

from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFFunnelBaseModel,
     TFFunnelForMaskedLM,
     TFFunnelForMultipleChoice,
     TFFunnelForPreTraining,
     TFFunnelForQuestionAnswering,
     TFFunnelForSequenceClassification,
     TFFunnelForTokenClassification,
     TFFunnelModel,
 )






class       __A :






    def __init__(		self	:			str			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			str=13			, UpperCAmelCase_	:			Any=7			, UpperCAmelCase_	:			List[str]=True			, UpperCAmelCase_	:			int=True			, UpperCAmelCase_	:			Any=True			, UpperCAmelCase_	:			List[Any]=True			, UpperCAmelCase_	:			Dict=99			, UpperCAmelCase_	:			List[str]=[1, 1, 2]			, UpperCAmelCase_	:			List[Any]=1			, UpperCAmelCase_	:			Dict=32			, UpperCAmelCase_	:			Optional[int]=4			, UpperCAmelCase_	:			str=8			, UpperCAmelCase_	:			Dict=37			, UpperCAmelCase_	:			Tuple="gelu_new"			, UpperCAmelCase_	:			List[Any]=0.1			, UpperCAmelCase_	:			Optional[Any]=0.1			, UpperCAmelCase_	:			Dict=0.0			, UpperCAmelCase_	:			Optional[int]=512			, UpperCAmelCase_	:			List[str]=3			, UpperCAmelCase_	:			Dict=0.02			, UpperCAmelCase_	:			int=3			, UpperCAmelCase_	:			Optional[Any]=4			, UpperCAmelCase_	:			Dict=None			, UpperCAmelCase_	:			Tuple=False			, ):
        lowerCAmelCase :	str				=					parent
        lowerCAmelCase :	Tuple				=					batch_size
        lowerCAmelCase :	Optional[int]				=					seq_length
        lowerCAmelCase :	Dict				=					is_training
        lowerCAmelCase :	Any				=					use_input_mask
        lowerCAmelCase :	List[str]				=					use_token_type_ids
        lowerCAmelCase :	List[Any]				=					use_labels
        lowerCAmelCase :	Optional[int]				=					vocab_size
        lowerCAmelCase :	str				=					block_sizes
        lowerCAmelCase :	Optional[int]				=					num_decoder_layers
        lowerCAmelCase :	Optional[Any]				=					d_model
        lowerCAmelCase :	Tuple				=					n_head
        lowerCAmelCase :	Optional[int]				=					d_head
        lowerCAmelCase :	Dict				=					d_inner
        lowerCAmelCase :	Optional[Any]				=					hidden_act
        lowerCAmelCase :	Dict				=					hidden_dropout
        lowerCAmelCase :	Optional[Any]				=					attention_dropout
        lowerCAmelCase :	Tuple				=					activation_dropout
        lowerCAmelCase :	Optional[int]				=					max_position_embeddings
        lowerCAmelCase :	int				=					type_vocab_size
        lowerCAmelCase :	Optional[Any]				=					2
        lowerCAmelCase :	List[Any]				=					num_labels
        lowerCAmelCase :	List[Any]				=					num_choices
        lowerCAmelCase :	Optional[int]				=					scope
        lowerCAmelCase :	str				=					initializer_std

        # Used in the tests to check the size of the first attention layer
        lowerCAmelCase :	int				=					n_head
        # Used in the tests to check the size of the first hidden state
        lowerCAmelCase :	Dict				=					self.d_model
        # Used in the tests to check the number of output hidden states/attentions
        lowerCAmelCase :	int				=					sum(self.block_sizes			) + (0 if base else self.num_decoder_layers)
        # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
        # the last hidden state of the first block (which is the first hidden state of the decoder).
        if not base:
            lowerCAmelCase :	Any				=					self.num_hidden_layers + 2






