



'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		_A				:    str     						='''platform'''

		import jax
		import jax.numpy as jnp

		from transformers import BlenderbotTokenizer
		from transformers.models.blenderbot.modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    shift_tokens_right,
		)







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase=None,				_lowercase=None,				_lowercase=None,				_lowercase=None,				_lowercase=None,				_lowercase=None,				) ->			Union[str, Any]:
	if attention_mask is None:
		_lowercase					:       Union[str, Any]       =		np.where(input_ids != config.pad_token_id,				1,				0					)
	if decoder_attention_mask is None:
		_lowercase					:       Any       =		np.where(decoder_input_ids != config.pad_token_id,				1,				0					)
	if head_mask is None:
		_lowercase					:       int       =		np.ones((config.encoder_layers, config.encoder_attention_heads)					)
	if decoder_head_mask is None:
		_lowercase					:       List[str]       =		np.ones((config.decoder_layers, config.decoder_attention_heads)					)
	if cross_attn_head_mask is None:
		_lowercase					:       int       =		np.ones((config.decoder_layers, config.decoder_attention_heads)					)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	}



class 		lowerCamelCase__      :




	'''simple docstring'''



	def __init__(  self				:	List[Any]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	Union[str, Any]=13     ,  UpperCamelCase_				:	Tuple=7     ,  UpperCamelCase_				:	Optional[Any]=True     ,  UpperCamelCase_				:	Any=False     ,  UpperCamelCase_				:	Dict=99     ,  UpperCamelCase_				:	int=16     ,  UpperCamelCase_				:	Optional[Any]=2     ,  UpperCamelCase_				:	List[str]=4     ,  UpperCamelCase_				:	Optional[Any]=4     ,  UpperCamelCase_				:	Dict="gelu"     ,  UpperCamelCase_				:	List[Any]=0.1     ,  UpperCamelCase_				:	Optional[Any]=0.1     ,  UpperCamelCase_				:	Union[str, Any]=32     ,  UpperCamelCase_				:	Optional[int]=2     ,  UpperCamelCase_				:	str=1     ,  UpperCamelCase_				:	str=0     ,  UpperCamelCase_				:	List[Any]=0.02     ,  )  ->       Union[str, Any]:



		'''simple docstring'''





		_lowercase					:       Optional[Any]       =		parent
		_lowercase					:       int       =		batch_size
		_lowercase					:       Tuple       =		seq_length
		_lowercase					:       Any       =		is_training
		_lowercase					:       List[str]       =		use_labels
		_lowercase					:       Optional[Any]       =		vocab_size
		_lowercase					:       Dict       =		hidden_size
		_lowercase					:       Tuple       =		num_hidden_layers
		_lowercase					:       Union[str, Any]       =		num_attention_heads
		_lowercase					:       int       =		intermediate_size
		_lowercase					:       Optional[int]       =		hidden_act
		_lowercase					:       List[Any]       =		hidden_dropout_prob
		_lowercase					:       Tuple       =		attention_probs_dropout_prob
		_lowercase					:       Dict       =		max_position_embeddings
		_lowercase					:       int       =		eos_token_id
		_lowercase					:       Dict       =		pad_token_id
		_lowercase					:       Optional[int]       =		bos_token_id
		_lowercase					:       Dict       =		initializer_range




	def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Union[str, Any]:



		'''simple docstring'''





		_lowercase					:       List[Any]       =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,  self.vocab_size					)     ,  3     ,  self.vocab_size					)
		_lowercase					:       Tuple       =		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,  dtype=np.intaa					))     ,  -1					)

		_lowercase					:       str       =		shift_tokens_right(UpperCamelCase_     ,  1     ,  2					)

		_lowercase					:       Union[str, Any]       =		BlenderbotConfig(
		    vocab_size=self.vocab_size     ,  d_model=self.hidden_size     ,  encoder_layers=self.num_hidden_layers     ,  decoder_layers=self.num_hidden_layers     ,  encoder_attention_heads=self.num_attention_heads     ,  decoder_attention_heads=self.num_attention_heads     ,  encoder_ffn_dim=self.intermediate_size     ,  decoder_ffn_dim=self.intermediate_size     ,  dropout=self.hidden_dropout_prob     ,  attention_dropout=self.attention_probs_dropout_prob     ,  max_position_embeddings=self.max_position_embeddings     ,  eos_token_id=self.eos_token_id     ,  bos_token_id=self.bos_token_id     ,  pad_token_id=self.pad_token_id     ,  initializer_range=self.initializer_range     ,  use_cache=UpperCamelCase_     ,  )
		_lowercase					:       Dict       =		prepare_blenderbot_inputs_dict(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
		return config, inputs_dict




	def 						__UpperCAmelCase     (  self				:	Any					)  ->       Tuple:



