'''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        __A      :



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



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size )

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

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

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

         UpperCAmelCase_           =   self.get_config()

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



    def 					_lowercase						(self    :    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=__a   ,			initializer_range=self.initializer_range   ,			)



    def 					_lowercase						(self    :    Optional[Any] ):
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , 
         )           =   self.prepare_config_and_inputs()

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



    def 					_lowercase						(self    :    Dict   ,			__a    :    List[str]   ,			__a    :    Tuple   ,			__a    :    List[str]   ,			__a    :    Union[str, Any]   ,			__a    :    List[str]   ,			__a    :    List[Any]   ,			__a    :    Optional[Any] ):
         UpperCAmelCase_           =   NezhaForNextSentencePrediction(config=__a )
         model.to(__a )
         model.eval()
         UpperCAmelCase_           =   model(
             __a   ,			attention_mask=__a   ,			token_type_ids=__a   ,			labels=__a   ,			)
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, 2) )



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



    def 					_lowercase						(self    :    Tuple   ,			__a    :    Tuple   ,			__a    :    Any   ,			__a    :    Dict   ,			__a    :    Union[str, Any]   ,			__a    :    Dict   ,			__a    :    Dict   ,			__a    :    int ):
         UpperCAmelCase_           =   self.num_labels
         UpperCAmelCase_           =   NezhaForTokenClassification(config=__a )
         model.to(__a )
         model.eval()
         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a   ,			token_type_ids=__a   ,			labels=__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.seq_length, self.num_labels) )



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






    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , 
         )           =   config_and_inputs
         UpperCAmelCase_           =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
         return config, inputs_dict




@require_torch
class        __A      ( UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Any          =  (
        (
            NezhaModel,
            NezhaForMaskedLM,
            NezhaForMultipleChoice,
            NezhaForNextSentencePrediction,
            NezhaForPreTraining,
            NezhaForQuestionAnswering,
            NezhaForSequenceClassification,
            NezhaForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    a__       :						List[Any]          =  (
        {
            """feature-extraction""": NezhaModel,
            """fill-mask""": NezhaForMaskedLM,
            """question-answering""": NezhaForQuestionAnswering,
            """text-classification""": NezhaForSequenceClassification,
            """token-classification""": NezhaForTokenClassification,
            """zero-shot""": NezhaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a__       :						int          =  True



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Any   ,			__a    :    List[Any]   ,			__a    :    List[str]=False ):
         UpperCAmelCase_           =   super()._prepare_for_class(__a   ,			__a   ,			return_labels=__a )

         if return_labels:
              if model_class in get_values(__a ):
                   UpperCAmelCase_           =   torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)   ,			dtype=torch.long   ,			device=__a )
                   UpperCAmelCase_           =   torch.zeros(
                       self.model_tester.batch_size   ,			dtype=torch.long   ,			device=__a )
         return inputs_dict



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   NezhaModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    List[Any] ):
         self.config_tester.run_common_tests()



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



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*__a )



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

         UpperCAmelCase_           =   None

         self.model_tester.create_and_check_model_as_decoder(
             __a   ,			__a   ,			__a   ,			__a   ,			__a   ,			__a   ,			__a   ,			__a   ,			__a   ,			)



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__a )



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*__a )



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_next_sequence_prediction(*__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__a )



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__a )



    @slow
    def 					_lowercase						(self    :    Optional[int] ):
         for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_           =   NezhaModel.from_pretrained(__a )
              self.assertIsNotNone(__a )






    @slow
    @require_torch_gpu
    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_    , UpperCAmelCase_           =   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

              UpperCAmelCase_           =   True
              UpperCAmelCase_           =   model_class(config=__a )

              UpperCAmelCase_           =   self._prepare_for_class(__a   ,			__a )
              UpperCAmelCase_           =   torch.jit.trace(
                  __a   ,			(inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )

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




@require_torch
class        __A      ( unittest.TestCase	):



    @slow
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   NezhaModel.from_pretrained("sijunhe/nezha-cn-base" )
         UpperCAmelCase_           =   torch.tensor([[0, 1, 2, 3, 4, 5]] )
         UpperCAmelCase_           =   torch.tensor([[0, 1, 1, 1, 1, 1]] )
         with torch.no_grad():
              UpperCAmelCase_           =   model(__a   ,			attention_mask=__a )[0]
         UpperCAmelCase_           =   torch.Size((1, 6, 768) )
         self.assertEqual(output.shape   ,			__a )
         UpperCAmelCase_           =   torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]] )

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






    @slow
    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base" )
         UpperCAmelCase_           =   torch.tensor([[0, 1, 2, 3, 4, 5]] )
         UpperCAmelCase_           =   torch.tensor([[1, 1, 1, 1, 1, 1]] )
         with torch.no_grad():
              UpperCAmelCase_           =   model(__a   ,			attention_mask=__a )[0]
         UpperCAmelCase_           =   torch.Size((1, 6, 21128) )
         self.assertEqual(output.shape   ,			__a )
         UpperCAmelCase_           =   torch.tensor(
             [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]] )

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





'''simple docstring'''





import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


SCREAMING_SNAKE_CASE_:     Optional[Any]  ='\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


SCREAMING_SNAKE_CASE_:     Union[str, Any]  ='\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


SCREAMING_SNAKE_CASE_:     List[Any]  =r'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'




@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION	)
class        __A      ( datasets.Metric	):



    def 					_lowercase						(self    :    Optional[Any] ):
         return datasets.MetricInfo(
             description=_DESCRIPTION   ,			citation=_CITATION   ,			inputs_description=_KWARGS_DESCRIPTION   ,			features=datasets.Features(
                 {
                     "predictions": datasets.Value("string" ),
                     "references": datasets.Value("string" ),
                 } )   ,			homepage="https://github.com/hendrycks/math"   ,			codebase_urls=["https://github.com/hendrycks/math"]   ,			)






    def 					_lowercase						(self    :    Tuple   ,			__a    :    Optional[int]   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   0.0
         for i, j in zip(__a   ,			__a ):
              n_correct += 1.0 if math_equivalence.is_equiv(__a   ,			__a ) else 0.0
         UpperCAmelCase_           =   n_correct / len(__a )
         return {
             "accuracy": accuracy,
         }