    def 		lowercase__       (		self	:			List[Any]			):
        lowerCAmelCase :	Optional[Any]				=					ids_tensor([self.batch_size, self.seq_length]			, self.vocab_size			)

        lowerCAmelCase :	Dict				=					None
        if self.use_input_mask:
            lowerCAmelCase :	Optional[int]				=					random_attention_mask([self.batch_size, self.seq_length]			)

        lowerCAmelCase :	int				=					None
        if self.use_token_type_ids:
            lowerCAmelCase :	Optional[int]				=					ids_tensor([self.batch_size, self.seq_length]			, self.type_vocab_size			)

        lowerCAmelCase :	Optional[Any]				=					None
        lowerCAmelCase :	List[str]				=					None
        lowerCAmelCase :	Tuple				=					None
        if self.use_labels:
            lowerCAmelCase :	str				=					ids_tensor([self.batch_size]			, self.type_sequence_label_size			)
            lowerCAmelCase :	Any				=					ids_tensor([self.batch_size, self.seq_length]			, self.num_labels			)
            lowerCAmelCase :	List[Any]				=					ids_tensor([self.batch_size]			, self.num_choices			)

        lowerCAmelCase :	Dict				=					FunnelConfig(
            vocab_size=self.vocab_size			, block_sizes=self.block_sizes			, num_decoder_layers=self.num_decoder_layers			, d_model=self.d_model			, n_head=self.n_head			, d_head=self.d_head			, d_inner=self.d_inner			, hidden_act=self.hidden_act			, hidden_dropout=self.hidden_dropout			, attention_dropout=self.attention_dropout			, activation_dropout=self.activation_dropout			, max_position_embeddings=self.max_position_embeddings			, type_vocab_size=self.type_vocab_size			, initializer_std=self.initializer_std			, )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        )






    def 		lowercase__       (		self	:			Any			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			Any			, UpperCAmelCase_	:			Tuple			, UpperCAmelCase_	:			List[str]			, UpperCAmelCase_	:			Union[str, Any]			, UpperCAmelCase_	:			str			, ):
        lowerCAmelCase :	List[Any]				=					TFFunnelModel(config=_UpperCamelCase			)
        lowerCAmelCase :	Optional[int]				=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        lowerCAmelCase :	Union[str, Any]				=					model(_UpperCamelCase			)

        lowerCAmelCase :	str				=					[input_ids, input_mask]
        lowerCAmelCase :	Optional[int]				=					model(_UpperCamelCase			)

        lowerCAmelCase :	str				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.d_model)			)

        lowerCAmelCase :	Union[str, Any]				=					False
        lowerCAmelCase :	Dict				=					TFFunnelModel(config=_UpperCamelCase			)
        lowerCAmelCase :	str				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.d_model)			)

        lowerCAmelCase :	Any				=					False
        lowerCAmelCase :	Dict				=					TFFunnelModel(config=_UpperCamelCase			)
        lowerCAmelCase :	Tuple				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.d_model)			)






    def 		lowercase__       (		self	:			str			, UpperCAmelCase_	:			Dict			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			str			, UpperCAmelCase_	:			Dict			, UpperCAmelCase_	:			List[str]			, UpperCAmelCase_	:			List[str]			, ):
        lowerCAmelCase :	List[Any]				=					TFFunnelBaseModel(config=_UpperCamelCase			)
        lowerCAmelCase :	int				=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        lowerCAmelCase :	List[str]				=					model(_UpperCamelCase			)

        lowerCAmelCase :	List[str]				=					[input_ids, input_mask]
        lowerCAmelCase :	Tuple				=					model(_UpperCamelCase			)

        lowerCAmelCase :	List[str]				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, 2, self.d_model)			)

        lowerCAmelCase :	List[str]				=					False
        lowerCAmelCase :	List[Any]				=					TFFunnelBaseModel(config=_UpperCamelCase			)
        lowerCAmelCase :	List[str]				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, 3, self.d_model)			)

