




'''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_      :





     def __init__(     self     :					str     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					List[str]=13     ,							__lowerCamelCase     :					Union[str, Any]=7     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					Optional[int]=True     ,							__lowerCamelCase     :					List[Any]=True     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					str=99     ,							__lowerCamelCase     :					Optional[Any]=32     ,							__lowerCamelCase     :					Optional[int]=5     ,							__lowerCamelCase     :					Any=4     ,							__lowerCamelCase     :					List[Any]=37     ,							__lowerCamelCase     :					str="gelu"     ,							__lowerCamelCase     :					int=0.1     ,							__lowerCamelCase     :					Optional[int]=0.1     ,							__lowerCamelCase     :					List[Any]=128     ,							__lowerCamelCase     :					str=32     ,							__lowerCamelCase     :					Any=16     ,							__lowerCamelCase     :					List[str]=2     ,							__lowerCamelCase     :					Optional[Any]=0.0_2     ,							__lowerCamelCase     :					Tuple=3     ,							__lowerCamelCase     :					Optional[int]=4     ,							__lowerCamelCase     :					Any=None     ,							):
           snake_case__       :   int	      =    parent
           snake_case__       :   List[str]	      =    batch_size
           snake_case__       :   Tuple	      =    seq_length
           snake_case__       :   Optional[int]	      =    is_training
           snake_case__       :   Union[str, Any]	      =    use_input_mask
           snake_case__       :   Optional[Any]	      =    use_token_type_ids
           snake_case__       :   str	      =    use_labels
           snake_case__       :   str	      =    vocab_size
           snake_case__       :   List[Any]	      =    hidden_size
           snake_case__       :   Dict	      =    num_hidden_layers
           snake_case__       :   Optional[Any]	      =    num_attention_heads
           snake_case__       :   List[Any]	      =    intermediate_size
           snake_case__       :   List[str]	      =    hidden_act
           snake_case__       :   Dict	      =    hidden_dropout_prob
           snake_case__       :   Tuple	      =    attention_probs_dropout_prob
           snake_case__       :   Any	      =    max_position_embeddings
           snake_case__       :   Optional[Any]	      =    type_vocab_size
           snake_case__       :   Tuple	      =    type_sequence_label_size
           snake_case__       :   Optional[Any]	      =    initializer_range
           snake_case__       :   Optional[int]	      =    num_labels
           snake_case__       :   List[str]	      =    num_choices
           snake_case__       :   Dict	      =    scope





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Union[str, Any]	      =    ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size			)

           snake_case__       :   List[Any]	      =    None
           if self.use_input_mask:
                 snake_case__       :   Union[str, Any]	      =    random_attention_mask([self.batch_size, self.seq_length]			)

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

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

           snake_case__       :   int	      =    self.get_config()

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





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           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=__lowerCamelCase     ,							initializer_range=self.initializer_range     ,							)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           (
               (
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							
           )       :   Optional[int]	      =    self.prepare_config_and_inputs()

           snake_case__       :   Any	      =    True
           snake_case__       :   Dict	      =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
           snake_case__       :   Dict	      =    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 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Tuple			):
           snake_case__       :   List[Any]	      =    NezhaModel(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Dict	      =    model(__lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase			)
           snake_case__       :   int	      =    model(__lowerCamelCase     ,							token_type_ids=__lowerCamelCase			)
           snake_case__       :   Tuple	      =    model(__lowerCamelCase			)
           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 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					List[str]     ,							):
           snake_case__       :   Union[str, Any]	      =    True
           snake_case__       :   List[Any]	      =    NezhaModel(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[Any]	      =    model(
               __lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase     ,							encoder_hidden_states=__lowerCamelCase     ,							encoder_attention_mask=__lowerCamelCase     ,							)
           snake_case__       :   Any	      =    model(
               __lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase     ,							encoder_hidden_states=__lowerCamelCase     ,							)
           snake_case__       :   Tuple	      =    model(__lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase			)
           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 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Tuple			):
           snake_case__       :   Union[str, Any]	      =    NezhaForMaskedLM(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[Any]	      =    model(__lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)			)





