





'''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  lowerCAmelCase_		:




 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=128      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=None      ,  )     ->				str:
     _lowerCAmelCase						      =   parent
     _lowerCAmelCase						      =   batch_size
     _lowerCAmelCase						      =   seq_length
     _lowerCAmelCase						      =   is_training
     _lowerCAmelCase						      =   use_input_mask
     _lowerCAmelCase						      =   use_token_type_ids
     _lowerCAmelCase						      =   use_labels
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   max_position_embeddings
     _lowerCAmelCase						      =   type_vocab_size
     _lowerCAmelCase						      =   type_sequence_label_size
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   num_labels
     _lowerCAmelCase						      =   num_choices
     _lowerCAmelCase						      =   scope




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size		)

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

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

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

     _lowerCAmelCase						      =   self.get_config()

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




 def 				_snake_case (						self		)     ->				Dict:
     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 				_snake_case (						self		)     ->				str:
     (
         (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      
     )						      =   self.prepare_config_and_inputs()

     _lowerCAmelCase						      =   True
     _lowerCAmelCase						      =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   NezhaModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase		)
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )     ->				Optional[int]:
     _lowerCAmelCase						      =   True
     _lowerCAmelCase						      =   NezhaModel(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  encoder_hidden_states=_lowerCAmelCase      ,  encoder_attention_mask=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  encoder_hidden_states=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   NezhaForMaskedLM(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   NezhaForNextSentencePrediction(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase      ,  )
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, 2)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   NezhaForPreTraining(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   NezhaForQuestionAnswering(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   self.num_labels
     _lowerCAmelCase						      =   NezhaForSequenceClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   self.num_labels
     _lowerCAmelCase						      =   NezhaForTokenClassification(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   self.num_choices
     _lowerCAmelCase						      =   NezhaForMultipleChoice(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   input_ids.unsqueeze(1		).expand(-1      ,  self.num_choices      ,  -1		).contiguous()
     _lowerCAmelCase						      =   token_type_ids.unsqueeze(1		).expand(-1      ,  self.num_choices      ,  -1		).contiguous()
     _lowerCAmelCase						      =   input_mask.unsqueeze(1		).expand(-1      ,  self.num_choices      ,  -1		).contiguous()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase      ,  )
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_choices)		)





 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.prepare_config_and_inputs()
     (
         (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      
     )						      =   config_and_inputs
     _lowerCAmelCase						      =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict







@require_torch
class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Dict								=       (
     (
         NezhaModel,
         NezhaForMaskedLM,
         NezhaForMultipleChoice,
         NezhaForNextSentencePrediction,
         NezhaForPreTraining,
         NezhaForQuestionAnswering,
         NezhaForSequenceClassification,
         NezhaForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase    :				str								=       (
     {
         "feature-extraction": NezhaModel,
         "fill-mask": NezhaForMaskedLM,
         "question-answering": NezhaForQuestionAnswering,
         "text-classification": NezhaForSequenceClassification,
         "token-classification": NezhaForTokenClassification,
         "zero-shot": NezhaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCamelCase    :				str								=       True




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=False		)     ->				Any:
     _lowerCAmelCase						      =   super()._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)

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




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   NezhaModelTester(self		)
     _lowerCAmelCase						      =   ConfigTester(self      ,  config_class=_lowerCAmelCase      ,  hidden_size=37		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.config_tester.run_common_tests()




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(*_lowerCAmelCase		)




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

     _lowerCAmelCase						      =   None

     self.model_tester.create_and_check_model_as_decoder(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_next_sequence_prediction(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase		)




 @slow
 def 				_snake_case (						self		)     ->				Optional[Any]:
     for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						      =   NezhaModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)





 @slow
 @require_torch_gpu
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   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

         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   model_class(config=_lowerCAmelCase		)

         _lowerCAmelCase						      =   self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   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"		)		)
             _lowerCAmelCase						      =   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  lowerCAmelCase_		(		unittest.TestCase		):




 @slow
 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   NezhaModel.from_pretrained("sijunhe/nezha-cn-base"		)
     _lowerCAmelCase						      =   torch.tensor([[0, 1, 2, 3, 4, 5]]		)
     _lowerCAmelCase						      =   torch.tensor([[0, 1, 1, 1, 1, 1]]		)
     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase		)[0]
     _lowerCAmelCase						      =   torch.Size((1, 6, 768)		)
     self.assertEqual(output.shape      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   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]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)





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








from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
				import torch

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


SCREAMING_SNAKE_CASE__     :     int		    =       logging.get_logger(__name__)



@dataclass
class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= [
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]





	def __init__(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				for deprecated_arg in self.deprecated_args:
							if deprecated_arg in kwargs:
										SCREAMING_SNAKE_CASE_								=						deprecated_arg[3:]
										setattr(self      ,     SCREAMING_SNAKE_CASE      ,     not kwargs.pop(SCREAMING_SNAKE_CASE      )      )
										logger.warning(
										    f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
										    f' {positive_arg}={kwargs[positive_arg]}'      )

				SCREAMING_SNAKE_CASE_								=						kwargs.pop('torchscript'      ,     self.torchscript      )
				SCREAMING_SNAKE_CASE_								=						kwargs.pop('torch_xla_tpu_print_metrics'      ,     self.torch_xla_tpu_print_metrics      )
				SCREAMING_SNAKE_CASE_								=						kwargs.pop('fp16_opt_level'      ,     self.fpaa_opt_level      )
				super().__init__(**SCREAMING_SNAKE_CASE      )

	A    							= field(default=SCREAMING_SNAKE_CASE__  ,    metadata={'''help''': '''Trace the models using torchscript'''}     )
	A    							= field(default=SCREAMING_SNAKE_CASE__  ,    metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}     )
	A    							= field(
	    default='''O1'''  ,    metadata={
	        '''help''': (
	            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
	            '''See details at https://nvidia.github.io/apex/amp.html'''
	        )
	    }  ,    )





	@cached_property
	def 						A_(							self      )  ->					Tuple["torch.device", int]:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				logger.info('PyTorch: setting up devices'      )
				if not self.cuda:
							SCREAMING_SNAKE_CASE_								=						torch.device('cpu'      )
							SCREAMING_SNAKE_CASE_								=						0
				elif is_torch_tpu_available():
							SCREAMING_SNAKE_CASE_								=						xm.xla_device()
							SCREAMING_SNAKE_CASE_								=						0
				else:
							SCREAMING_SNAKE_CASE_								=						torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
							SCREAMING_SNAKE_CASE_								=						torch.cuda.device_count()
				return device, n_gpu





	@property
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				return is_torch_tpu_available() and self.tpu





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				# TODO(PVP): currently only single GPU is supported
				return torch.cuda.current_device()





	@property
	def 						A_(							self      )  ->					"torch.device":




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				return self._setup_devices[0]





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				return self._setup_devices[1]





	@property
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				return self.n_gpu > 0
