


'''simple docstring'''

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     RemBertConfig,
     RemBertForCausalLM,
     RemBertForMaskedLM,
     RemBertForMultipleChoice,
     RemBertForQuestionAnswering,
     RemBertForSequenceClassification,
     RemBertForTokenClassification,
     RemBertModel,
 )
 from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self   :   Optional[Any]       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   List[str]=13       ,						UpperCamelCase   :   Any=7       ,						UpperCamelCase   :   Union[str, Any]=True       ,						UpperCamelCase   :   Union[str, Any]=True       ,						UpperCamelCase   :   List[str]=True       ,						UpperCamelCase   :   List[Any]=True       ,						UpperCamelCase   :   Optional[Any]=99       ,						UpperCamelCase   :   Dict=32       ,						UpperCamelCase   :   Optional[Any]=18       ,						UpperCamelCase   :   Tuple=43       ,						UpperCamelCase   :   List[str]=5       ,						UpperCamelCase   :   Any=4       ,						UpperCamelCase   :   Tuple=37       ,						UpperCamelCase   :   Optional[int]="gelu"       ,						UpperCamelCase   :   Union[str, Any]=0.1       ,						UpperCamelCase   :   List[Any]=0.1       ,						UpperCamelCase   :   Any=512       ,						UpperCamelCase   :   List[Any]=16       ,						UpperCamelCase   :   Tuple=2       ,						UpperCamelCase   :   List[Any]=0.02       ,						UpperCamelCase   :   Tuple=3       ,						UpperCamelCase   :   int=4       ,						UpperCamelCase   :   Tuple=None       ,						):

             '''simple docstring'''







             lowercase__				      =			parent
             lowercase__				      =			batch_size
             lowercase__				      =			seq_length
             lowercase__				      =			is_training
             lowercase__				      =			use_input_mask
             lowercase__				      =			use_token_type_ids
             lowercase__				      =			use_labels
             lowercase__				      =			vocab_size
             lowercase__				      =			hidden_size
             lowercase__				      =			input_embedding_size
             lowercase__				      =			output_embedding_size
             lowercase__				      =			num_hidden_layers
             lowercase__				      =			num_attention_heads
             lowercase__				      =			intermediate_size
             lowercase__				      =			hidden_act
             lowercase__				      =			hidden_dropout_prob
             lowercase__				      =			attention_probs_dropout_prob
             lowercase__				      =			max_position_embeddings
             lowercase__				      =			type_vocab_size
             lowercase__				      =			type_sequence_label_size
             lowercase__				      =			initializer_range
             lowercase__				      =			num_labels
             lowercase__				      =			num_choices
             lowercase__				      =			scope



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             lowercase__				      =			ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size			)

             lowercase__				      =			None
             if self.use_input_mask:
                    lowercase__				      =			random_attention_mask([self.batch_size, self.seq_length]			)

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

             lowercase__				      =			None
             lowercase__				      =			None
             lowercase__				      =			None
             if self.use_labels:
                    lowercase__				      =			ids_tensor([self.batch_size]       ,						self.type_sequence_label_size			)
                    lowercase__				      =			ids_tensor([self.batch_size, self.seq_length]       ,						self.num_labels			)
                    lowercase__				      =			ids_tensor([self.batch_size]       ,						self.num_choices			)

             lowercase__				      =			RemBertConfig(
                 vocab_size=self.vocab_size       ,						hidden_size=self.hidden_size       ,						input_embedding_size=self.input_embedding_size       ,						output_embedding_size=self.output_embedding_size       ,						num_hidden_layers=self.num_hidden_layers       ,						num_attention_heads=self.num_attention_heads       ,						intermediate_size=self.intermediate_size       ,						hidden_act=self.hidden_act       ,						hidden_dropout_prob=self.hidden_dropout_prob       ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,						max_position_embeddings=self.max_position_embeddings       ,						type_vocab_size=self.type_vocab_size       ,						is_decoder=UpperCamelCase       ,						initializer_range=self.initializer_range       ,						)

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



      def 				UpperCamelCase__		(self   :   List[str]			):

             '''simple docstring'''







             (
                 (
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,
             )				      =			self.prepare_config_and_inputs()

             lowercase__				      =			True
             lowercase__				      =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
             lowercase__				      =			ids_tensor([self.batch_size, self.seq_length]       ,						vocab_size=2			)

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



      def 				UpperCamelCase__		(self   :   Union[str, Any]       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   Union[str, Any]       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   Tuple       ,						UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   List[str]       ,						UpperCamelCase   :   str			):

             '''simple docstring'''







             lowercase__				      =			RemBertModel(config=UpperCamelCase			)
             model.to(UpperCamelCase			)
             model.eval()
             lowercase__				      =			model(UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase			)
             lowercase__				      =			model(UpperCamelCase       ,						token_type_ids=UpperCamelCase			)
             lowercase__				      =			model(UpperCamelCase			)
             self.parent.assertEqual(result.last_hidden_state.shape       ,						(self.batch_size, self.seq_length, self.hidden_size)			)



      def 				UpperCamelCase__		(self   :   Tuple       ,						UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   Any       ,						UpperCamelCase   :   Any       ,						UpperCamelCase   :   Any       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   int       ,						UpperCamelCase   :   List[str]       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   List[str]       ,						):