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Optional[int]			):
           snake_case__       :   str	      =    NezhaForNextSentencePrediction(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   str	      =    model(
               __lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase     ,							labels=__lowerCamelCase     ,							)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, 2)			)





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					List[Any]			):
           snake_case__       :   str	      =    NezhaForPreTraining(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Optional[int]	      =    model(
               __lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase     ,							labels=__lowerCamelCase     ,							next_sentence_label=__lowerCamelCase     ,							)
           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 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Optional[Any]			):
           snake_case__       :   int	      =    NezhaForQuestionAnswering(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Any	      =    model(
               __lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase     ,							start_positions=__lowerCamelCase     ,							end_positions=__lowerCamelCase     ,							)
           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 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Dict			):
           snake_case__       :   Any	      =    self.num_labels
           snake_case__       :   List[Any]	      =    NezhaForSequenceClassification(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Dict	      =    model(__lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)			)





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Dict			):
           snake_case__       :   str	      =    self.num_labels
           snake_case__       :   List[Any]	      =    NezhaForTokenClassification(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[Any]	      =    model(__lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.num_labels)			)





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





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Any	      =    self.prepare_config_and_inputs()
           (
               (
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							
           )       :   List[Any]	      =    config_and_inputs
           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_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			(
         (
             NezhaModel,
             NezhaForMaskedLM,
             NezhaForMultipleChoice,
             NezhaForNextSentencePrediction,
             NezhaForPreTraining,
             NezhaForQuestionAnswering,
             NezhaForSequenceClassification,
             NezhaForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     A_											=			(
         {
             "feature-extraction": NezhaModel,
             "fill-mask": NezhaForMaskedLM,
             "question-answering": NezhaForQuestionAnswering,
             "text-classification": NezhaForSequenceClassification,
             "token-classification": NezhaForTokenClassification,
             "zero-shot": NezhaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     A_											=			True





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					str=False			):
           snake_case__       :   List[str]	      =    super()._prepare_for_class(__lowerCamelCase     ,							__lowerCamelCase     ,							return_labels=__lowerCamelCase			)

           if return_labels:
                 if model_class in get_values(__lowerCamelCase			):
                       snake_case__       :   Optional[Any]	      =    torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.seq_length)     ,							dtype=torch.long     ,							device=__lowerCamelCase			)
                       snake_case__       :   Optional[Any]	      =    torch.zeros(
                           self.model_tester.batch_size     ,							dtype=torch.long     ,							device=__lowerCamelCase			)
           return inputs_dict





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Optional[Any]	      =    NezhaModelTester(self			)
           snake_case__       :   int	      =    ConfigTester(self     ,							config_class=__lowerCamelCase     ,							hidden_size=37			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           self.config_tester.run_common_tests()





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   List[str]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Union[str, Any]	      =    self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           # This regression test was failing with PyTorch < 1.3
           (
               (
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							
           )       :   str	      =    self.model_tester.prepare_config_and_inputs_for_decoder()

           snake_case__       :   int	      =    None

           self.model_tester.create_and_check_model_as_decoder(
               __lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   str	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Tuple	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Dict	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_next_sequence_prediction(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Optional[int]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   str	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   List[str]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__       :   List[str]	      =    NezhaModel.from_pretrained(__lowerCamelCase			)
                 self.assertIsNotNone(__lowerCamelCase			)





     @slow
     @require_torch_gpu
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__      ,							snake_case__       :   int	      =    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

                 snake_case__       :   int	      =    True
                 snake_case__       :   Tuple	      =    model_class(config=__lowerCamelCase			)

                 snake_case__       :   str	      =    self._prepare_for_class(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   str	      =    torch.jit.trace(
                     __lowerCamelCase     ,							(inputs_dict['input_ids'].to('cpu'			), inputs_dict['attention_mask'].to('cpu'			))			)

                 with tempfile.TemporaryDirectory() as tmp:
                       torch.jit.save(__lowerCamelCase     ,							os.path.join(__lowerCamelCase     ,							'bert.pt'			)			)
                       snake_case__       :   str	      =    torch.jit.load(os.path.join(__lowerCamelCase     ,							'bert.pt'			)     ,							map_location=__lowerCamelCase			)
                       loaded(inputs_dict['input_ids'].to(__lowerCamelCase			)     ,							inputs_dict['attention_mask'].to(__lowerCamelCase			)			)






