




import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class     __a(	_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = GPTaTokenizer
    lowerCAmelCase         = GPTaTokenizerFast
    lowerCAmelCase         = True
    lowerCAmelCase         = {'''add_prefix_space''': True}
    lowerCAmelCase         = False





    def 				a__ ( self    )					->						int:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         UpperCAmelCase_     :				Optional[Any]  =     [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
             '''<|endoftext|>''',
         ]
         UpperCAmelCase_     :				Optional[Any]  =     dict(zip(_SCREAMING_SNAKE_CASE  ,range(len(_SCREAMING_SNAKE_CASE    )    )    )    )
         UpperCAmelCase_     :				int  =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         UpperCAmelCase_     :				Optional[int]  =     {'''unk_token''': '''<unk>'''}

         UpperCAmelCase_     :				str  =     os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['''vocab_file''']    )
         UpperCAmelCase_     :				str  =     os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['''merges_file''']    )
         with open(self.vocab_file  ,'''w'''  ,encoding='''utf-8'''    ) as fp:
              fp.write(json.dumps(_SCREAMING_SNAKE_CASE    ) + '''\n'''    )
         with open(self.merges_file  ,'''w'''  ,encoding='''utf-8'''    ) as fp:
              fp.write('''\n'''.join(_SCREAMING_SNAKE_CASE    )    )





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						str:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizer.from_pretrained(self.tmpdirname  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						str:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizerFast.from_pretrained(self.tmpdirname  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						List[str]:
         UpperCAmelCase_     :				Optional[int]  =     '''lower newer'''
         UpperCAmelCase_     :				List[str]  =     '''lower newer'''
         return input_text, output_text





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				List[str]  =     GPTaTokenizer(self.vocab_file  ,self.merges_file  ,**self.special_tokens_map    )
         UpperCAmelCase_     :				int  =     '''lower newer'''
         UpperCAmelCase_     :				Tuple  =     ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         UpperCAmelCase_     :				Dict  =     tokenizer.tokenize(_SCREAMING_SNAKE_CASE  ,add_prefix_space=_SCREAMING_SNAKE_CASE    )
         self.assertListEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Any  =     tokens + [tokenizer.unk_token]
         UpperCAmelCase_     :				Optional[Any]  =     [14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE    )  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Any:
         if not self.test_rust_tokenizer:
              return

         UpperCAmelCase_     :				Union[str, Any]  =     self.get_tokenizer()
         UpperCAmelCase_     :				int  =     self.get_rust_tokenizer(add_prefix_space=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Dict  =     '''lower newer'''

         # Testing tokenization
         UpperCAmelCase_     :				Dict  =     tokenizer.tokenize(_SCREAMING_SNAKE_CASE  ,add_prefix_space=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE    )
         self.assertListEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         # Testing conversion to ids without special tokens
         UpperCAmelCase_     :				Any  =     tokenizer.encode(_SCREAMING_SNAKE_CASE  ,add_special_tokens=_SCREAMING_SNAKE_CASE  ,add_prefix_space=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     rust_tokenizer.encode(_SCREAMING_SNAKE_CASE  ,add_special_tokens=_SCREAMING_SNAKE_CASE    )
         self.assertListEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         # Testing conversion to ids with special tokens
         UpperCAmelCase_     :				str  =     self.get_rust_tokenizer(add_prefix_space=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     tokenizer.encode(_SCREAMING_SNAKE_CASE  ,add_prefix_space=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     rust_tokenizer.encode(_SCREAMING_SNAKE_CASE    )
         self.assertListEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         # Testing the unknown token
         UpperCAmelCase_     :				Dict  =     tokens + [rust_tokenizer.unk_token]
         UpperCAmelCase_     :				List[Any]  =     [14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE    )  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						List[str]:
         # It's very difficult to mix/test pretokenization with byte-level
         # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
         pass





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE=15    )					->						int:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                   UpperCAmelCase_     :				Union[str, Any]  =     self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )

                   # Simple input
                   UpperCAmelCase_     :				str  =     '''This is a simple input'''
                   UpperCAmelCase_     :				str  =     ['''This is a simple input 1''', '''This is a simple input 2''']
                   UpperCAmelCase_     :				int  =     ('''This is a simple input''', '''This is a pair''')
                   UpperCAmelCase_     :				Any  =     [
                       ('''This is a simple input 1''', '''This is a simple input 2'''),
                       ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                   ]

                   # Simple input tests
                   self.assertRaises(_SCREAMING_SNAKE_CASE  ,tokenizer_r.encode  ,_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,padding='''max_length'''    )

                   # Simple input
                   self.assertRaises(_SCREAMING_SNAKE_CASE  ,tokenizer_r.encode_plus  ,_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,padding='''max_length'''    )

                   # Simple input
                   self.assertRaises(
                       _SCREAMING_SNAKE_CASE  ,tokenizer_r.batch_encode_plus  ,_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,padding='''max_length'''  ,)