             '''simple docstring'''







             lowercase__				      =			True
             lowercase__				      =			RemBertModel(UpperCamelCase			)
             model.to(UpperCamelCase			)
             model.eval()
             lowercase__				      =			model(
                 UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase       ,						encoder_hidden_states=UpperCamelCase       ,						encoder_attention_mask=UpperCamelCase       ,						)
             lowercase__				      =			model(
                 UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase       ,						encoder_hidden_states=UpperCamelCase       ,						)
             lowercase__				      =			model(UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase			)
             self.parent.assertEqual(result.last_hidden_state.shape       ,						(self.batch_size, self.seq_length, self.hidden_size)			)



      def 				UpperCamelCase__		(self   :   Tuple       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Union[str, Any]       ,						UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   List[str]       ,						UpperCamelCase   :   Tuple       ,						UpperCamelCase   :   Optional[Any]       ,						):

             '''simple docstring'''







             lowercase__				      =			RemBertForCausalLM(config=UpperCamelCase			)
             model.to(UpperCamelCase			)
             model.eval()
             lowercase__				      =			model(UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase       ,						labels=UpperCamelCase			)
             self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.seq_length, self.vocab_size)			)



      def 				UpperCamelCase__		(self   :   int       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   List[str]       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Union[str, Any]       ,						UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   Union[str, Any]			):

             '''simple docstring'''







             lowercase__				      =			RemBertForMaskedLM(config=UpperCamelCase			)
             model.to(UpperCamelCase			)
             model.eval()
             lowercase__				      =			model(UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase       ,						labels=UpperCamelCase			)
             self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.seq_length, self.vocab_size)			)



      def 				UpperCamelCase__		(self   :   Dict       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   List[str]       ,						UpperCamelCase   :   Tuple       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   Any       ,						UpperCamelCase   :   List[str]       ,						UpperCamelCase   :   Union[str, Any]       ,						):

             '''simple docstring'''







             lowercase__				      =			True
             lowercase__				      =			True
             lowercase__				      =			RemBertForCausalLM(config=UpperCamelCase			)
             model.to(UpperCamelCase			)
             model.eval()

             # first forward pass
             lowercase__				      =			model(
                 UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						encoder_hidden_states=UpperCamelCase       ,						encoder_attention_mask=UpperCamelCase       ,						use_cache=UpperCamelCase       ,						)
             lowercase__				      =			outputs.past_key_values

             # create hypothetical multiple next token and extent to next_input_ids
             lowercase__				      =			ids_tensor((self.batch_size, 3)       ,						config.vocab_size			)
             lowercase__				      =			ids_tensor((self.batch_size, 3)       ,						vocab_size=2			)

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

             lowercase__				      =			model(
                 UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						encoder_hidden_states=UpperCamelCase       ,						encoder_attention_mask=UpperCamelCase       ,						output_hidden_states=UpperCamelCase       ,						)['''hidden_states'''][0]
             lowercase__				      =			model(
                 UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						encoder_hidden_states=UpperCamelCase       ,						encoder_attention_mask=UpperCamelCase       ,						past_key_values=UpperCamelCase       ,						output_hidden_states=UpperCamelCase       ,						)['''hidden_states'''][0]

             # select random slice
             lowercase__				      =			ids_tensor((1,)       ,						output_from_past.shape[-1]			).item()
             lowercase__				      =			output_from_no_past[:, -3:, random_slice_idx].detach()
             lowercase__				      =			output_from_past[:, :, random_slice_idx].detach()

             self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

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



      def 				UpperCamelCase__		(self   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   List[str]       ,						UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   Optional[Any]			):

             '''simple docstring'''







             lowercase__				      =			RemBertForQuestionAnswering(config=UpperCamelCase			)
             model.to(UpperCamelCase			)
             model.eval()
             lowercase__				      =			model(
                 UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase       ,						start_positions=UpperCamelCase       ,						end_positions=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 				UpperCamelCase__		(self   :   str       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   int       ,						UpperCamelCase   :   List[str]       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   List[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.num_labels
             lowercase__				      =			RemBertForSequenceClassification(UpperCamelCase			)
             model.to(UpperCamelCase			)
             model.eval()
             lowercase__				      =			model(UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase       ,						labels=UpperCamelCase			)
             self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_labels)			)



      def 				UpperCamelCase__		(self   :   Dict       ,						UpperCamelCase   :   Tuple       ,						UpperCamelCase   :   Any       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   List[str]			):

