'''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 				UpperCAmelCase :




    '''simple docstring'''







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






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Any    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

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

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

         SCREAMING_SNAKE_CASE					:			List[str]    	=       None
         SCREAMING_SNAKE_CASE					:			int    	=       None
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None
         if self.use_labels:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       ids_tensor([self.batch_size]    ,							self.type_sequence_label_size )
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       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					:			str    	=       self.get_config()

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






    def       _UpperCamelCase    (       self )							-> List[str]:
         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=lowercase__    ,							initializer_range=self.initializer_range    ,							)






    def       _UpperCamelCase    (       self )							-> Dict:
         (
             (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   (
             SCREAMING_SNAKE_CASE
         )	,   
         )					:			Any    	=       self.prepare_config_and_inputs()

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






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Any    	=       NezhaForNextSentencePrediction(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Dict    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__    ,							)
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, 2) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       NezhaForPreTraining(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Any    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__    ,							next_sentence_label=lowercase__    ,							)
         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    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       NezhaForQuestionAnswering(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       model(
             lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							start_positions=lowercase__    ,							end_positions=lowercase__    ,							)
         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    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			List[str]    	=       NezhaForSequenceClassification(lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			int    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       NezhaForTokenClassification(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels) )






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






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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
         )	,   
         )					:			Tuple    	=       config_and_inputs
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_torch
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Dict							=						(
        (
            NezhaModel,
            NezhaForMaskedLM,
            NezhaForMultipleChoice,
            NezhaForNextSentencePrediction,
            NezhaForPreTraining,
            NezhaForQuestionAnswering,
            NezhaForSequenceClassification,
            NezhaForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    snake_case__							:   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 {}
    )
    snake_case__							:   Tuple							=						True






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__=False )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       super()._prepare_for_class(lowercase__    ,							lowercase__    ,							return_labels=lowercase__ )

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






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Dict    	=       NezhaModelTester(self )
         SCREAMING_SNAKE_CASE					:			int    	=       ConfigTester(self    ,							config_class=lowercase__    ,							hidden_size=37 )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         # 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
         )	,   
         )					:			int    	=       self.model_tester.prepare_config_and_inputs_for_decoder()

         SCREAMING_SNAKE_CASE					:			str    	=       None

         self.model_tester.create_and_check_model_as_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							)






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_next_sequence_prediction(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowercase__ )






    @slow
    def       _UpperCamelCase    (       self )							-> int:
         for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE					:			int    	=       NezhaModel.from_pretrained(lowercase__ )
              self.assertIsNotNone(lowercase__ )






    @slow
    @require_torch_gpu
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       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					:			List[Any]    	=       model_class(config=lowercase__ )

              SCREAMING_SNAKE_CASE					:			Any    	=       self._prepare_for_class(lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.jit.trace(
                  lowercase__    ,							(inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )

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






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







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






    @slow
    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base' )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor([[0, 1, 2, 3, 4, 5]] )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([[1, 1, 1, 1, 1, 1]] )
         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			str    	=       model(lowercase__    ,							attention_mask=lowercase__ )[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.Size((1, 6, 21_128) )
         self.assertEqual(output.shape    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       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]    ,							lowercase__    ,							atol=1E-4 ) )




'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)

_lowerCAmelCase  :int  				=    {
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						"marian"
    snake_case__							:   Union[str, Any]							=						["past_key_values"]
    snake_case__							:   str							=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(       self    ,							lowercase__=58_101    ,							lowercase__=None    ,							lowercase__=1_024    ,							lowercase__=12    ,							lowercase__=4_096    ,							lowercase__=16    ,							lowercase__=12    ,							lowercase__=4_096    ,							lowercase__=16    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__="gelu"    ,							lowercase__=1_024    ,							lowercase__=0.1    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.0_2    ,							lowercase__=58_100    ,							lowercase__=False    ,							lowercase__=58_100    ,							lowercase__=0    ,							lowercase__=0    ,							lowercase__=True    ,							**lowercase__    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       decoder_vocab_size or vocab_size
         SCREAMING_SNAKE_CASE					:			int    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       d_model
         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_attention_heads
         SCREAMING_SNAKE_CASE					:			int    	=       decoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       decoder_layers
         SCREAMING_SNAKE_CASE					:			str    	=       decoder_attention_heads
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       dropout
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       attention_dropout
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       activation_dropout
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       activation_function
         SCREAMING_SNAKE_CASE					:			Tuple    	=       init_std
         SCREAMING_SNAKE_CASE					:			int    	=       encoder_layerdrop
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       decoder_layerdrop
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_cache
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			int    	=       scale_embedding  # scale factor will be sqrt(d_model) if True
         SCREAMING_SNAKE_CASE					:			str    	=       share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=lowercase__    ,							eos_token_id=lowercase__    ,							is_encoder_decoder=lowercase__    ,							decoder_start_token_id=lowercase__    ,							forced_eos_token_id=lowercase__    ,							**lowercase__    ,							)






