






"""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 					UpperCamelCase__   :







    """simple docstring"""







    def __init__(		self   :  Dict							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  str=1_3							,						UpperCamelCase_   :  Dict=7							,						UpperCamelCase_   :  Optional[int]=True							,						UpperCamelCase_   :  Union[str, Any]=True							,						UpperCamelCase_   :  Union[str, Any]=True							,						UpperCamelCase_   :  Optional[int]=True							,						UpperCamelCase_   :  Dict=9_9							,						UpperCamelCase_   :  List[str]=3_2							,						UpperCamelCase_   :  str=1_8							,						UpperCamelCase_   :  Tuple=4_3							,						UpperCamelCase_   :  Optional[int]=5							,						UpperCamelCase_   :  Optional[Any]=4							,						UpperCamelCase_   :  Optional[int]=3_7							,						UpperCamelCase_   :  str="gelu"							,						UpperCamelCase_   :  List[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Optional[Any]=5_1_2							,						UpperCamelCase_   :  Tuple=1_6							,						UpperCamelCase_   :  List[str]=2							,						UpperCamelCase_   :  Dict=0.02							,						UpperCamelCase_   :  List[Any]=3							,						UpperCamelCase_   :  str=4							,						UpperCamelCase_   :  List[str]=None							,						):





           '''simple docstring'''

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







    def 							a__ (		self   :  List[Any]   ):





           '''simple docstring'''

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

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

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

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

           __magic_name__										=						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 							a__ (		self   :  Optional[int]   ):





           '''simple docstring'''

           (
               (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     (
               __magic_name__
           )							,     
           )										=						self.prepare_config_and_inputs()

           __magic_name__										=						True
           __magic_name__										=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
           __magic_name__										=						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 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Optional[Any]   ):





           '''simple docstring'''

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







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





           '''simple docstring'''

           __magic_name__										=						True
           __magic_name__										=						RemBertModel(UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						model(
               UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						encoder_hidden_states=UpperCamelCase_							,						encoder_attention_mask=UpperCamelCase_							,						)
           __magic_name__										=						model(
               UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						encoder_hidden_states=UpperCamelCase_							,						)
           __magic_name__										=						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 							a__ (		self   :  Dict							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Union[str, Any]							,						):





           '''simple docstring'''

           __magic_name__										=						RemBertForCausalLM(config=UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						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 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Optional[int]   ):





           '''simple docstring'''

           __magic_name__										=						RemBertForMaskedLM(config=UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						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 							a__ (		self   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any]							,						):





           '''simple docstring'''

           __magic_name__										=						True
           __magic_name__										=						True
           __magic_name__										=						RemBertForCausalLM(config=UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()

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

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

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

           __magic_name__										=						model(
               UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						encoder_hidden_states=UpperCamelCase_							,						encoder_attention_mask=UpperCamelCase_							,						output_hidden_states=UpperCamelCase_							,						)['hidden_states'][0]
           __magic_name__										=						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
           __magic_name__										=						ids_tensor((1,)							,						output_from_past.shape[-1]   ).item()
           __magic_name__										=						output_from_no_past[:, -3:, random_slice_idx].detach()
           __magic_name__										=						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 							a__ (		self   :  str							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any   ):





           '''simple docstring'''

           __magic_name__										=						RemBertForQuestionAnswering(config=UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						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 							a__ (		self   :  int							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Union[str, Any]   ):





           '''simple docstring'''

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







    def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Dict   ):





           '''simple docstring'''

           __magic_name__										=						self.num_labels
           __magic_name__										=						RemBertForTokenClassification(config=UpperCamelCase_   )
           model.to(UpperCamelCase_   )
           model.eval()
           __magic_name__										=						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 							a__ (		self   :  Any							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  List[str]   ):





           '''simple docstring'''

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







    def 							a__ (		self   :  int   ):





           '''simple docstring'''

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



@require_torch
class 					UpperCamelCase__   (							a_       ,  a_       ,  unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase											=   (
        (
            RemBertModel,
            RemBertForMaskedLM,
            RemBertForCausalLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    __UpperCAmelCase											=   (RemBertForCausalLM,) if is_torch_available() else ()
    __UpperCAmelCase											=   (
        {
            """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 							a__ (		self   :  Tuple   ):





           '''simple docstring'''

           __magic_name__										=						RemBertModelTester(self   )
           __magic_name__										=						ConfigTester(self							,						config_class=UpperCamelCase_							,						hidden_size=3_7   )







    def 							a__ (		self   :  str   ):





           '''simple docstring'''

           self.config_tester.run_common_tests()







    def 							a__ (		self   :  str   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_   )







    def 							a__ (		self   :  Optional[Any]   ):





           '''simple docstring'''

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







    def 							a__ (		self   :  Dict   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_   )







    def 							a__ (		self   :  Dict   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase_   )







    def 							a__ (		self   :  Optional[int]   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCamelCase_   )







    def 							a__ (		self   :  Optional[int]   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_   )







    def 							a__ (		self   :  Tuple   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_   )







    def 							a__ (		self   :  Optional[int]   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_   )







    def 							a__ (		self   :  Any   ):





           '''simple docstring'''

           __magic_name__										=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase_   )







    def 							a__ (		self   :  List[Any]   ):





           '''simple docstring'''

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

           __magic_name__										=						None

           self.model_tester.create_and_check_model_as_decoder(
               UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						)







    @slow
    def 							a__ (		self   :  Optional[Any]   ):





           '''simple docstring'''

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



@require_torch
class 					UpperCamelCase__   (							unittest.TestCase):







    """simple docstring"""







    @slow
    def 							a__ (		self   :  Optional[int]   ):





           '''simple docstring'''

           __magic_name__										=						RemBertModel.from_pretrained('google/rembert'   )
           __magic_name__										=						torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]]   )
           __magic_name__										=						torch.tensor([[0, 0, 0, 1, 1]]   )
           with torch.no_grad():
                  __magic_name__										=						model(UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						output_hidden_states=UpperCamelCase_   )

           __magic_name__										=						1_1_5_2

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

           __magic_name__										=						torch.tensor(
               [
                   [
                       [0.0_754, -0.2_022, 0.1_904],
                       [-0.3_354, -0.3_692, -0.4_791],
                       [-0.2_314, -0.6_729, -0.0_749],
                       [-0.0_396, -0.3_105, -0.4_234],
                       [-0.1_571, -0.0_525, 0.5_353],
                   ]
               ]   )

           # 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_tokenizers_available, is_torch_available


snake_case   :   Optional[int]    		=		{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   Dict    		=		["""SplinterTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   Dict    		=		[
     """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SplinterForQuestionAnswering""",
     """SplinterForPreTraining""",
     """SplinterLayer""",
     """SplinterModel""",
     """SplinterPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
 from .tokenization_splinter import SplinterTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_splinter_fast import SplinterTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_splinter import (
      SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
      SplinterForPreTraining,
      SplinterForQuestionAnswering,
      SplinterLayer,
      SplinterModel,
      SplinterPreTrainedModel,
  )


else:
 import sys

 snake_case   :   List[str]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)