                   # Pair input
                   self.assertRaises(_SCREAMING_SNAKE_CASE  ,tokenizer_r.encode  ,_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,padding='''max_length'''    )

                   # Pair input
                   self.assertRaises(_SCREAMING_SNAKE_CASE  ,tokenizer_r.encode_plus  ,_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,padding='''max_length'''    )

                   # Pair input
                   self.assertRaises(
                       _SCREAMING_SNAKE_CASE  ,tokenizer_r.batch_encode_plus  ,_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,padding='''max_length'''  ,)





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Tuple  =     GPTaTokenizer.from_pretrained(self.tmpdirname  ,pad_token='''<pad>'''    )

         # Simple input
         UpperCAmelCase_     :				str  =     '''This is a simple input'''
         UpperCAmelCase_     :				Tuple  =     ['''This is a simple input looooooooong''', '''This is a simple input''']
         UpperCAmelCase_     :				Union[str, Any]  =     ('''This is a simple input''', '''This is a pair''')
         UpperCAmelCase_     :				Optional[Any]  =     [
             ('''This is a simple input loooooong''', '''This is a simple input'''),
             ('''This is a simple pair loooooong''', '''This is a simple pair'''),
         ]

         UpperCAmelCase_     :				Union[str, Any]  =     tokenizer.pad_token_id

         UpperCAmelCase_     :				Dict  =     tokenizer(_SCREAMING_SNAKE_CASE  ,padding='''max_length'''  ,max_length=30  ,return_tensors='''np'''    )
         UpperCAmelCase_     :				int  =     tokenizer(_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncate=_SCREAMING_SNAKE_CASE  ,return_tensors='''np'''    )
         UpperCAmelCase_     :				Tuple  =     tokenizer(*_SCREAMING_SNAKE_CASE  ,padding='''max_length'''  ,max_length=60  ,return_tensors='''np'''    )
         UpperCAmelCase_     :				List[Any]  =     tokenizer(_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncate=_SCREAMING_SNAKE_CASE  ,return_tensors='''np'''    )

         # s
         # test single string max_length padding
         self.assertEqual(out_s['''input_ids'''].shape[-1]  ,30    )
         self.assertTrue(pad_token_id in out_s['''input_ids''']    )
         self.assertTrue(0 in out_s['''attention_mask''']    )

         # s2
         # test automatic padding
         self.assertEqual(out_sa['''input_ids'''].shape[-1]  ,33    )
         # long slice doesn't have padding
         self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]    )
         self.assertFalse(0 in out_sa['''attention_mask'''][0]    )
         # short slice does have padding
         self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]    )
         self.assertTrue(0 in out_sa['''attention_mask'''][1]    )

         # p
         # test single pair max_length padding
         self.assertEqual(out_p['''input_ids'''].shape[-1]  ,60    )
         self.assertTrue(pad_token_id in out_p['''input_ids''']    )
         self.assertTrue(0 in out_p['''attention_mask''']    )

         # p2
         # test automatic padding pair
         self.assertEqual(out_pa['''input_ids'''].shape[-1]  ,52    )
         # long slice pair doesn't have padding
         self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]    )
         self.assertFalse(0 in out_pa['''attention_mask'''][0]    )
         # short slice pair does have padding
         self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]    )
         self.assertTrue(0 in out_pa['''attention_mask'''][1]    )





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				List[Any]  =     '''$$$'''
         UpperCAmelCase_     :				Any  =     GPTaTokenizer.from_pretrained(self.tmpdirname  ,bos_token=_SCREAMING_SNAKE_CASE  ,add_bos_token=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Tuple  =     '''This is a simple input'''
         UpperCAmelCase_     :				Union[str, Any]  =     ['''This is a simple input 1''', '''This is a simple input 2''']

         UpperCAmelCase_     :				List[Any]  =     tokenizer.bos_token_id

         UpperCAmelCase_     :				List[str]  =     tokenizer(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[Any]  =     tokenizer(_SCREAMING_SNAKE_CASE    )

         self.assertEqual(out_s.input_ids[0]  ,_SCREAMING_SNAKE_CASE    )
         self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids    )    )

