
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    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=5       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=1_28       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=16       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=None       ,	):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   parent
   lowercase__	: Dict     		=   batch_size
   lowercase__	: Union[str, Any]     		=   seq_length
   lowercase__	: List[str]     		=   is_training
   lowercase__	: List[Any]     		=   use_input_mask
   lowercase__	: str     		=   use_token_type_ids
   lowercase__	: str     		=   use_labels
   lowercase__	: Union[str, Any]     		=   vocab_size
   lowercase__	: List[Any]     		=   hidden_size
   lowercase__	: Optional[Any]     		=   num_hidden_layers
   lowercase__	: Union[str, Any]     		=   num_attention_heads
   lowercase__	: List[Any]     		=   intermediate_size
   lowercase__	: str     		=   hidden_act
   lowercase__	: Any     		=   hidden_dropout_prob
   lowercase__	: Optional[Any]     		=   attention_probs_dropout_prob
   lowercase__	: Optional[Any]     		=   max_position_embeddings
   lowercase__	: Dict     		=   type_vocab_size
   lowercase__	: int     		=   type_sequence_label_size
   lowercase__	: List[str]     		=   initializer_range
   lowercase__	: Optional[Any]     		=   num_labels
   lowercase__	: List[Any]     		=   num_choices
   lowercase__	: str     		=   scope


 def   lowercase__  (       self):


   '''simple docstring'''




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

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

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

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

   lowercase__	: Optional[int]     		=   self.get_config()

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


 def   lowercase__  (       self):


   '''simple docstring'''




   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=SCREAMING_SNAKE_CASE_       ,	initializer_range=self.initializer_range       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: Dict     		=   True
   lowercase__	: int     		=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
   lowercase__	: Optional[int]     		=   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   NezhaModel(config=SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__	: Optional[Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   model(SCREAMING_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   lowercase__  (       self       ,	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_       ,	):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   True
   lowercase__	: List[Any]     		=   NezhaModel(SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__	: str     		=   model(
       SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,	encoder_attention_mask=SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: Dict     		=   model(
       SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: int     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   NezhaForMaskedLM(config=SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	labels=SCREAMING_SNAKE_CASE_)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   NezhaForNextSentencePrediction(config=SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__	: int     		=   model(
       SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	labels=SCREAMING_SNAKE_CASE_       ,	)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, 2))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[str]     		=   NezhaForPreTraining(config=SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__	: List[str]     		=   model(
       SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	labels=SCREAMING_SNAKE_CASE_       ,	next_sentence_label=SCREAMING_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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   NezhaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__	: Any     		=   model(
       SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	start_positions=SCREAMING_SNAKE_CASE_       ,	end_positions=SCREAMING_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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   self.num_labels
   lowercase__	: List[Any]     		=   NezhaForSequenceClassification(SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__	: Tuple     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	labels=SCREAMING_SNAKE_CASE_)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.num_labels
   lowercase__	: Optional[int]     		=   NezhaForTokenClassification(config=SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__	: List[Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	token_type_ids=SCREAMING_SNAKE_CASE_       ,	labels=SCREAMING_SNAKE_CASE_)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.prepare_config_and_inputs()
   (
       (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     
   )	: int     		=   config_and_inputs
   lowercase__	: str     		=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
   return config, inputs_dict



@require_torch
class    _snake_case (						UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  List[str]	   =      (
     (
         NezhaModel,
         NezhaForMaskedLM,
         NezhaForMultipleChoice,
         NezhaForNextSentencePrediction,
         NezhaForPreTraining,
         NezhaForQuestionAnswering,
         NezhaForSequenceClassification,
         NezhaForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowerCAmelCase	:  List[str]	   =      (
     {
         'feature-extraction': NezhaModel,
         'fill-mask': NezhaForMaskedLM,
         'question-answering': NezhaForQuestionAnswering,
         'text-classification': NezhaForSequenceClassification,
         'token-classification': NezhaForTokenClassification,
         'zero-shot': NezhaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase	:  Optional[Any]	   =      True