		'''simple docstring'''





		_lowercase  ,    _lowercase					:       List[str]       =		self.prepare_config_and_inputs()
		return config, inputs_dict




	def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	List[str]					)  ->       Any:



		'''simple docstring'''





		_lowercase					:       List[str]       =		20
		_lowercase					:       Tuple       =		model_class_name(UpperCamelCase_					)

		_lowercase					:       str       =		model.encode(inputs_dict['input_ids']					)

		_lowercase  ,    _lowercase					:       str       =		(
		    inputs_dict['decoder_input_ids'],
		    inputs_dict['decoder_attention_mask'],
		)

		_lowercase					:       List[Any]       =		model.init_cache(decoder_input_ids.shape[0]     ,  UpperCamelCase_     ,  UpperCamelCase_					)
		_lowercase					:       List[str]       =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,  dtype='i4'					)

		_lowercase					:       Optional[int]       =		jnp.broadcast_to(
		    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]     ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,  )
		_lowercase					:       List[Any]       =		model.decode(
		    decoder_input_ids[:, :-1]     ,  UpperCamelCase_     ,  decoder_attention_mask=UpperCamelCase_     ,  past_key_values=UpperCamelCase_     ,  decoder_position_ids=UpperCamelCase_     ,  )

		_lowercase					:       str       =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,  dtype='i4'					)
		_lowercase					:       Tuple       =		model.decode(
		    decoder_input_ids[:, -1:]     ,  UpperCamelCase_     ,  decoder_attention_mask=UpperCamelCase_     ,  past_key_values=outputs_cache.past_key_values     ,  decoder_position_ids=UpperCamelCase_     ,  )

		_lowercase					:       Optional[Any]       =		model.decode(UpperCamelCase_     ,  UpperCamelCase_					)

		_lowercase					:       Optional[int]       =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
		self.parent.assertTrue(diff < 1E-3     ,  msg=F'''Max diff is {diff}'''					)




	def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Optional[Any]					)  ->       Dict:



		'''simple docstring'''





		_lowercase					:       Optional[int]       =		20
		_lowercase					:       Any       =		model_class_name(UpperCamelCase_					)

		_lowercase					:       List[str]       =		model.encode(inputs_dict['input_ids']					)

		_lowercase  ,    _lowercase					:       int       =		(
		    inputs_dict['decoder_input_ids'],
		    inputs_dict['decoder_attention_mask'],
		)

		_lowercase					:       Any       =		jnp.concatenate(
		    [
		        decoder_attention_mask,
		        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
		    ]     ,  axis=-1     ,  )

		_lowercase					:       str       =		model.init_cache(decoder_input_ids.shape[0]     ,  UpperCamelCase_     ,  UpperCamelCase_					)
		_lowercase					:       str       =		jnp.broadcast_to(
		    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]     ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,  )

		_lowercase					:       Tuple       =		model.decode(
		    decoder_input_ids[:, :-1]     ,  UpperCamelCase_     ,  decoder_attention_mask=UpperCamelCase_     ,  past_key_values=UpperCamelCase_     ,  decoder_position_ids=UpperCamelCase_     ,  )
		_lowercase					:       Dict       =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,  dtype='i4'					)
		_lowercase					:       Any       =		model.decode(
		    decoder_input_ids[:, -1:]     ,  UpperCamelCase_     ,  past_key_values=outputs_cache.past_key_values     ,  decoder_attention_mask=UpperCamelCase_     ,  decoder_position_ids=UpperCamelCase_     ,  )

		_lowercase					:       int       =		model.decode(UpperCamelCase_     ,  UpperCamelCase_     ,  decoder_attention_mask=UpperCamelCase_					)

		_lowercase					:       Union[str, Any]       =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
		self.parent.assertTrue(diff < 1E-3     ,  msg=F'''Max diff is {diff}'''					)






@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




	'''simple docstring'''



	A_						    =						99




	def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       str:



		'''simple docstring'''





