


'''simple docstring'''



import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.generation import (
		    FlaxForcedBOSTokenLogitsProcessor,
		    FlaxForcedEOSTokenLogitsProcessor,
		    FlaxLogitsProcessorList,
		    FlaxMinLengthLogitsProcessor,
		    FlaxTemperatureLogitsWarper,
		    FlaxTopKLogitsWarper,
		    FlaxTopPLogitsWarper,
		)


@require_flax
class 		_snake_case					(     unittest.TestCase	):




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[Any]        =     jnp.ones((batch_size, length)) / length
								return scores




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     None
								UpperCAmelCase__  :					int        =     20

								UpperCAmelCase__  :					str        =     self._get_uniform_logits(batch_size=2							,							length=_lowerCamelCase)

								# tweak scores to not be uniform anymore
								UpperCAmelCase__  :					Any        =     scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
								UpperCAmelCase__  :					Any        =     scores.at[1, 10].set((1 / length) - 0.4)  # valley, 1st batch

								# compute softmax
								UpperCAmelCase__  :					int        =     jax.nn.softmax(_lowerCamelCase							,							axis=-1)

								UpperCAmelCase__  :					int        =     FlaxTemperatureLogitsWarper(temperature=0.5)
								UpperCAmelCase__  :					int        =     FlaxTemperatureLogitsWarper(temperature=1.3)

								UpperCAmelCase__  :					str        =     jax.nn.softmax(temp_dist_warper_sharper(_lowerCamelCase							,							scores.copy()							,							cur_len=_lowerCamelCase)							,							axis=-1)
								UpperCAmelCase__  :					Dict        =     jax.nn.softmax(temp_dist_warper_smoother(_lowerCamelCase							,							scores.copy()							,							cur_len=_lowerCamelCase)							,							axis=-1)

								# uniform distribution stays uniform
								self.assertTrue(jnp.allclose(probs[0, :]							,							warped_prob_sharp[0, :]							,							atol=1e-3))
								self.assertTrue(jnp.allclose(probs[0, :]							,							warped_prob_smooth[0, :]							,							atol=1e-3))

								# sharp peaks get higher, valleys get lower
								self.assertLess(probs[1, :].max()							,							warped_prob_sharp[1, :].max())
								self.assertGreater(probs[1, :].min()							,							warped_prob_sharp[1, :].min())

								# smooth peaks get lower, valleys get higher
								self.assertGreater(probs[1, :].max()							,							warped_prob_smooth[1, :].max())
								self.assertLess(probs[1, :].min()							,							warped_prob_smooth[1, :].min())




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     None
								UpperCAmelCase__  :					List[Any]        =     10
								UpperCAmelCase__  :					List[str]        =     2

								# create ramp distribution
								UpperCAmelCase__  :					Optional[int]        =     np.broadcast_to(np.arange(_lowerCamelCase)[None, :]							,							(batch_size, vocab_size)).copy()
								UpperCAmelCase__  :					List[Any]        =     ramp_logits[1:, : vocab_size // 2] + vocab_size

								UpperCAmelCase__  :					List[Any]        =     FlaxTopKLogitsWarper(3)

								UpperCAmelCase__  :					Tuple        =     top_k_warp(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)

								# check that correct tokens are filtered
								self.assertListEqual(jnp.isinf(scores[0]).tolist()							,							7 * [True] + 3 * [False])
								self.assertListEqual(jnp.isinf(scores[1]).tolist()							,							2 * [True] + 3 * [False] + 5 * [True])

								# check special case
								UpperCAmelCase__  :					List[Any]        =     5
								UpperCAmelCase__  :					int        =     FlaxTopKLogitsWarper(top_k=1							,							filter_value=0.0							,							min_tokens_to_keep=3)

								UpperCAmelCase__  :					Union[str, Any]        =     np.broadcast_to(np.arange(_lowerCamelCase)[None, :]							,							(batch_size, length)).copy()
								UpperCAmelCase__  :					int        =     top_k_warp_safety_check(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)

								# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
								self.assertListEqual((scores == 0.0).sum(axis=-1).tolist()							,							[2, 2])




