


'''simple docstring'''


import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				)
				from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST





class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=13    ,					__snake_case=7    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case=99    ,					__snake_case=32    ,					__snake_case=5    ,					__snake_case=4    ,					__snake_case=37    ,					__snake_case="gelu"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=128    ,					__snake_case=32    ,					__snake_case=16    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=3    ,					__snake_case=4    ,					__snake_case=None    ,					):
											_SCREAMING_SNAKE_CASE :       str     		=      parent
											_SCREAMING_SNAKE_CASE :       Dict     		=      batch_size
											_SCREAMING_SNAKE_CASE :       int     		=      seq_length
											_SCREAMING_SNAKE_CASE :       List[str]     		=      is_training
											_SCREAMING_SNAKE_CASE :       List[str]     		=      use_input_mask
											_SCREAMING_SNAKE_CASE :       int     		=      use_token_type_ids
											_SCREAMING_SNAKE_CASE :       int     		=      use_labels
											_SCREAMING_SNAKE_CASE :       Dict     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       str     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Dict     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Tuple     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       str     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Any     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Any     		=      type_sequence_label_size
											_SCREAMING_SNAKE_CASE :       Any     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Any     		=      num_labels
											_SCREAMING_SNAKE_CASE :       int     		=      num_choices
											_SCREAMING_SNAKE_CASE :       List[str]     		=      scope






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size  )

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      None
											if self.use_input_mask:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      random_attention_mask([self.batch_size, self.seq_length]  )

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

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      None
											_SCREAMING_SNAKE_CASE :       Tuple     		=      None
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      None
											if self.use_labels:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      ids_tensor([self.batch_size]    ,					self.type_sequence_label_size  )
															_SCREAMING_SNAKE_CASE :       str     		=      ids_tensor([self.batch_size, self.seq_length]    ,					self.num_labels  )
															_SCREAMING_SNAKE_CASE :       Any     		=      ids_tensor([self.batch_size]    ,					self.num_choices  )

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_config()

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






							def UpperCAmelCase_	(    self  ):
											return NezhaConfig(
											    vocab_size=self.vocab_size    ,					hidden_size=self.hidden_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=__snake_case    ,					initializer_range=self.initializer_range    ,					)






							def UpperCAmelCase_	(    self  ):
											(
											    (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       
											) :       Dict     		=      self.prepare_config_and_inputs()

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      True
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      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    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      NezhaModel(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      model(__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      model(__snake_case    ,					token_type_ids=__snake_case  )
											_SCREAMING_SNAKE_CASE :       int     		=      model(__snake_case  )
											self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)  )
											self.parent.assertEqual(result.pooler_output.shape    ,					(self.batch_size, self.hidden_size)  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       str     		=      True
											_SCREAMING_SNAKE_CASE :       str     		=      NezhaModel(__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      model(
											    __snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					encoder_hidden_states=__snake_case    ,					encoder_attention_mask=__snake_case    ,					)
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      model(
											    __snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					encoder_hidden_states=__snake_case    ,					)
											_SCREAMING_SNAKE_CASE :       Any     		=      model(__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case  )
											self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)  )
											self.parent.assertEqual(result.pooler_output.shape    ,					(self.batch_size, self.hidden_size)  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      NezhaForMaskedLM(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       Tuple     		=      model(__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					labels=__snake_case  )
											self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.vocab_size)  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      NezhaForNextSentencePrediction(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      model(
											    __snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					labels=__snake_case    ,					)
											self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, 2)  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       str     		=      NezhaForPreTraining(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       str     		=      model(
											    __snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					labels=__snake_case    ,					next_sentence_label=__snake_case    ,					)
											self.parent.assertEqual(result.prediction_logits.shape    ,					(self.batch_size, self.seq_length, self.vocab_size)  )
											self.parent.assertEqual(result.seq_relationship_logits.shape    ,					(self.batch_size, 2)  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       str     		=      NezhaForQuestionAnswering(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       List[str]     		=      model(
											    __snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					start_positions=__snake_case    ,					end_positions=__snake_case    ,					)
											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    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.num_labels
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      NezhaForSequenceClassification(__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					labels=__snake_case  )
											self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.num_labels
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      NezhaForTokenClassification(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       Tuple     		=      model(__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					labels=__snake_case  )
											self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.num_labels)  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.num_choices
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      NezhaForMultipleChoice(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       Dict     		=      input_ids.unsqueeze(1  ).expand(-1    ,					self.num_choices    ,					-1  ).contiguous()
											_SCREAMING_SNAKE_CASE :       Dict     		=      token_type_ids.unsqueeze(1  ).expand(-1    ,					self.num_choices    ,					-1  ).contiguous()
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      input_mask.unsqueeze(1  ).expand(-1    ,					self.num_choices    ,					-1  ).contiguous()
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      model(
											    __snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					labels=__snake_case    ,					)
											self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_choices)  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.prepare_config_and_inputs()
											(
											    (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       
											) :       Dict     		=      config_and_inputs
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
											return config, inputs_dict