		_lowercase					:       List[Any]       =		np.array(
		    [
		        [71, 82, 18, 33, 46, 91, 2],
		        [68, 34, 26, 58, 30, 82, 2],
		        [5, 97, 17, 39, 94, 40, 2],
		        [76, 83, 94, 25, 70, 78, 2],
		        [87, 59, 41, 35, 48, 66, 2],
		        [55, 13, 16, 58, 5, 2, 1],  # note padding
		        [64, 27, 31, 51, 12, 75, 2],
		        [52, 64, 86, 17, 83, 39, 2],
		        [48, 61, 9, 24, 71, 82, 2],
		        [26, 1, 60, 48, 22, 13, 2],
		        [21, 5, 62, 28, 14, 76, 2],
		        [45, 98, 37, 86, 59, 48, 2],
		        [70, 70, 50, 9, 28, 0, 2],
		    ]     ,  dtype=np.intaa     ,  )

		_lowercase					:       Optional[Any]       =		input_ids.shape[0]
		_lowercase					:       List[Any]       =		BlenderbotConfig(
		    vocab_size=self.vocab_size     ,  d_model=24     ,  encoder_layers=2     ,  decoder_layers=2     ,  encoder_attention_heads=2     ,  decoder_attention_heads=2     ,  encoder_ffn_dim=32     ,  decoder_ffn_dim=32     ,  max_position_embeddings=48     ,  eos_token_id=2     ,  pad_token_id=1     ,  bos_token_id=0     ,  )
		return config, input_ids, batch_size




	def 						__UpperCAmelCase     (  self				:	int					)  ->       Optional[int]:



		'''simple docstring'''





		_lowercase  ,    _lowercase  ,    _lowercase					:       Dict       =		self._get_config_and_data()
		_lowercase					:       str       =		FlaxBlenderbotForConditionalGeneration(UpperCamelCase_					)
		_lowercase					:       Optional[int]       =		lm_model(input_ids=UpperCamelCase_					)
		_lowercase					:       Any       =		(batch_size, input_ids.shape[1], config.vocab_size)
		self.assertEqual(outputs['logits'].shape     ,  UpperCamelCase_					)




	def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Optional[int]:



		'''simple docstring'''





		_lowercase					:       Any       =		BlenderbotConfig(
		    vocab_size=self.vocab_size     ,  d_model=14     ,  encoder_layers=2     ,  decoder_layers=2     ,  encoder_attention_heads=2     ,  decoder_attention_heads=2     ,  encoder_ffn_dim=8     ,  decoder_ffn_dim=8     ,  max_position_embeddings=48     ,  )
		_lowercase					:       Any       =		FlaxBlenderbotForConditionalGeneration(UpperCamelCase_					)
		_lowercase					:       Tuple       =		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,  dtype=np.intaa					)
		_lowercase					:       Optional[Any]       =		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,  dtype=np.intaa					)
		_lowercase					:       str       =		lm_model(input_ids=UpperCamelCase_     ,  decoder_input_ids=UpperCamelCase_					)
		_lowercase					:       Any       =		(*summary.shape, config.vocab_size)
		self.assertEqual(outputs['logits'].shape     ,  UpperCamelCase_					)




	def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Any:



		'''simple docstring'''





		_lowercase					:       List[Any]       =		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,  dtype=np.intaa					)
		_lowercase					:       Optional[Any]       =		shift_tokens_right(UpperCamelCase_     ,  1     ,  2					)
		_lowercase					:       Union[str, Any]       =		np.equal(UpperCamelCase_     ,  1					).astype(np.floataa					).sum()
		_lowercase					:       Optional[int]       =		np.equal(UpperCamelCase_     ,  1					).astype(np.floataa					).sum()
		self.assertEqual(shifted.shape     ,  input_ids.shape					)
		self.assertEqual(UpperCamelCase_     ,  n_pad_before - 1					)
		self.assertTrue(np.equal(shifted[:, 0]     ,  2					).all()					)






@require_flax
class 		lowerCamelCase__      (						lowercase__	,   unittest.TestCase	,   lowercase__     ):




	'''simple docstring'''



	A_						    =						True
	A_						    =						(
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	A_						    =						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




	def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       int:



		'''simple docstring'''





		_lowercase					:       Any       =		FlaxBlenderbotModelTester(self					)




	def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       str:



		'''simple docstring'''





		_lowercase  ,    _lowercase					:       Union[str, Any]       =		self.model_tester.prepare_config_and_inputs()
		for model_class in self.all_model_classes:
			self.model_tester.check_use_cache_forward(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)




	def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[int]:



		'''simple docstring'''





		_lowercase  ,    _lowercase					:       Optional[Any]       =		self.model_tester.prepare_config_and_inputs()
		for model_class in self.all_model_classes:
			self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)




	def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Dict:



		'''simple docstring'''