         UpperCAmelCase_     :				Tuple  =     tokenizer.decode(out_s.input_ids    )
         UpperCAmelCase_     :				Union[str, Any]  =     tokenizer.batch_decode(out_sa.input_ids    )

         self.assertEqual(decode_s.split()[0]  ,_SCREAMING_SNAKE_CASE    )
         self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa    )    )





    def 				a__ ( self    )					->						str:
         pass





    def 				a__ ( self    )					->						int:
         # TODO: change to self.get_tokenizers() when the fast version is implemented
         UpperCAmelCase_     :				Tuple  =     [self.get_tokenizer(do_lower_case=_SCREAMING_SNAKE_CASE  ,add_bos_token=_SCREAMING_SNAKE_CASE    )]
         for tokenizer in tokenizers:
              with self.subTest(f'''{tokenizer.__class__.__name__}'''    ):
                   UpperCAmelCase_     :				List[Any]  =     '''Encode this.'''
                   UpperCAmelCase_     :				List[Any]  =     '''This one too please.'''
                   UpperCAmelCase_     :				Optional[int]  =     tokenizer.encode(_SCREAMING_SNAKE_CASE  ,add_special_tokens=_SCREAMING_SNAKE_CASE    )
                   encoded_sequence += tokenizer.encode(_SCREAMING_SNAKE_CASE  ,add_special_tokens=_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Dict  =     tokenizer.encode_plus(
                       _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,add_special_tokens=_SCREAMING_SNAKE_CASE  ,return_special_tokens_mask=_SCREAMING_SNAKE_CASE  ,)
                   UpperCAmelCase_     :				int  =     encoded_sequence_dict['''input_ids''']
                   UpperCAmelCase_     :				Union[str, Any]  =     encoded_sequence_dict['''special_tokens_mask''']
                   self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,len(_SCREAMING_SNAKE_CASE    )    )

                   UpperCAmelCase_     :				List[str]  =     [
                       (x if not special_tokens_mask[i] else None) for i, x in enumerate(_SCREAMING_SNAKE_CASE    )
                   ]
                   UpperCAmelCase_     :				str  =     [x for x in filtered_sequence if x is not None]
                   self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )


@require_tokenizers
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def 				a__ ( self    )					->						List[Any]:
         # More context:
         # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
         # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
         # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

         UpperCAmelCase_     :				Any  =     AutoTokenizer.from_pretrained('''facebook/opt-350m'''  ,from_slow=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[str]  =     '''A photo of a cat'''

         UpperCAmelCase_     :				Any  =     tokenizer.encode(
             _SCREAMING_SNAKE_CASE  ,)
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,[2, 250, 1_345, 9, 10, 4_758]    )
         tokenizer.save_pretrained('''test_opt'''    )

         UpperCAmelCase_     :				Dict  =     AutoTokenizer.from_pretrained('''./test_opt'''    )
         UpperCAmelCase_     :				Optional[Any]  =     tokenizer.encode(
             _SCREAMING_SNAKE_CASE  ,)
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,[2, 250, 1_345, 9, 10, 4_758]    )





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				Tuple  =     AutoTokenizer.from_pretrained('''facebook/opt-350m'''  ,use_slow=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Dict  =     '''A photo of a cat'''

         UpperCAmelCase_     :				int  =     tokenizer.encode(
             _SCREAMING_SNAKE_CASE  ,)
         # Same as above
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,[2, 250, 1_345, 9, 10, 4_758]    )





    @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''    )
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Union[str, Any]  =     AutoTokenizer.from_pretrained('''facebook/opt-350m'''  ,from_slow=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Any  =     '''bos'''
         UpperCAmelCase_     :				Dict  =     tokenizer.get_vocab()['''bos''']

         UpperCAmelCase_     :				Optional[int]  =     '''A photo of a cat'''
         UpperCAmelCase_     :				Any  =     tokenizer.encode(
             _SCREAMING_SNAKE_CASE  ,)
         # We changed the bos token
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,[31_957, 250, 1_345, 9, 10, 4_758]    )
         tokenizer.save_pretrained('''./tok'''    )
         UpperCAmelCase_     :				Optional[Any]  =     AutoTokenizer.from_pretrained('''./tok'''    )
         self.assertTrue(tokenizer.is_fast    )
         UpperCAmelCase_     :				Tuple  =     tokenizer.encode(
             _SCREAMING_SNAKE_CASE  ,)
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,[31_957, 250, 1_345, 9, 10, 4_758]    )



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			:  Dict										=   {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  List[Any]										=   ['DistilBertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  int										=   [
     'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DistilBertForMaskedLM',
     'DistilBertForMultipleChoice',
     'DistilBertForQuestionAnswering',
     'DistilBertForSequenceClassification',
     'DistilBertForTokenClassification',
     'DistilBertModel',
     'DistilBertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[int]										=   [
     'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDistilBertForMaskedLM',
     'TFDistilBertForMultipleChoice',
     'TFDistilBertForQuestionAnswering',
     'TFDistilBertForSequenceClassification',
     'TFDistilBertForTokenClassification',
     'TFDistilBertMainLayer',
     'TFDistilBertModel',
     'TFDistilBertPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  List[str]										=   [
     'FlaxDistilBertForMaskedLM',
     'FlaxDistilBertForMultipleChoice',
     'FlaxDistilBertForQuestionAnswering',
     'FlaxDistilBertForSequenceClassification',
     'FlaxDistilBertForTokenClassification',
     'FlaxDistilBertModel',
     'FlaxDistilBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase_			:  Optional[Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