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			Dict    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ] )

              if self.use_past:
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       {0: 'batch'}
                   SCREAMING_SNAKE_CASE					:			Any    	=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
              else:
                   SCREAMING_SNAKE_CASE					:			str    	=       {0: 'batch', 1: 'decoder_sequence'}
                   SCREAMING_SNAKE_CASE					:			int    	=       {0: 'batch', 1: 'decoder_sequence'}

              if self.use_past:
                   self.fill_with_past_key_values_(lowercase__    ,							direction='inputs' )
         elif self.task == "causal-lm":
              # TODO: figure this case out.
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ] )
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       self.num_layers
                   for i in range(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			Dict    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			Dict    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         else:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                      ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                  ] )

         return common_inputs






    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       super().outputs
         else:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       super(lowercase__    ,							self ).outputs
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.num_layers
                   for i in range(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			int    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )

         # Generate decoder inputs
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       seq_length if not self.use_past else 1
         SCREAMING_SNAKE_CASE					:			str    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dict(**lowercase__    ,							**lowercase__ )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
              else:
                   import torch
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       common_inputs['input_ids'].shape
              SCREAMING_SNAKE_CASE					:			Tuple    	=       common_inputs['decoder_input_ids'].shape[1]
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.num_attention_heads
              SCREAMING_SNAKE_CASE					:			int    	=       (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              SCREAMING_SNAKE_CASE					:			str    	=       decoder_seq_length + 3
              SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(lowercase__    ,							lowercase__ )]    ,							dim=1 )

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       self.num_layers
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       min(lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			Tuple    	=       max(lowercase__    ,							lowercase__ ) - min_num_layers
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

              for _ in range(lowercase__ ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(lowercase__ ),
                           torch.zeros(lowercase__ ),
                           torch.zeros(lowercase__ ),
                           torch.zeros(lowercase__ ),
                       ) )
              # TODO: test this.
              SCREAMING_SNAKE_CASE					:			str    	=       encoder_shape if remaining_side_name == 'encoder' else decoder_shape
              for _ in range(lowercase__    ,							lowercase__ ):
                   common_inputs["past_key_values"].append((torch.zeros(lowercase__ ), torch.zeros(lowercase__ )) )
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
              else:
                   import torch
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       common_inputs['input_ids'].shape
              # Not using the same length for past_key_values
              SCREAMING_SNAKE_CASE					:			Dict    	=       seqlen + 2
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.num_layers
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.num_attention_heads
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       common_inputs['attention_mask'].dtype
              SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.cat(
                  [common_inputs['attention_mask'], torch.ones(lowercase__    ,							lowercase__    ,							dtype=lowercase__ )]    ,							dim=1 )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [
                  (torch.zeros(lowercase__ ), torch.zeros(lowercase__ )) for _ in range(lowercase__ )
              ]
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         SCREAMING_SNAKE_CASE					:			List[str]    	=       compute_effective_axis_dimension(
             lowercase__    ,							fixed_dimension=OnnxConfig.default_fixed_batch    ,							num_token_to_add=0 )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tokenizer.num_special_tokens_to_add(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       compute_effective_axis_dimension(
             lowercase__    ,							fixed_dimension=OnnxConfig.default_fixed_sequence    ,							num_token_to_add=lowercase__ )

         # Generate dummy inputs according to compute batch and sequence
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       dict(tokenizer(lowercase__    ,							return_tensors=lowercase__ ) )
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  lowercase__    ,							batch_size=lowercase__    ,							seq_length=lowercase__    ,							is_pair=lowercase__    ,							framework=lowercase__ )

         else:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self._generate_dummy_inputs_for_causal_lm(
                  lowercase__    ,							batch_size=lowercase__    ,							seq_length=lowercase__    ,							is_pair=lowercase__    ,							framework=lowercase__ )

         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> int:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       super()._flatten_past_key_values_(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )
         else:
              SCREAMING_SNAKE_CASE					:			Any    	=       super(lowercase__    ,							self )._flatten_past_key_values_(
                  lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4



