





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class 				_UpperCAmelCase    :

  """simple docstring"""


  def __init__(				self :		Dict					,  lowerCAmelCase_ :		List[Any]					,  lowerCAmelCase_ :		Optional[Any]=9_9					,  lowerCAmelCase_ :		str=1_3					,  lowerCAmelCase_ :		int=7					,  lowerCAmelCase_ :		Union[str, Any]=9					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		Optional[Any]=True					,  lowerCAmelCase_ :		List[str]=False					,  lowerCAmelCase_ :		List[str]=3_2					,  lowerCAmelCase_ :		Union[str, Any]=5					,  lowerCAmelCase_ :		Any=4					,  lowerCAmelCase_ :		int=3_7					,  lowerCAmelCase_ :		str=8					,  lowerCAmelCase_ :		List[Any]=0.1					,  lowerCAmelCase_ :		str=0.0_02					,  lowerCAmelCase_ :		Union[str, Any]=1					,  lowerCAmelCase_ :		Any=0					,  lowerCAmelCase_ :		List[str]=0					,  lowerCAmelCase_ :		Union[str, Any]=None					,  lowerCAmelCase_ :		List[str]=None					,  )							->						List[str]:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	encoder_seq_length
     __lowerCAmelCase								=	decoder_seq_length
     # For common tests
     __lowerCAmelCase								=	self.decoder_seq_length
     __lowerCAmelCase								=	is_training
     __lowerCAmelCase								=	use_attention_mask
     __lowerCAmelCase								=	use_labels
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	d_ff
     __lowerCAmelCase								=	relative_attention_num_buckets
     __lowerCAmelCase								=	dropout_rate
     __lowerCAmelCase								=	initializer_factor
     __lowerCAmelCase								=	eos_token_id
     __lowerCAmelCase								=	pad_token_id
     __lowerCAmelCase								=	decoder_start_token_id
     __lowerCAmelCase								=	None
     __lowerCAmelCase								=	decoder_layers






  def 				lowercase		(				self :		List[str]     )							->						Tuple:
     return TaConfig.from_pretrained('google/umt5-base'     )






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		Any=None					,  lowerCAmelCase_ :		Union[str, Any]=None					,  lowerCAmelCase_ :		Union[str, Any]=None					,  lowerCAmelCase_ :		Optional[int]=None					,  lowerCAmelCase_ :		Tuple=None					,  )							->						Union[str, Any]:
     if attention_mask is None:
        __lowerCAmelCase								=	input_ids.ne(config.pad_token_id     )
     if decoder_attention_mask is None:
        __lowerCAmelCase								=	decoder_input_ids.ne(config.pad_token_id     )
     if head_mask is None:
        __lowerCAmelCase								=	torch.ones(config.num_hidden_layers					,  config.num_attention_heads					,  device=SCREAMING_SNAKE_CASE_     )
     if decoder_head_mask is None:
        __lowerCAmelCase								=	torch.ones(config.num_decoder_layers					,  config.num_attention_heads					,  device=SCREAMING_SNAKE_CASE_     )
     if cross_attn_head_mask is None:
        __lowerCAmelCase								=	torch.ones(
            config.num_decoder_layers					,  config.num_attention_heads					,  device=SCREAMING_SNAKE_CASE_     )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }






  def 				lowercase		(				self :		Union[str, Any]     )							->						Tuple:
     __lowerCAmelCase								=	ids_tensor([self.batch_size, self.encoder_seq_length]					,  self.vocab_size     )
     __lowerCAmelCase								=	ids_tensor([self.batch_size, self.decoder_seq_length]					,  self.vocab_size     )

     # we need to clamp the input ids here to avoid having pad token in between
     # this is because for NllbMoe the position_ids are prepared such that
     # all pad tokens have pos id = 2 and rest are between 2..seq_length
     # and the seq_length here is seq_length - num_pad_tokens
     # but when using past, there is no way of knowing if the past input ids had
     # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
     # position_ids being off by num_pad_tokens in past input
     __lowerCAmelCase								=	input_ids.clamp(self.pad_token_id + 1     )
     __lowerCAmelCase								=	decoder_input_ids.clamp(self.pad_token_id + 1     )