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[int]        =     None
								UpperCAmelCase__  :					str        =     10
								UpperCAmelCase__  :					List[Any]        =     2

								# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
								UpperCAmelCase__  :					str        =     np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

								UpperCAmelCase__  :					Union[str, Any]        =     FlaxTopPLogitsWarper(0.8)
								UpperCAmelCase__  :					Union[str, Any]        =     np.exp(top_p_warp(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase))

								# dist should be filtered to keep min num values so that sum is >= top_p
								# exp (-inf) => 0
								UpperCAmelCase__  :					Dict        =     np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
								self.assertTrue(np.allclose(_lowerCamelCase							,							_lowerCamelCase							,							atol=1e-3))

								# check edge cases with negative and extreme logits
								UpperCAmelCase__  :					str        =     np.broadcast_to(np.arange(_lowerCamelCase)[None, :]							,							(batch_size, vocab_size)).copy() - (
								    vocab_size // 2
								)

								# make ramp_logits more extreme
								UpperCAmelCase__  :					int        =     ramp_logits[1] * 100.0

								# make sure at least 2 tokens are kept
								UpperCAmelCase__  :					str        =     FlaxTopPLogitsWarper(0.9							,							min_tokens_to_keep=2							,							filter_value=0.0)
								UpperCAmelCase__  :					Optional[Any]        =     top_p_warp(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)

								# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
								self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist()							,							[3, 2])




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     20
								UpperCAmelCase__  :					List[str]        =     4
								UpperCAmelCase__  :					Optional[int]        =     0

								UpperCAmelCase__  :					Union[str, Any]        =     FlaxMinLengthLogitsProcessor(min_length=10							,							eos_token_id=_lowerCamelCase)

								# check that min length is applied at length 5
								UpperCAmelCase__  :					int        =     ids_tensor((batch_size, 20)							,							vocab_size=20)
								UpperCAmelCase__  :					Union[str, Any]        =     5
								UpperCAmelCase__  :					str        =     self._get_uniform_logits(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					int        =     min_dist_processor(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()							,							4 * [-float("""inf""")])

								# check that min length is not applied anymore at length 15
								UpperCAmelCase__  :					Any        =     self._get_uniform_logits(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     15
								UpperCAmelCase__  :					Optional[Any]        =     min_dist_processor(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								self.assertFalse(jnp.isinf(_lowerCamelCase).any())




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Dict        =     20
								UpperCAmelCase__  :					Optional[Any]        =     4
								UpperCAmelCase__  :					Any        =     0

								UpperCAmelCase__  :					Optional[Any]        =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCamelCase)

								# check that all scores are -inf except the bos_token_id score
								UpperCAmelCase__  :					List[Any]        =     ids_tensor((batch_size, 1)							,							vocab_size=20)
								UpperCAmelCase__  :					Dict        =     1
								UpperCAmelCase__  :					Tuple        =     self._get_uniform_logits(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     logits_processor(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
								self.assertListEqual(scores[:, bos_token_id].tolist()							,							4 * [0])  # score for bos_token_id shold be zero

								# check that bos_token_id is not forced if current length is greater than 1
								UpperCAmelCase__  :					Any        =     3
								UpperCAmelCase__  :					Dict        =     self._get_uniform_logits(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     logits_processor(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								self.assertFalse(jnp.isinf(_lowerCamelCase).any())




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Dict        =     20
								UpperCAmelCase__  :					List[str]        =     4
								UpperCAmelCase__  :					Tuple        =     0
								UpperCAmelCase__  :					Optional[int]        =     5

								UpperCAmelCase__  :					Optional[Any]        =     FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCamelCase							,							eos_token_id=_lowerCamelCase)

								# check that all scores are -inf except the eos_token_id when max_length is reached
								UpperCAmelCase__  :					List[str]        =     ids_tensor((batch_size, 4)							,							vocab_size=20)
								UpperCAmelCase__  :					Any        =     4
								UpperCAmelCase__  :					Optional[int]        =     self._get_uniform_logits(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     logits_processor(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
								self.assertListEqual(scores[:, eos_token_id].tolist()							,							4 * [0])  # score for eos_token_id should be zero