		_lowercase  ,    _lowercase					:       Tuple       =		self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__					):
				_lowercase					:       List[Any]       =		self._prepare_for_class(UpperCamelCase_     ,  UpperCamelCase_					)
				_lowercase					:       Optional[Any]       =		model_class(UpperCamelCase_					)

				@jax.jit
				def encode_jitted(UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	Tuple=None     ,  **UpperCamelCase_				:	Optional[Any]					):
					return model.encode(input_ids=UpperCamelCase_     ,  attention_mask=UpperCamelCase_					)

				with self.subTest('JIT Enabled'					):
					_lowercase					:       Optional[int]       =		encode_jitted(**UpperCamelCase_					).to_tuple()

				with self.subTest('JIT Disabled'					):
					with jax.disable_jit():
						_lowercase					:       Optional[int]       =		encode_jitted(**UpperCamelCase_					).to_tuple()

				self.assertEqual(len(UpperCamelCase_					)     ,  len(UpperCamelCase_					)					)
				for jitted_output, output in zip(UpperCamelCase_     ,  UpperCamelCase_					):
					self.assertEqual(jitted_output.shape     ,  output.shape					)




	def 						__UpperCAmelCase     (  self				:	str					)  ->       List[str]:



		'''simple docstring'''





		_lowercase  ,    _lowercase					:       Tuple       =		self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__					):
				_lowercase					:       str       =		model_class(UpperCamelCase_					)
				_lowercase					:       List[Any]       =		model.encode(inputs_dict['input_ids']     ,  inputs_dict['attention_mask']					)

				_lowercase					:       Union[str, Any]       =		{
				    'decoder_input_ids': inputs_dict['decoder_input_ids'],
				    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
				    'encoder_outputs': encoder_outputs,
				}

				@jax.jit
				def decode_jitted(UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	List[str]					):
					return model.decode(
					    decoder_input_ids=UpperCamelCase_     ,  decoder_attention_mask=UpperCamelCase_     ,  encoder_outputs=UpperCamelCase_     ,  )

				with self.subTest('JIT Enabled'					):
					_lowercase					:       int       =		decode_jitted(**UpperCamelCase_					).to_tuple()

				with self.subTest('JIT Disabled'					):
					with jax.disable_jit():
						_lowercase					:       Dict       =		decode_jitted(**UpperCamelCase_					).to_tuple()

				self.assertEqual(len(UpperCamelCase_					)     ,  len(UpperCamelCase_					)					)
				for jitted_output, output in zip(UpperCamelCase_     ,  UpperCamelCase_					):
					self.assertEqual(jitted_output.shape     ,  output.shape					)




	@slow
	def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Tuple:



		'''simple docstring'''





		for model_class_name in self.all_model_classes:
			_lowercase					:       Any       =		model_class_name.from_pretrained('facebook/blenderbot-400M-distill'					)
			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
			_lowercase					:       List[Any]       =		np.ones((1, 1)					) * model.config.eos_token_id
			_lowercase					:       Union[str, Any]       =		model(UpperCamelCase_					)
			self.assertIsNotNone(UpperCamelCase_					)




	@unittest.skipUnless(jax_device != 'cpu'     ,  '3B test too slow on CPU.'					)
	@slow
	def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       List[str]:



		'''simple docstring'''





		_lowercase					:       Union[str, Any]       =		{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
		_lowercase					:       Union[str, Any]       =		{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

		_lowercase					:       Any       =		FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'     ,  from_pt=UpperCamelCase_					)
		_lowercase					:       int       =		BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'					)

		_lowercase					:       Any       =		['Sam']
		_lowercase					:       Any       =		tokenizer(UpperCamelCase_     ,  return_tensors='jax'					)

		_lowercase					:       Any       =		model.generate(**UpperCamelCase_     ,  **UpperCamelCase_					)
		_lowercase					:       List[str]       =		'Sam is a great name. It means \"sun\" in Gaelic.'

		_lowercase					:       int       =		tokenizer.batch_decode(UpperCamelCase_     ,  **UpperCamelCase_					)
		assert generated_txt[0].strip() == tgt_text





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A				:    Union[str, Any]     						={'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Dict     						=['''ReformerTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Union[str, Any]     						=['''ReformerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=[
		    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ReformerAttention''',
		    '''ReformerForMaskedLM''',
		    '''ReformerForQuestionAnswering''',
		    '''ReformerForSequenceClassification''',
		    '''ReformerLayer''',
		    '''ReformerModel''',
		    '''ReformerModelWithLMHead''',
		    '''ReformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		_A				:    Union[str, Any]     						=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