     __lowerCAmelCase								=	self.get_config()
     __lowerCAmelCase								=	config.num_attention_heads
     __lowerCAmelCase								=	self.prepare_inputs_dict(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_     )
     return config, input_dict






  def 				lowercase		(				self :		List[str]     )							->						List[str]:
     __lowerCAmelCase								=	self.prepare_config_and_inputs()
     return config, inputs_dict






  def 				lowercase		(				self :		Optional[Any]     )							->						List[Any]:
     return TaConfig(
         vocab_size=1_6_6					,  d_model=self.hidden_size					,  d_ff=self.d_ff					,  d_kv=self.hidden_size // self.num_attention_heads					,  num_layers=self.num_hidden_layers					,  num_decoder_layers=self.decoder_layers					,  num_heads=self.num_attention_heads					,  relative_attention_num_buckets=self.relative_attention_num_buckets					,  dropout_rate=self.dropout_rate					,  initializer_factor=self.initializer_factor					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.pad_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  )






  def 				lowercase		(				self :		Tuple     )							->						int:
     return TaConfig(
         vocab_size=self.vocab_size					,  d_model=self.hidden_size					,  d_ff=self.d_ff					,  d_kv=self.hidden_size // self.num_attention_heads					,  num_layers=self.num_hidden_layers					,  num_decoder_layers=self.decoder_layers					,  num_heads=self.num_attention_heads					,  relative_attention_num_buckets=self.relative_attention_num_buckets					,  dropout_rate=self.dropout_rate					,  initializer_factor=self.initializer_factor					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.pad_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  )






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		Dict					,  )							->						Tuple:
     __lowerCAmelCase								=	UMTaModel(config=SCREAMING_SNAKE_CASE_     )
     model.to(SCREAMING_SNAKE_CASE_     )
     model.eval()
     __lowerCAmelCase								=	model(
         input_ids=SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  decoder_attention_mask=SCREAMING_SNAKE_CASE_					,  )
     __lowerCAmelCase								=	model(input_ids=SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_     )
     __lowerCAmelCase								=	result.last_hidden_state
     __lowerCAmelCase								=	result.past_key_values
     __lowerCAmelCase								=	result.encoder_last_hidden_state

     self.parent.assertEqual(encoder_output.size()					,  (self.batch_size, self.encoder_seq_length, self.hidden_size)     )
     self.parent.assertEqual(decoder_output.size()					,  (self.batch_size, self.decoder_seq_length, self.hidden_size)     )
     # There should be `num_layers` key value embeddings stored in decoder_past
     self.parent.assertEqual(len(SCREAMING_SNAKE_CASE_     )					,  config.num_layers     )
     # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
     self.parent.assertEqual(len(decoder_past[0]     )					,  4     )






  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		str					,  )							->						Optional[int]:
     __lowerCAmelCase								=	UMTaModel(config=SCREAMING_SNAKE_CASE_     ).get_decoder().to(SCREAMING_SNAKE_CASE_     ).eval()
     # first forward pass
     __lowerCAmelCase								=	model(SCREAMING_SNAKE_CASE_					,  use_cache=SCREAMING_SNAKE_CASE_     )
     __lowerCAmelCase								=	model(SCREAMING_SNAKE_CASE_     )
     __lowerCAmelCase								=	model(SCREAMING_SNAKE_CASE_					,  use_cache=SCREAMING_SNAKE_CASE_     )

     self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     )     )
     self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     ) + 1     )

     __lowerCAmelCase								=	outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     __lowerCAmelCase								=	ids_tensor((self.batch_size, 1)					,  config.vocab_size     )

     # append to next input_ids and
     __lowerCAmelCase								=	torch.cat([input_ids, next_tokens]					,  dim=-1     )

     __lowerCAmelCase								=	model(SCREAMING_SNAKE_CASE_     )['last_hidden_state']
     __lowerCAmelCase								=	model(SCREAMING_SNAKE_CASE_					,  past_key_values=SCREAMING_SNAKE_CASE_     )['last_hidden_state']

     # select random slice
     __lowerCAmelCase								=	ids_tensor((1,)					,  output_from_past.shape[-1]     ).item()
     __lowerCAmelCase								=	output_from_no_past[:, -1, random_slice_idx].detach()
     __lowerCAmelCase								=	output_from_past[:, 0, random_slice_idx].detach()

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  atol=1e-3     )     )






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		Union[str, Any]					,  )							->						List[Any]:
     __lowerCAmelCase								=	UMTaModel(config=SCREAMING_SNAKE_CASE_     ).to(SCREAMING_SNAKE_CASE_     ).half().eval()
     __lowerCAmelCase								=	model(**SCREAMING_SNAKE_CASE_     )['last_hidden_state']
     self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE_     ).any().item()     )





