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							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=13		,				lowerCAmelCase_=7		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=99		,				lowerCAmelCase_=32		,				lowerCAmelCase_=5		,				lowerCAmelCase_=4		,				lowerCAmelCase_=37		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=1_28		,				lowerCAmelCase_=32		,				lowerCAmelCase_=16		,				lowerCAmelCase_=2		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=3		,				lowerCAmelCase_=4		,				lowerCAmelCase_=None		,				)  ->					Dict:
        _A        =  parent
        _A        =  batch_size
        _A        =  seq_length
        _A        =  is_training
        _A        =  use_input_mask
        _A        =  use_token_type_ids
        _A        =  use_labels
        _A        =  vocab_size
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  max_position_embeddings
        _A        =  type_vocab_size
        _A        =  type_sequence_label_size
        _A        =  initializer_range
        _A        =  num_labels
        _A        =  num_choices
        _A        =  scope



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size							)

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

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

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

        _A        =  self.get_config()

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



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        return NezhaConfig(
            vocab_size=self.vocab_size		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				hidden_act=self.hidden_act		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				max_position_embeddings=self.max_position_embeddings		,				type_vocab_size=self.type_vocab_size		,				is_decoder=__snake_case		,				initializer_range=self.initializer_range		,				)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        (
            _A       
        )        =  self.prepare_config_and_inputs()

        _A        =  True
        _A        =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
        _A        =  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		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Dict:
        _A        =  NezhaModel(config=__snake_case							)
        model.to(__snake_case							)
        model.eval()
        _A        =  model(__snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case							)
        _A        =  model(__snake_case		,				token_type_ids=__snake_case							)
        _A        =  model(__snake_case							)
        self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)
        self.parent.assertEqual(result.pooler_output.shape		,				(self.batch_size, self.hidden_size)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				)  ->					Any:
        _A        =  True
        _A        =  NezhaModel(__snake_case							)
        model.to(__snake_case							)
        model.eval()
        _A        =  model(
            __snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case		,				encoder_hidden_states=__snake_case		,				encoder_attention_mask=__snake_case		,				)
        _A        =  model(
            __snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case		,				encoder_hidden_states=__snake_case		,				)
        _A        =  model(__snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case							)
        self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)
        self.parent.assertEqual(result.pooler_output.shape		,				(self.batch_size, self.hidden_size)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Optional[Any]:
        _A        =  NezhaForMaskedLM(config=__snake_case							)
        model.to(__snake_case							)
        model.eval()
        _A        =  model(__snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case		,				labels=__snake_case							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[str]:
        _A        =  NezhaForNextSentencePrediction(config=__snake_case							)
        model.to(__snake_case							)
        model.eval()
        _A        =  model(
            __snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case		,				labels=__snake_case		,				)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, 2)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Union[str, Any]:
        _A        =  NezhaForPreTraining(config=__snake_case							)
        model.to(__snake_case							)
        model.eval()
        _A        =  model(
            __snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case		,				labels=__snake_case		,				next_sentence_label=__snake_case		,				)
        self.parent.assertEqual(result.prediction_logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)							)
        self.parent.assertEqual(result.seq_relationship_logits.shape		,				(self.batch_size, 2)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        _A        =  NezhaForQuestionAnswering(config=__snake_case							)
        model.to(__snake_case							)
        model.eval()
        _A        =  model(
            __snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case		,				start_positions=__snake_case		,				end_positions=__snake_case		,				)
        self.parent.assertEqual(result.start_logits.shape		,				(self.batch_size, self.seq_length)							)
        self.parent.assertEqual(result.end_logits.shape		,				(self.batch_size, self.seq_length)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Tuple:
        _A        =  self.num_labels
        _A        =  NezhaForSequenceClassification(__snake_case							)
        model.to(__snake_case							)
        model.eval()
        _A        =  model(__snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case		,				labels=__snake_case							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Optional[int]:
        _A        =  self.num_labels
        _A        =  NezhaForTokenClassification(config=__snake_case							)
        model.to(__snake_case							)
        model.eval()
        _A        =  model(__snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case		,				labels=__snake_case							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.num_labels)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        _A        =  self.num_choices
        _A        =  NezhaForMultipleChoice(config=__snake_case							)
        model.to(__snake_case							)
        model.eval()
        _A        =  input_ids.unsqueeze(1							).expand(-1		,				self.num_choices		,				-1							).contiguous()
        _A        =  token_type_ids.unsqueeze(1							).expand(-1		,				self.num_choices		,				-1							).contiguous()
        _A        =  input_mask.unsqueeze(1							).expand(-1		,				self.num_choices		,				-1							).contiguous()
        _A        =  model(
            __snake_case		,				attention_mask=__snake_case		,				token_type_ids=__snake_case		,				labels=__snake_case		,				)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_choices)							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.prepare_config_and_inputs()
        (
            _A       
        )        =  config_and_inputs
        _A        =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict



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







 """simple docstring"""






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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_=False							)  ->					Any:
        _A        =  super()._prepare_for_class(__snake_case		,				__snake_case		,				return_labels=__snake_case							)

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



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  NezhaModelTester(self							)
        _A        =  ConfigTester(self		,				config_class=__snake_case		,				hidden_size=37							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        self.config_tester.run_common_tests()



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__snake_case							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*__snake_case							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        # This regression test was failing with PyTorch < 1.3
        (
            _A       
        )        =  self.model_tester.prepare_config_and_inputs_for_decoder()

        _A        =  None

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



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__snake_case							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__snake_case							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_next_sequence_prediction(*__snake_case							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__snake_case							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__snake_case							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__snake_case							)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__snake_case							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A        =  NezhaModel.from_pretrained(__snake_case							)
               self.assertIsNotNone(__snake_case							)



 @slow
 @require_torch_gpu
 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  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

               _A        =  True
               _A        =  model_class(config=__snake_case							)

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

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



@require_torch
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 @slow
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""							)
        _A        =  torch.tensor([[0, 1, 2, 3, 4, 5]]							)
        _A        =  torch.tensor([[0, 1, 1, 1, 1, 1]]							)
        with torch.no_grad():
               _A        =  model(__snake_case		,				attention_mask=__snake_case							)[0]
        _A        =  torch.Size((1, 6, 7_68)							)
        self.assertEqual(output.shape		,				__snake_case							)
        _A        =  torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]							)

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



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""							)
        _A        =  torch.tensor([[0, 1, 2, 3, 4, 5]]							)
        _A        =  torch.tensor([[1, 1, 1, 1, 1, 1]]							)
        with torch.no_grad():
               _A        =  model(__snake_case		,				attention_mask=__snake_case							)[0]
        _A        =  torch.Size((1, 6, 2_11_28)							)
        self.assertEqual(output.shape		,				__snake_case							)
        _A        =  torch.tensor(
            [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]							)

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








'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      0
				while len(SCREAMING_SNAKE_CASE__     ) > 1:
								_SCREAMING_SNAKE_CASE :       Any     		=      0
								# Consider two files with minimum cost to be merged
								for _ in range(2     ):
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      files.index(min(SCREAMING_SNAKE_CASE__     )     )
												temp += files[min_index]
												files.pop(SCREAMING_SNAKE_CASE__     )
								files.append(SCREAMING_SNAKE_CASE__     )
								optimal_merge_cost += temp
				return optimal_merge_cost


if __name__ == "__main__":
				import doctest

				doctest.testmod()