        lowerCAmelCase :	Any				=					False
        lowerCAmelCase :	int				=					TFFunnelBaseModel(config=_UpperCamelCase			)
        lowerCAmelCase :	List[Any]				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, 2, self.d_model)			)






    def 		lowercase__       (		self	:			List[str]			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			Any			, UpperCAmelCase_	:			Union[str, Any]			, UpperCAmelCase_	:			Dict			, UpperCAmelCase_	:			Dict			, UpperCAmelCase_	:			Tuple			, UpperCAmelCase_	:			Any			, ):
        lowerCAmelCase :	Union[str, Any]				=					TFFunnelForPreTraining(config=_UpperCamelCase			)
        lowerCAmelCase :	Optional[Any]				=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        lowerCAmelCase :	Dict				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length)			)






    def 		lowercase__       (		self	:			str			, UpperCAmelCase_	:			Tuple			, UpperCAmelCase_	:			List[Any]			, UpperCAmelCase_	:			List[str]			, UpperCAmelCase_	:			List[Any]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			Any			, UpperCAmelCase_	:			List[Any]			, ):
        lowerCAmelCase :	Tuple				=					TFFunnelForMaskedLM(config=_UpperCamelCase			)
        lowerCAmelCase :	int				=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        lowerCAmelCase :	int				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.vocab_size)			)






    def 		lowercase__       (		self	:			Any			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			Union[str, Any]			, UpperCAmelCase_	:			Tuple			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			Union[str, Any]			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			int			, ):
        lowerCAmelCase :	List[str]				=					self.num_labels
        lowerCAmelCase :	Tuple				=					TFFunnelForSequenceClassification(config=_UpperCamelCase			)
        lowerCAmelCase :	Optional[int]				=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        lowerCAmelCase :	Union[str, Any]				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)			)






    def 		lowercase__       (		self	:			Any			, UpperCAmelCase_	:			List[Any]			, UpperCAmelCase_	:			List[Any]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			str			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			Union[str, Any]			, ):
        lowerCAmelCase :	str				=					self.num_choices
        lowerCAmelCase :	Dict				=					TFFunnelForMultipleChoice(config=_UpperCamelCase			)
        lowerCAmelCase :	Optional[int]				=					tf.tile(tf.expand_dims(_UpperCamelCase			, 1			)			, (1, self.num_choices, 1)			)
        lowerCAmelCase :	Optional[Any]				=					tf.tile(tf.expand_dims(_UpperCamelCase			, 1			)			, (1, self.num_choices, 1)			)
        lowerCAmelCase :	Union[str, Any]				=					tf.tile(tf.expand_dims(_UpperCamelCase			, 1			)			, (1, self.num_choices, 1)			)
        lowerCAmelCase :	List[str]				=					{
            'input_ids': multiple_choice_inputs_ids,
            'attention_mask': multiple_choice_input_mask,
            'token_type_ids': multiple_choice_token_type_ids,
        }
        lowerCAmelCase :	Union[str, Any]				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_choices)			)






    def 		lowercase__       (		self	:			List[Any]			, UpperCAmelCase_	:			Dict			, UpperCAmelCase_	:			Union[str, Any]			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			List[Any]			, UpperCAmelCase_	:			List[str]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			Optional[int]			, ):
        lowerCAmelCase :	Any				=					self.num_labels
        lowerCAmelCase :	List[Any]				=					TFFunnelForTokenClassification(config=_UpperCamelCase			)
        lowerCAmelCase :	List[Any]				=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        lowerCAmelCase :	int				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.num_labels)			)






    def 		lowercase__       (		self	:			Union[str, Any]			, UpperCAmelCase_	:			Union[str, Any]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			Tuple			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			Any			, UpperCAmelCase_	:			int			, ):
        lowerCAmelCase :	List[str]				=					TFFunnelForQuestionAnswering(config=_UpperCamelCase			)
        lowerCAmelCase :	Optional[int]				=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        lowerCAmelCase :	int				=					model(_UpperCamelCase			)
        self.parent.assertEqual(result.start_logits.shape			, (self.batch_size, self.seq_length)			)
        self.parent.assertEqual(result.end_logits.shape			, (self.batch_size, self.seq_length)			)