@require_torch
class        lowercase_      (						unittest.TestCase							):





     @slow
     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Optional[Any]	      =    NezhaModel.from_pretrained('sijunhe/nezha-cn-base'			)
           snake_case__       :   Dict	      =    torch.tensor([[0, 1, 2, 3, 4, 5]]			)
           snake_case__       :   Optional[Any]	      =    torch.tensor([[0, 1, 1, 1, 1, 1]]			)
           with torch.no_grad():
                 snake_case__       :   int	      =    model(__lowerCamelCase     ,							attention_mask=__lowerCamelCase			)[0]
           snake_case__       :   Dict	      =    torch.Size((1, 6, 768)			)
           self.assertEqual(output.shape     ,							__lowerCamelCase			)
           snake_case__       :   Any	      =    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]     ,							__lowerCamelCase     ,							atol=1E-4			)			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   Dict	      =    NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'			)
           snake_case__       :   Any	      =    torch.tensor([[0, 1, 2, 3, 4, 5]]			)
           snake_case__       :   Dict	      =    torch.tensor([[1, 1, 1, 1, 1, 1]]			)
           with torch.no_grad():
                 snake_case__       :   List[str]	      =    model(__lowerCamelCase     ,							attention_mask=__lowerCamelCase			)[0]
           snake_case__       :   int	      =    torch.Size((1, 6, 21128)			)
           self.assertEqual(output.shape     ,							__lowerCamelCase			)
           snake_case__       :   List[str]	      =    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]     ,							__lowerCamelCase     ,							atol=1E-4			)			)






'''simple docstring'''






import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A_			      =		"."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
A_			      =		[
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      snake_case__       :   str	      =    SavedModel()
      snake_case__       :   Union[str, Any]	      =    []

      with open(os.path.join(__SCREAMING_SNAKE_CASE				,      'utils'				,      'tf_ops'				,      'onnx.json'		)		) as f:
            snake_case__       :   Optional[Any]	      =    json.load(__SCREAMING_SNAKE_CASE		)['opsets']

      for i in range(1				,      opset + 1		):
            onnx_ops.extend(onnx_opsets[str(__SCREAMING_SNAKE_CASE		)]		)

      with open(__SCREAMING_SNAKE_CASE				,      'rb'		) as f:
            saved_model.ParseFromString(f.read()		)

      snake_case__       :   List[str]	      =    set()

      # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
      for meta_graph in saved_model.meta_graphs:
            # Add operations in the graph definition
            model_op_names.update(node.op for node in meta_graph.graph_def.node		)

            # Go through the functions in the graph definition
            for func in meta_graph.graph_def.library.function:
                  # Add operations in each function
                  model_op_names.update(node.op for node in func.node_def		)

    # Convert to list, sorted if you want
      snake_case__       :   Optional[int]	      =    sorted(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[Any]	      =    []

      for op in model_op_names:
            if op not in onnx_ops and op not in INTERNAL_OPS:
                  incompatible_ops.append(__SCREAMING_SNAKE_CASE		)

      if strict and len(__SCREAMING_SNAKE_CASE		) > 0:
            raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops		)
      elif len(__SCREAMING_SNAKE_CASE		) > 0:
            print(f"Found the following incompatible ops for the opset {opset}:"		)
            print(*__SCREAMING_SNAKE_CASE				,      sep='\n'		)
      else:
            print(f"The saved model {saved_model_path} can properly be converted with ONNX."		)


if __name__ == "__main__":
      A_			      =		argparse.ArgumentParser()
      parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
      parser.add_argument(
          "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
      )
      parser.add_argument(
          "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
      )
      parser.add_argument(
          "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
      )
      A_			      =		parser.parse_args()

      if args.framework == "onnx":
            onnx_compliancy(args.saved_model_path, args.strict, args.opset)