@require_torch
class 				_UpperCAmelCase    (    _UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = (
      (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
  )
  a_        = (UMTaForConditionalGeneration,) if is_torch_available() else ()
  a_        = (
      {
          'conversational': UMTaForConditionalGeneration,
          'feature-extraction': UMTaModel,
          'summarization': UMTaForConditionalGeneration,
          'text2text-generation': UMTaForConditionalGeneration,
          'translation': UMTaForConditionalGeneration,
          'question-answering': UMTaForQuestionAnswering,
      }
      if is_torch_available()
      else {}
  )
  a_        = True
  a_        = False
  a_        = False
  a_        = True
  a_        = True
  # The small UMT5 model needs higher percentages for CPU/MP tests
  a_        = [0.8, 0.9]






  def 				lowercase		(				self :		Union[str, Any]     )							->						Optional[Any]:
     __lowerCAmelCase								=	UMTaModelTester(self     )






  @unittest.skip('Test has a segmentation fault on torch 1.8.0'     )
  def 				lowercase		(				self :		Any     )							->						int:
     __lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs()
     __lowerCAmelCase								=	UMTaModel(config_and_inputs[0]     ).to(SCREAMING_SNAKE_CASE_     )
     with tempfile.TemporaryDirectory() as tmpdirname:
        torch.onnx.export(
            SCREAMING_SNAKE_CASE_					,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])					,  f"""{tmpdirname}/t5_test.onnx"""					,  export_params=SCREAMING_SNAKE_CASE_					,  opset_version=9					,  input_names=['input_ids', 'decoder_input_ids']					,  )






  @unittest.skipIf(torch_device == 'cpu'					,  'Cant do half precision'     )
  def 				lowercase		(				self :		int     )							->						Optional[int]:
     __lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE_     )






  def 				lowercase		(				self :		List[Any]     )							->						int:
     __lowerCAmelCase								=	['encoder_attentions', 'decoder_attentions', 'cross_attentions']
     __lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs()
     __lowerCAmelCase								=	config_and_inputs[0]
     __lowerCAmelCase								=	UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE_     ).eval()
     model.to(SCREAMING_SNAKE_CASE_     )

     __lowerCAmelCase								=	{
         'head_mask': torch.zeros(config.num_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_     ),
         'decoder_head_mask': torch.zeros(config.num_decoder_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_     ),
         'cross_attn_head_mask': torch.zeros(config.num_decoder_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_     ),
     }

     for attn_name, (name, mask) in zip(SCREAMING_SNAKE_CASE_					,  head_masking.items()     ):
        __lowerCAmelCase								=	{name: mask}
        # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
        if name == "head_mask":
           __lowerCAmelCase								=	torch.ones(
               config.num_decoder_layers					,  config.num_heads					,  device=SCREAMING_SNAKE_CASE_     )

        __lowerCAmelCase								=	model.generate(
            config_and_inputs[1]['input_ids']					,  num_beams=1					,  max_length=3					,  output_attentions=SCREAMING_SNAKE_CASE_					,  return_dict_in_generate=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_					,  )
        # We check the state of decoder_attentions and cross_attentions just from the last step
        __lowerCAmelCase								=	out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
        self.assertEqual(sum([w.sum().item() for w in attn_weights]     )					,  0.0     )






  @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'     )
  def 				lowercase		(				self :		List[Any]     )							->						Union[str, Any]:
     pass





@require_torch
@require_sentencepiece
@require_tokenizers
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  @slow
  @unittest.skip(
      'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'     )
  def 				lowercase		(				self :		Dict     )							->						int:
     __lowerCAmelCase								=	UMTaForConditionalGeneration.from_pretrained('google/umt5-small'					,  return_dict=SCREAMING_SNAKE_CASE_     ).to(SCREAMING_SNAKE_CASE_     )
     __lowerCAmelCase								=	AutoTokenizer.from_pretrained('google/umt5-small'					,  use_fast=SCREAMING_SNAKE_CASE_					,  legacy=SCREAMING_SNAKE_CASE_     )
     __lowerCAmelCase								=	[
         'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
         'No se como puedo <extra_id_0>.',
         'This is the reason why we <extra_id_0> them.',
         'The <extra_id_0> walks in <extra_id_1>, seats',
         'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
     ]
     __lowerCAmelCase								=	tokenizer(SCREAMING_SNAKE_CASE_					,  return_tensors='pt'					,  padding=SCREAMING_SNAKE_CASE_     ).input_ids
     # fmt: off
     __lowerCAmelCase								=	torch.tensor(
         [
             [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
             [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
         ]     )
     # fmt: on
     torch.testing.assert_allclose(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_     )

     __lowerCAmelCase								=	model.generate(input_ids.to(SCREAMING_SNAKE_CASE_     )     )
     __lowerCAmelCase								=	[
         '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
         '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
     ]
     __lowerCAmelCase								=	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_     )





'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

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