    def 		lowercase__       (		self	:			Optional[int]			):
        lowerCAmelCase :	List[str]				=					self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase  
        )	,     (
            lowerCAmelCase  
        )	,     (
            lowerCAmelCase  
        )	,     (
            lowerCAmelCase  
        )	,     (
            lowerCAmelCase  
        )	,     (
            lowerCAmelCase  
        )	,     (
            lowerCAmelCase  
        )	,       
        ) :	Union[str, Any]				=					config_and_inputs
        lowerCAmelCase :	Union[str, Any]				=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict






@require_tf
class       __A (							__a  ,  __a  ,  unittest.TestCase       ):
    lowerCAmelCase_							:						Dict						 =							(
        (
            TFFunnelModel,
            TFFunnelForMaskedLM,
            TFFunnelForPreTraining,
            TFFunnelForQuestionAnswering,
            TFFunnelForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    lowerCAmelCase_							:						Optional[Any]						 =							(
        {
            "feature-extraction": (TFFunnelBaseModel, TFFunnelModel),
            "fill-mask": TFFunnelForMaskedLM,
            "question-answering": TFFunnelForQuestionAnswering,
            "text-classification": TFFunnelForSequenceClassification,
            "token-classification": TFFunnelForTokenClassification,
            "zero-shot": TFFunnelForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    lowerCAmelCase_							:						Dict						 =							False
    lowerCAmelCase_							:						List[str]						 =							False






    def 		lowercase__       (		self	:			str			):
        lowerCAmelCase :	Optional[int]				=					TFFunnelModelTester(self			)
        lowerCAmelCase :	int				=					ConfigTester(self			, config_class=_UpperCamelCase			)






    def 		lowercase__       (		self	:			List[Any]			):
        self.config_tester.run_common_tests()






    def 		lowercase__       (		self	:			int			):
        lowerCAmelCase :	Optional[Any]				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCamelCase			)






    def 		lowercase__       (		self	:			List[Any]			):
        lowerCAmelCase :	str				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*_UpperCamelCase			)






    def 		lowercase__       (		self	:			List[Any]			):
        lowerCAmelCase :	List[Any]				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase			)






    def 		lowercase__       (		self	:			Union[str, Any]			):
        lowerCAmelCase :	Optional[Any]				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase			)






    def 		lowercase__       (		self	:			Optional[Any]			):
        lowerCAmelCase :	List[Any]				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase			)






@require_tf
class       __A (							__a  ,  unittest.TestCase       ):
    lowerCAmelCase_							:						int						 =							(
        (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
    )
    lowerCAmelCase_							:						Union[str, Any]						 =							False
    lowerCAmelCase_							:						Optional[Any]						 =							False






    def 		lowercase__       (		self	:			Dict			):
        lowerCAmelCase :	Tuple				=					TFFunnelModelTester(self			, base=_UpperCamelCase			)
        lowerCAmelCase :	List[str]				=					ConfigTester(self			, config_class=_UpperCamelCase			)






    def 		lowercase__       (		self	:			Dict			):
        self.config_tester.run_common_tests()






    def 		lowercase__       (		self	:			Dict			):
        lowerCAmelCase :	Tuple				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_base_model(*_UpperCamelCase			)






    def 		lowercase__       (		self	:			Dict			):
        lowerCAmelCase :	Tuple				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase			)






    def 		lowercase__       (		self	:			Dict			):
        lowerCAmelCase :	str				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase			)







from typing import TYPE_CHECKING

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


_A   =     {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["DistilBertTokenizerFast"]

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

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "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:
			_A   =     [
			    "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

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