								# check that eos_token_id is not forced if max_length is not reached
								UpperCAmelCase__  :					str        =     3
								UpperCAmelCase__  :					Optional[int]        =     self._get_uniform_logits(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					Tuple        =     logits_processor(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								self.assertFalse(jnp.isinf(_lowerCamelCase).any())




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     4
								UpperCAmelCase__  :					Dict        =     10
								UpperCAmelCase__  :					Optional[int]        =     15
								UpperCAmelCase__  :					Dict        =     2
								UpperCAmelCase__  :					List[str]        =     1
								UpperCAmelCase__  :					str        =     15

								# dummy input_ids and scores
								UpperCAmelCase__  :					List[str]        =     ids_tensor((batch_size, sequence_length)							,							_lowerCamelCase)
								UpperCAmelCase__  :					Tuple        =     input_ids.copy()

								UpperCAmelCase__  :					Optional[int]        =     self._get_uniform_logits(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     scores.copy()

								# instantiate all dist processors
								UpperCAmelCase__  :					List[Any]        =     FlaxTemperatureLogitsWarper(temperature=0.5)
								UpperCAmelCase__  :					Any        =     FlaxTopKLogitsWarper(3)
								UpperCAmelCase__  :					Optional[int]        =     FlaxTopPLogitsWarper(0.8)

								# instantiate all logits processors
								UpperCAmelCase__  :					Any        =     FlaxMinLengthLogitsProcessor(min_length=10							,							eos_token_id=_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCamelCase							,							eos_token_id=_lowerCamelCase)

								UpperCAmelCase__  :					List[Any]        =     10

								# no processor list
								UpperCAmelCase__  :					Tuple        =     temp_dist_warp(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								UpperCAmelCase__  :					Optional[Any]        =     top_k_warp(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     top_p_warp(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     min_dist_proc(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								UpperCAmelCase__  :					str        =     bos_dist_proc(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
								UpperCAmelCase__  :					Optional[int]        =     eos_dist_proc(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)

								# with processor list
								UpperCAmelCase__  :					List[str]        =     FlaxLogitsProcessorList(
								    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
								UpperCAmelCase__  :					List[Any]        =     processor(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)

								# scores should be equal
								self.assertTrue(jnp.allclose(_lowerCamelCase							,							_lowerCamelCase							,							atol=1e-3))

								# input_ids should never be changed
								self.assertListEqual(input_ids.tolist()							,							input_ids_comp.tolist())




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     4
								UpperCAmelCase__  :					str        =     10
								UpperCAmelCase__  :					List[Any]        =     15
								UpperCAmelCase__  :					Tuple        =     2
								UpperCAmelCase__  :					List[Any]        =     1
								UpperCAmelCase__  :					Optional[Any]        =     15

								# dummy input_ids and scores
								UpperCAmelCase__  :					Optional[Any]        =     ids_tensor((batch_size, sequence_length)							,							_lowerCamelCase)
								UpperCAmelCase__  :					Optional[Any]        =     input_ids.copy()

								UpperCAmelCase__  :					List[Any]        =     self._get_uniform_logits(_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					int        =     scores.copy()

								# instantiate all dist processors
								UpperCAmelCase__  :					List[Any]        =     FlaxTemperatureLogitsWarper(temperature=0.5)
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxTopKLogitsWarper(3)
								UpperCAmelCase__  :					Dict        =     FlaxTopPLogitsWarper(0.8)

								# instantiate all logits processors
								UpperCAmelCase__  :					Optional[Any]        =     FlaxMinLengthLogitsProcessor(min_length=10							,							eos_token_id=_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCamelCase)
								UpperCAmelCase__  :					int        =     FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCamelCase							,							eos_token_id=_lowerCamelCase)

