
"""simple docstring"""




import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		GPTSanJapaneseTokenizer
       UpperCamelCase_   =		False
       UpperCamelCase_   =		{'''do_clean_text''': False, '''add_prefix_space''': False}
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         super().setUp()

         # fmt: off
         _lowercase:			List[str]       =     ['''こん''', '''こんに''', '''にちは''', '''ばんは''', '''世界,㔺界''', '''、''', '''。''', '''<BR>''', '''<SP>''', '''<TAB>''', '''<URL>''', '''<EMAIL>''', '''<TEL>''', '''<DATE>''', '''<PRICE>''', '''<BLOCK>''', '''<KIGOU>''', '''<U2000U2BFF>''', '''<|emoji1|>''', '''<unk>''', '''<|bagoftoken|>''', '''<|endoftext|>''']
         # fmt: on
         _lowercase:			List[str]       =     {'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
         _lowercase:			Tuple       =     {'''unk_token''': '''<unk>'''}

         _lowercase:			Optional[int]       =     os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['''vocab_file''']       )
         _lowercase:			Optional[int]       =     os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['''emoji_file''']       )
         with open(self.vocab_file ,				'''w''' ,				encoding='''utf-8'''       ) as vocab_writer:
           vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )
         with open(self.emoji_file ,				'''w'''       ) as emoji_writer:
           emoji_writer.write(json.dumps(A_       )       )
       def 							lowercase_							(     self ,				**A_       )     ->	int:





         """simple docstring"""




         kwargs.update(self.special_tokens_map       )
         return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname ,				**A_       )
       def 							lowercase_							(     self ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
         _lowercase:			Any       =     '''こんにちは、世界。 \nこんばんは、世界。😀'''
         return input_text, output_text
       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         _lowercase ,				_lowercase:			Tuple       =     self.get_input_output_texts(A_       )
         _lowercase:			List[Any]       =     tokenizer.encode(A_ ,				add_special_tokens=A_       )
         _lowercase:			Dict       =     tokenizer.decode(A_ ,				clean_up_tokenization_spaces=A_       )
         return text, ids
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         pass  # TODO add if relevant
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         pass  # TODO add if relevant
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         pass  # TODO add if relevant
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.get_tokenizer()

         # Testing tokenization
         _lowercase:			Tuple       =     '''こんにちは、世界。　こんばんは、㔺界。'''
         _lowercase:			int       =     ['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
         _lowercase:			Union[str, Any]       =     tokenizer.tokenize(A_       )
         self.assertListEqual(A_ ,				A_       )

         # Testing conversion to ids without special tokens
         _lowercase:			List[Any]       =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
         _lowercase:			List[Any]       =     tokenizer.convert_tokens_to_ids(A_       )
         self.assertListEqual(A_ ,				A_       )

         # Testing conversion to ids with special tokens
         _lowercase:			int       =     tokens + [tokenizer.unk_token]
         _lowercase:			str       =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
         _lowercase:			Any       =     tokenizer.convert_tokens_to_ids(A_       )
         self.assertListEqual(A_ ,				A_       )
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.get_tokenizer()

         # Testing tokenization
         _lowercase:			Tuple       =     '''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
         _lowercase:			List[str]       =     '''こんにちは、、、、世界。こんばんは、、、、世界。'''
         _lowercase:			List[str]       =     tokenizer.encode(A_       )
         _lowercase:			Dict       =     tokenizer.decode(A_       )
         self.assertEqual(A_ ,				A_       )
       @slow
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''       )

         # Testing tokenization
         _lowercase:			Union[str, Any]       =     '''こんにちは、世界。'''
         _lowercase:			Dict       =     '''こんばんは、㔺界。😀'''
         _lowercase:			Union[str, Any]       =     '''こんにちは、世界。こんばんは、世界。😀'''
         _lowercase:			Optional[int]       =     tokenizer.encode(prefix_text + input_text       )
         _lowercase:			List[str]       =     tokenizer.encode('''''' ,				prefix_text=prefix_text + input_text       )
         _lowercase:			Tuple       =     tokenizer.encode(A_ ,				prefix_text=A_       )
         _lowercase:			Any       =     tokenizer.decode(A_       )
         _lowercase:			Optional[int]       =     tokenizer.decode(A_       )
         _lowercase:			List[str]       =     tokenizer.decode(A_       )
         self.assertEqual(A_ ,				A_       )
         self.assertEqual(A_ ,				A_       )
         self.assertEqual(A_ ,				A_       )
       @slow
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			str       =     self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''       )

         # Testing tokenization
         _lowercase:			str       =     '''こんにちは、世界。'''
         _lowercase:			str       =     '''こんばんは、㔺界。😀'''

         _lowercase:			List[str]       =     len(tokenizer.encode(A_       )       ) - 2
         _lowercase:			List[Any]       =     len(tokenizer.encode(A_       )       ) - 2

         _lowercase:			Any       =     [1] + [0] * (len_prefix + len_text + 1)
         _lowercase:			Dict       =     [1] * (len_prefix + len_text + 1) + [0]
         _lowercase:			Union[str, Any]       =     [1] + [1] * (len_prefix) + [0] * (len_text + 1)

         _lowercase:			Optional[int]       =     tokenizer(prefix_text + input_text       ).token_type_ids
         _lowercase:			Union[str, Any]       =     tokenizer('''''' ,				prefix_text=prefix_text + input_text       ).token_type_ids
         _lowercase:			Dict       =     tokenizer(A_ ,				prefix_text=A_       ).token_type_ids
         self.assertListEqual(A_ ,				A_       )
         self.assertListEqual(A_ ,				A_       )
         self.assertListEqual(A_ ,				A_       )
       @slow
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''       )

         _lowercase:			Optional[Any]       =     tokenizer.encode('''あンいワ'''       )
         _lowercase:			int       =     tokenizer.encode('''''' ,				prefix_text='''あンいワ'''       )
         _lowercase:			Tuple       =     tokenizer.encode('''いワ''' ,				prefix_text='''あン'''       )

         self.assertEqual(tokenizer.decode(A_       ) ,				tokenizer.decode(A_       )       )
         self.assertEqual(tokenizer.decode(A_       ) ,				tokenizer.decode(A_       )       )
         self.assertNotEqual(A_ ,				A_       )
         self.assertNotEqual(A_ ,				A_       )
         self.assertEqual(x_token_a[1] ,				x_token_a[-1]       )  # SEG token
         self.assertEqual(x_token_a[1] ,				x_token_a[3]       )  # SEG token
       @slow
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''       )

         _lowercase:			Dict       =     [['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
         _lowercase:			Tuple       =     tokenizer(A_ ,				padding=A_       )
         _lowercase:			Optional[int]       =     tokenizer.batch_encode_plus(A_ ,				padding=A_       )

         # fmt: off
         _lowercase:			Union[str, Any]       =     [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
         _lowercase:			Optional[int]       =     [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
         _lowercase:			Optional[Any]       =     [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
         # fmt: on
         self.assertListEqual(x_token.input_ids ,				A_       )
         self.assertListEqual(x_token.token_type_ids ,				A_       )
         self.assertListEqual(x_token.attention_mask ,				A_       )
         self.assertListEqual(x_token_a.input_ids ,				A_       )
         self.assertListEqual(x_token_a.token_type_ids ,				A_       )
         self.assertListEqual(x_token_a.attention_mask ,				A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         pass



       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         pass


"""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)