             '''simple docstring'''







             lowercase__				      =			self.num_labels
             lowercase__				      =			RemBertForTokenClassification(config=UpperCamelCase			)
             model.to(UpperCamelCase			)
             model.eval()
             lowercase__				      =			model(UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase       ,						labels=UpperCamelCase			)
             self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.seq_length, self.num_labels)			)



      def 				UpperCamelCase__		(self   :   Union[str, Any]       ,						UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   Optional[int]       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   int       ,						UpperCamelCase   :   Dict       ,						UpperCamelCase   :   Union[str, Any]       ,						UpperCamelCase   :   Optional[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.num_choices
             lowercase__				      =			RemBertForMultipleChoice(config=UpperCamelCase			)
             model.to(UpperCamelCase			)
             model.eval()
             lowercase__				      =			input_ids.unsqueeze(1			).expand(-1       ,						self.num_choices       ,						-1			).contiguous()
             lowercase__				      =			token_type_ids.unsqueeze(1			).expand(-1       ,						self.num_choices       ,						-1			).contiguous()
             lowercase__				      =			input_mask.unsqueeze(1			).expand(-1       ,						self.num_choices       ,						-1			).contiguous()
             lowercase__				      =			model(
                 UpperCamelCase       ,						attention_mask=UpperCamelCase       ,						token_type_ids=UpperCamelCase       ,						labels=UpperCamelCase       ,						)
             self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_choices)			)






      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             lowercase__				      =			self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,
             )				      =			config_and_inputs
             lowercase__				      =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_torch
class   __lowerCAmelCase						(lowercase_  ,					lowercase_  ,					unittest.TestCase					):





      '''simple docstring'''






      lowerCAmelCase__ :					Any   =							(
          (
              RemBertModel,
              RemBertForMaskedLM,
              RemBertForCausalLM,
              RemBertForMultipleChoice,
              RemBertForQuestionAnswering,
              RemBertForSequenceClassification,
              RemBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__ :					List[Any]   =							(RemBertForCausalLM,) if is_torch_available() else ()
      lowerCAmelCase__ :					Tuple   =							(
          {
              """feature-extraction""": RemBertModel,
              """fill-mask""": RemBertForMaskedLM,
              """question-answering""": RemBertForQuestionAnswering,
              """text-classification""": RemBertForSequenceClassification,
              """text-generation""": RemBertForCausalLM,
              """token-classification""": RemBertForTokenClassification,
              """zero-shot""": RemBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             lowercase__				      =			RemBertModelTester(self			)
             lowercase__				      =			ConfigTester(self       ,						config_class=UpperCamelCase       ,						hidden_size=37			)



      def 				UpperCamelCase__		(self   :   Union[str, Any]			):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             lowercase__				      =			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   List[str]			):

             '''simple docstring'''







             lowercase__				      =			self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    lowercase__				      =			type
                    self.model_tester.create_and_check_model(*UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             lowercase__				      =			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Tuple			):

             '''simple docstring'''







             lowercase__				      =			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             lowercase__				      =			self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Union[str, Any]			):

             '''simple docstring'''







             lowercase__				      =			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             lowercase__				      =			self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







             (
                 (
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,(
                 lowercase__
             )      ,
             )				      =			self.model_tester.prepare_config_and_inputs_for_decoder()

             lowercase__				      =			None

             self.model_tester.create_and_check_model_as_decoder(
                 UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase       ,						UpperCamelCase       ,						)






      @slow
      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__				      =			RemBertModel.from_pretrained(UpperCamelCase			)
                    self.assertIsNotNone(UpperCamelCase			)






@require_torch
class   __lowerCAmelCase						(unittest.TestCase					):





      '''simple docstring'''






      @slow
      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             lowercase__				      =			RemBertModel.from_pretrained('''google/rembert'''			)
             lowercase__				      =			torch.tensor([[312, 56498, 313, 2125, 313]]			)
             lowercase__				      =			torch.tensor([[0, 0, 0, 1, 1]]			)
             with torch.no_grad():
                    lowercase__				      =			model(UpperCamelCase       ,						token_type_ids=UpperCamelCase       ,						output_hidden_states=UpperCamelCase			)

             lowercase__				      =			1152

             lowercase__				      =			torch.Size((1, 5, hidden_size)			)
             self.assertEqual(output['''last_hidden_state'''].shape       ,						UpperCamelCase			)

             lowercase__				      =			torch.tensor(
                 [
                     [
                         [0.07_54, -0.20_22, 0.19_04],
                         [-0.33_54, -0.36_92, -0.47_91],
                         [-0.23_14, -0.67_29, -0.07_49],
                         [-0.03_96, -0.31_05, -0.42_34],
                         [-0.15_71, -0.05_25, 0.53_53],
                     ]
                 ]			)

             # Running on the original tf implementation gives slightly different results here.
             # Not clear why this variations is present
             # TODO: Find reason for discrepancy
             # expected_original_implementation = [[
             #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
             #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
             #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
             #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
             #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
             # ]]

             self.assertTrue(torch.allclose(output['''last_hidden_state'''][:, :, :3]       ,						UpperCamelCase       ,						atol=1E-4			)			)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase							:       str  						=     {'configuration_nat': ['NAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NatConfig']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Tuple  						=     [
     'NAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'NatForImageClassification',
     'NatModel',
     'NatPreTrainedModel',
     'NatBackbone',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

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