@require_torch
class     lowercase__  (						_snake_case     ,     _snake_case     ,     _snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Union[str, Any]      	=  (
							    (
							        NezhaModel,
							        NezhaForMaskedLM,
							        NezhaForMultipleChoice,
							        NezhaForNextSentencePrediction,
							        NezhaForPreTraining,
							        NezhaForQuestionAnswering,
							        NezhaForSequenceClassification,
							        NezhaForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							A_							:      Tuple      	=  (
							    {
							        """feature-extraction""": NezhaModel,
							        """fill-mask""": NezhaForMaskedLM,
							        """question-answering""": NezhaForQuestionAnswering,
							        """text-classification""": NezhaForSequenceClassification,
							        """token-classification""": NezhaForTokenClassification,
							        """zero-shot""": NezhaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							A_							:      Optional[int]      	=  True






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case=False  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      super()._prepare_for_class(__snake_case    ,					__snake_case    ,					return_labels=__snake_case  )

											if return_labels:
															if model_class in get_values(__snake_case  ):
																			_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.seq_length)    ,					dtype=torch.long    ,					device=__snake_case  )
																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.zeros(
																			    self.model_tester.batch_size    ,					dtype=torch.long    ,					device=__snake_case  )
											return inputs_dict






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      NezhaModelTester(self  )
											_SCREAMING_SNAKE_CASE :       int     		=      ConfigTester(self    ,					config_class=__snake_case    ,					hidden_size=37  )






							def UpperCAmelCase_	(    self  ):
											self.config_tester.run_common_tests()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											# This regression test was failing with PyTorch < 1.3
											(
											    (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       
											) :       List[Any]     		=      self.model_tester.prepare_config_and_inputs_for_decoder()

											_SCREAMING_SNAKE_CASE :       int     		=      None

											self.model_tester.create_and_check_model_as_decoder(
											    __snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					)






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_next_sequence_prediction(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*__snake_case  )






							@slow
							def UpperCAmelCase_	(    self  ):
											for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      NezhaModel.from_pretrained(__snake_case  )
															self.assertIsNotNone(__snake_case  )






							@slow
							@require_torch_gpu
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Any     		=      self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
															# NezhaForMultipleChoice behaves incorrectly in JIT environments.
															if model_class == NezhaForMultipleChoice:
																			return

															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      True
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      model_class(config=__snake_case  )

															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self._prepare_for_class(__snake_case    ,					__snake_case  )
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.jit.trace(
															    __snake_case    ,					(inputs_dict["""input_ids"""].to("""cpu"""  ), inputs_dict["""attention_mask"""].to("""cpu"""  ))  )

															with tempfile.TemporaryDirectory() as tmp:
																			torch.jit.save(__snake_case    ,					os.path.join(__snake_case    ,					"""bert.pt"""  )  )
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.jit.load(os.path.join(__snake_case    ,					"""bert.pt"""  )    ,					map_location=__snake_case  )
																			loaded(inputs_dict["""input_ids"""].to(__snake_case  )    ,					inputs_dict["""attention_mask"""].to(__snake_case  )  )





@require_torch
class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""  )
											_SCREAMING_SNAKE_CASE :       int     		=      torch.tensor([[0, 1, 2, 3, 4, 5]]  )
											_SCREAMING_SNAKE_CASE :       Any     		=      torch.tensor([[0, 1, 1, 1, 1, 1]]  )
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       int     		=      model(__snake_case    ,					attention_mask=__snake_case  )[0]
											_SCREAMING_SNAKE_CASE :       Any     		=      torch.Size((1, 6, 768)  )
											self.assertEqual(output.shape    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]  )

											self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,					__snake_case    ,					atol=1e-4  )  )






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.tensor([[0, 1, 2, 3, 4, 5]]  )
											_SCREAMING_SNAKE_CASE :       int     		=      torch.tensor([[1, 1, 1, 1, 1, 1]]  )
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					attention_mask=__snake_case  )[0]
											_SCREAMING_SNAKE_CASE :       Any     		=      torch.Size((1, 6, 2_1128)  )
											self.assertEqual(output.shape    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      torch.tensor(
											    [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]  )

											self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,					__snake_case    ,					atol=1e-4  )  )








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:   Dict							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      Optional[int]      	=  """canine"""






							def __init__(    self    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=1_6384    ,					__snake_case=16    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=0    ,					__snake_case=0XE000    ,					__snake_case=0XE001    ,					__snake_case=4    ,					__snake_case=4    ,					__snake_case=8    ,					__snake_case=1_6384    ,					__snake_case=128    ,					**__snake_case    ,					):
											super().__init__(pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					**__snake_case  )

											_SCREAMING_SNAKE_CASE :       str     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       Any     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Dict     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Tuple     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       List[str]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       Tuple     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       str     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Dict     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      layer_norm_eps

											# Character config:
											_SCREAMING_SNAKE_CASE :       Any     		=      downsampling_rate
											_SCREAMING_SNAKE_CASE :       List[str]     		=      upsampling_kernel_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hash_functions
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      num_hash_buckets
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      local_transformer_stride




