for i in range(len(UpperCamelCase__    )    ):
                  lowerCamelCase					:					str        =	weights[og_keys[i]]
            our_model.load_state_dict(UpperCamelCase__    )

            lowerCamelCase					:					int        =	torch.randn((2, 3, 224, 224)    )
            lowerCamelCase					:					Any        =	from_model(UpperCamelCase__    )
            lowerCamelCase					:					List[Any]        =	our_model(UpperCamelCase__    ).logits

      assert torch.allclose(UpperCamelCase__			,				UpperCamelCase__    ), "The model logits don't match the original one."

      lowerCamelCase					:					Dict        =	name
      print(UpperCamelCase__    )

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name    )
            lowerCamelCase					:					Optional[int]        =	LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name    )

            print(F'Pushed {checkpoint_name}'    )




def   snake_case		(     UpperCamelCase__       :						Path			,				UpperCamelCase__       :						str = None			,				UpperCamelCase__       :						bool = True    )   ->      Optional[int]:
      lowerCamelCase					:					Optional[Any]        =	"""imagenet-1k-id2label.json"""
      lowerCamelCase					:					List[Any]        =	1000
      lowerCamelCase					:					Dict        =	(1, num_labels)

      lowerCamelCase					:					List[Any]        =	"""huggingface/label-files"""
      lowerCamelCase					:					Optional[int]        =	num_labels
      lowerCamelCase					:					List[str]        =	json.load(open(hf_hub_download(UpperCamelCase__			,				UpperCamelCase__			,				repo_type="""dataset"""    )			,				"""r"""    )    )
      lowerCamelCase					:					Any        =	{int(UpperCamelCase__    ): v for k, v in idalabel.items()}

      lowerCamelCase					:					List[Any]        =	idalabel
      lowerCamelCase					:					str        =	{v: k for k, v in idalabel.items()}

      lowerCamelCase					:					Tuple        =	partial(UpperCamelCase__			,				num_labels=UpperCamelCase__			,				idalabel=UpperCamelCase__			,				labelaid=UpperCamelCase__    )

      lowerCamelCase					:					Optional[int]        =	{
          """levit-128S""": 128,
          """levit-128""": 128,
          """levit-192""": 192,
          """levit-256""": 256,
          """levit-384""": 384,
      }

      lowerCamelCase					:					List[Any]        =	{
          """levit-128S""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 6, 8]			,				depths=[2, 3, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-128""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 8, 12]			,				depths=[4, 4, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-192""": ImageNetPreTrainedConfig(
              hidden_sizes=[192, 288, 384]			,				num_attention_heads=[3, 5, 6]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-256""": ImageNetPreTrainedConfig(
              hidden_sizes=[256, 384, 512]			,				num_attention_heads=[4, 6, 8]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-384""": ImageNetPreTrainedConfig(
              hidden_sizes=[384, 512, 768]			,				num_attention_heads=[6, 9, 12]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0.1			,				),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				names_to_config[model_name]			,				UpperCamelCase__			,				UpperCamelCase__    )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      return config, expected_shape


if __name__ == "__main__":
    __lowerCamelCase      :Union[str, Any]	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default=None,
        type=str,
        help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default='levit-dump-folder/',
        type=Path,
        required=False,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
    parser.add_argument(
        '--no-push_to_hub',
        dest='push_to_hub',
        action='store_false',
        help='Do not push model and image processor to the hub',
    )

    __lowerCamelCase      :List[Any]	    =		parser.parse_args()
    __lowerCamelCase      :Path	    =		args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




