
"""simple docstring"""




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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbart import MBartTokenizer
else:
  A__   :					List[str]							=   None


A__   :					str							=   logging.get_logger(__name__)


A__   :					Any							=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A__   :					Union[str, Any]							=   {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

A__   :					int							=   {
    'facebook/mbart-large-en-ro': 1_0_2_4,
    'facebook/mbart-large-cc25': 1_0_2_4,
}

# fmt: off
A__   :					Dict							=   ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		['''input_ids''', '''attention_mask''']
       UpperCamelCase_   =		MBartTokenizer

       UpperCamelCase_   =		[]
       UpperCamelCase_   =		[]
       def __init__(     self ,				A_=None ,				A_=None ,				A_="<s>" ,				A_="</s>" ,				A_="</s>" ,				A_="<s>" ,				A_="<unk>" ,				A_="<pad>" ,				A_="<mask>" ,				A_=None ,				A_=None ,				A_=None ,				**A_ ,				)     ->	str:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else mask_token

         super().__init__(
             vocab_file=A_ ,				tokenizer_file=A_ ,				bos_token=A_ ,				eos_token=A_ ,				sep_token=A_ ,				cls_token=A_ ,				unk_token=A_ ,				pad_token=A_ ,				mask_token=A_ ,				src_lang=A_ ,				tgt_lang=A_ ,				additional_special_tokens=A_ ,				**A_ ,				)

         _lowercase:			Dict       =     vocab_file
         _lowercase:			Optional[Any]       =     False if not self.vocab_file else True

         _lowercase:			Tuple       =     FAIRSEQ_LANGUAGE_CODES.copy()

         if additional_special_tokens is not None:
           # Only add those special tokens if they are not already there.
           _additional_special_tokens.extend(
               [t for t in additional_special_tokens if t not in _additional_special_tokens]       )

         self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}       )
         _lowercase:			Tuple       =     {
             lang_code: self.convert_tokens_to_ids(A_       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         _lowercase:			List[str]       =     src_lang if src_lang is not None else '''en_XX'''
         _lowercase:			Optional[Any]       =     self.convert_tokens_to_ids(self._src_lang       )
         _lowercase:			Dict       =     tgt_lang
         self.set_src_lang_special_tokens(self._src_lang       )
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         return self._src_lang
       @src_lang.setter
       def 							lowercase_							(     self ,				A_       )     ->	None:





         """simple docstring"""




         _lowercase:			Any       =     new_src_lang
         self.set_src_lang_special_tokens(self._src_lang       )
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     [self.sep_token_id]
         _lowercase:			List[str]       =     [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]
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				**A_       )     ->	str:





         """simple docstring"""




         if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
         _lowercase:			Tuple       =     src_lang
         _lowercase:			Tuple       =     self(A_ ,				add_special_tokens=A_ ,				return_tensors=A_ ,				**A_       )
         _lowercase:			Dict       =     self.convert_tokens_to_ids(A_       )
         _lowercase:			Union[str, Any]       =     tgt_lang_id
         return inputs
       def 							lowercase_							(     self ,				A_ ,				A_ = "en_XX" ,				A_ = None ,				A_ = "ro_RO" ,				**A_ ,				)     ->	BatchEncoding:





         """simple docstring"""




         _lowercase:			int       =     src_lang
         _lowercase:			Tuple       =     tgt_lang
         return super().prepare_seqaseq_batch(A_ ,				A_ ,				**A_       )
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         return self.set_src_lang_special_tokens(self.src_lang       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         return self.set_tgt_lang_special_tokens(self.tgt_lang       )
       def 							lowercase_							(     self ,				A_       )     ->	None:





         """simple docstring"""




         _lowercase:			Any       =     self.convert_tokens_to_ids(A_       )
         _lowercase:			Optional[Any]       =     []
         _lowercase:			str       =     [self.eos_token_id, self.cur_lang_code]

         _lowercase:			Optional[int]       =     self.convert_ids_to_tokens(self.prefix_tokens       )
         _lowercase:			Optional[int]       =     self.convert_ids_to_tokens(self.suffix_tokens       )

         _lowercase:			List[str]       =     processors.TemplateProcessing(
             single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,				pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,				self.prefix_tokens + self.suffix_tokens       )       ) ,				)
       def 							lowercase_							(     self ,				A_       )     ->	None:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.convert_tokens_to_ids(A_       )
         _lowercase:			Optional[Any]       =     []
         _lowercase:			List[Any]       =     [self.eos_token_id, self.cur_lang_code]

         _lowercase:			Optional[int]       =     self.convert_ids_to_tokens(self.prefix_tokens       )
         _lowercase:			Dict       =     self.convert_ids_to_tokens(self.suffix_tokens       )

         _lowercase:			int       =     processors.TemplateProcessing(
             single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,				pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,				self.prefix_tokens + self.suffix_tokens       )       ) ,				)



       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	Tuple[str]:





         """simple docstring"""




         if not self.can_save_slow_tokenizer:
           raise ValueError(
               '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
               '''tokenizer.'''       )

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

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

         return (out_vocab_file,)


"""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()
A__   :					List[Any]							=   logging.get_logger()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase = True						):






  """simple docstring"""



  print(f'''Converting {name}...'''						)

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        _lowercase:			int       =     timm.create_model('''levit_128s''' ,				pretrained=_UpperCamelCase						)
      else:
        _lowercase:			int       =     timm.create_model('''levit_128''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 192:
      _lowercase:			str       =     timm.create_model('''levit_192''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 256:
      _lowercase:			int       =     timm.create_model('''levit_256''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 384:
      _lowercase:			Dict       =     timm.create_model('''levit_384''' ,				pretrained=_UpperCamelCase						)

    from_model.eval()
    _lowercase:			Any       =     LevitForImageClassificationWithTeacher(_UpperCamelCase						).eval()
    _lowercase:			Union[str, Any]       =     OrderedDict()

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

    _lowercase:			int       =     torch.randn((2, 3, 224, 224)						)
    _lowercase:			List[str]       =     from_model(_UpperCamelCase						)
    _lowercase:			Optional[int]       =     our_model(_UpperCamelCase						).logits

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

  _lowercase:			Union[str, Any]       =     name
  print(_UpperCamelCase						)

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name						)
    _lowercase:			Dict       =     LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name						)

    print(f'''Pushed {checkpoint_name}'''						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase = None ,				_UpperCamelCase = True						):






  """simple docstring"""



  _lowercase:			int       =     '''imagenet-1k-id2label.json'''
  _lowercase:			int       =     1_000
  _lowercase:			Tuple       =     (1, num_labels)

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

  _lowercase:			int       =     idalabel
  _lowercase:			Tuple       =     {v: k for k, v in idalabel.items()}

  _lowercase:			Tuple       =     partial(_UpperCamelCase ,				num_labels=_UpperCamelCase ,				idalabel=_UpperCamelCase ,				labelaid=_UpperCamelCase						)

  _lowercase:			Union[str, Any]       =     {
      '''levit-128S''': 128,
      '''levit-128''': 128,
      '''levit-192''': 192,
      '''levit-256''': 256,
      '''levit-384''': 384,
  }

  _lowercase:			int       =     {
      '''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__":
  A__   :					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',
  )

  A__   :					Optional[Any]							=   parser.parse_args()
  A__   :					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)