								UpperCAmelCase__  :					Optional[int]        =     10

								# no processor list
								def run_no_processor_list(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
											UpperCAmelCase__  :					List[str]        =     temp_dist_warp(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
											UpperCAmelCase__  :					Optional[Any]        =     top_k_warp(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
											UpperCAmelCase__  :					Union[str, Any]        =     top_p_warp(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
											UpperCAmelCase__  :					int        =     min_dist_proc(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
											UpperCAmelCase__  :					List[str]        =     bos_dist_proc(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
											UpperCAmelCase__  :					Optional[Any]        =     eos_dist_proc(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
											return scores

								# with processor list
								def run_processor_list(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
											UpperCAmelCase__  :					Optional[int]        =     FlaxLogitsProcessorList(
											    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
											UpperCAmelCase__  :					Union[str, Any]        =     processor(_lowerCamelCase							,							_lowerCamelCase							,							cur_len=_lowerCamelCase)
											return scores

								UpperCAmelCase__  :					List[str]        =     jax.jit(_lowerCamelCase)
								UpperCAmelCase__  :					Any        =     jax.jit(_lowerCamelCase)

								UpperCAmelCase__  :					Any        =     jitted_run_no_processor_list(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
								UpperCAmelCase__  :					List[str]        =     jitted_run_processor_list(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)

								# scores should be equal
								self.assertTrue(jnp.allclose(_lowerCamelCase							,							_lowerCamelCase							,							atol=1e-3))

								# input_ids should never be changed
								self.assertListEqual(input_ids.tolist()							,							input_ids_comp.tolist())



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A	={
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['BertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BertForMaskedLM',
		    'BertForMultipleChoice',
		    'BertForNextSentencePrediction',
		    'BertForPreTraining',
		    'BertForQuestionAnswering',
		    'BertForSequenceClassification',
		    'BertForTokenClassification',
		    'BertLayer',
		    'BertLMHeadModel',
		    'BertModel',
		    'BertPreTrainedModel',
		    'load_tf_weights_in_bert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBertEmbeddings',
		    'TFBertForMaskedLM',
		    'TFBertForMultipleChoice',
		    'TFBertForNextSentencePrediction',
		    'TFBertForPreTraining',
		    'TFBertForQuestionAnswering',
		    'TFBertForSequenceClassification',
		    'TFBertForTokenClassification',
		    'TFBertLMHeadModel',
		    'TFBertMainLayer',
		    'TFBertModel',
		    'TFBertPreTrainedModel',
		]
try:
		if not is_tensorflow_text_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['TFBertTokenizer']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'FlaxBertForCausalLM',
		    'FlaxBertForMaskedLM',
		    'FlaxBertForMultipleChoice',
		    'FlaxBertForNextSentencePrediction',
		    'FlaxBertForPreTraining',
		    'FlaxBertForQuestionAnswering',
		    'FlaxBertForSequenceClassification',
		    'FlaxBertForTokenClassification',
		    'FlaxBertModel',
		    'FlaxBertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
		from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_fast import BertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bert import (
				    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BertForMaskedLM,
				    BertForMultipleChoice,
				    BertForNextSentencePrediction,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    BertForTokenClassification,
				    BertLayer,
				    BertLMHeadModel,
				    BertModel,
				    BertPreTrainedModel,
				    load_tf_weights_in_bert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_bert import (
				    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBertEmbeddings,
				    TFBertForMaskedLM,
				    TFBertForMultipleChoice,
				    TFBertForNextSentencePrediction,
				    TFBertForPreTraining,
				    TFBertForQuestionAnswering,
				    TFBertForSequenceClassification,
				    TFBertForTokenClassification,
				    TFBertLMHeadModel,
				    TFBertMainLayer,
				    TFBertModel,
				    TFBertPreTrainedModel,
				)

		try:
				if not is_tensorflow_text_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_tf import TFBertTokenizer

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_bert import (
				    FlaxBertForCausalLM,
				    FlaxBertForMaskedLM,
				    FlaxBertForMultipleChoice,
				    FlaxBertForNextSentencePrediction,
				    FlaxBertForPreTraining,
				    FlaxBertForQuestionAnswering,
				    FlaxBertForSequenceClassification,
				    FlaxBertForTokenClassification,
				    FlaxBertModel,
				    FlaxBertPreTrainedModel,
				)

else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)