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=False):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   super()._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	return_labels=SCREAMING_SNAKE_CASE_)

   if return_labels:
     if model_class in get_values(SCREAMING_SNAKE_CASE_):
       lowercase__	: Tuple     		=   torch.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)       ,	dtype=torch.long       ,	device=SCREAMING_SNAKE_CASE_)
       lowercase__	: Optional[int]     		=   torch.zeros(
           self.model_tester.batch_size       ,	dtype=torch.long       ,	device=SCREAMING_SNAKE_CASE_)
   return inputs_dict


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   NezhaModelTester(self)
   lowercase__	: Optional[int]     		=   ConfigTester(self       ,	config_class=SCREAMING_SNAKE_CASE_       ,	hidden_size=37)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.config_tester.run_common_tests()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   (
       (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     (
       lowercase__
   )		,     
   )	: Optional[int]     		=   self.model_tester.prepare_config_and_inputs_for_decoder()

   lowercase__	: str     		=   None

   self.model_tester.create_and_check_model_as_decoder(
       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_       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_)


 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__	: Tuple     		=   NezhaModel.from_pretrained(SCREAMING_SNAKE_CASE_)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)



 @slow
 @require_torch_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: List[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

     lowercase__	: Optional[Any]     		=   True
     lowercase__	: int     		=   model_class(config=SCREAMING_SNAKE_CASE_)

     lowercase__	: List[str]     		=   self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   torch.jit.trace(
         SCREAMING_SNAKE_CASE_       ,	(inputs_dict["""input_ids"""].to("""cpu"""), inputs_dict["""attention_mask"""].to("""cpu""")))

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



@require_torch
class    _snake_case (						unittest.TestCase				):


 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   NezhaModel.from_pretrained("""sijunhe/nezha-cn-base""")
   lowercase__	: Dict     		=   torch.tensor([[0, 1, 2, 3, 4, 5]])
   lowercase__	: str     		=   torch.tensor([[0, 1, 1, 1, 1, 1]])
   with torch.no_grad():
     lowercase__	: Any     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_)[0]
   lowercase__	: Any     		=   torch.Size((1, 6, 7_68))
   self.assertEqual(output.shape       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]])

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



 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base""")
   lowercase__	: List[str]     		=   torch.tensor([[0, 1, 2, 3, 4, 5]])
   lowercase__	: List[str]     		=   torch.tensor([[1, 1, 1, 1, 1, 1]])
   with torch.no_grad():
     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_)[0]
   lowercase__	: Optional[Any]     		=   torch.Size((1, 6, 2_11_28))
   self.assertEqual(output.shape       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   torch.tensor(
       [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]])

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








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory

def 		UpperCamelCase						(   )		->						List[Any]:


  '''simple docstring'''






  raise RuntimeError("""CUDA out of memory.""" )



class    _snake_case (						nn.Module				):


 def __init__(       self):


   '''simple docstring'''




   super().__init__()
   lowercase__	: Optional[Any]     		=   nn.Linear(3       ,	4)
   lowercase__	: Union[str, Any]     		=   nn.BatchNormad(4)
   lowercase__	: str     		=   nn.Linear(4       ,	5)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_)))



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()

   mock_training_loop_function()
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()
     return batch_size, arga

   lowercase__		,     lowercase__	: int     		=   mock_training_loop_function("""hello""")
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])
   self.assertListEqual([bs, arga]       ,	[8, """hello"""])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=0)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     if batch_size > 0:
       raise_fake_out_of_memory()
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     if batch_size != 8:
       raise raise_fake_out_of_memory()

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function(1_28       ,	"""hello"""       ,	"""world""")
     self.assertIn("""Batch size was passed into `f`"""       ,	cm.exception.args[0])
     self.assertIn("""`f(arg1='hello', arg2='world')"""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     raise ValueError("""Oops, we had an error!""")

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""Oops, we had an error!"""       ,	cm.exception.args[0])



 @require_cuda
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   torch.cuda.memory_allocated()
   lowercase__	: str     		=   ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   release_memory(SCREAMING_SNAKE_CASE_)
   self.assertEqual(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)






