


'''simple docstring'''






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments




@require_torch
class        a   (   unittest.TestCase				):
 def 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							str:




      for model_result in results.values():
           for batch_size, sequence_length in zip(model_result['bs']					, model_result['ss']  ):
                _a						=							model_result['''result'''][batch_size][sequence_length]
                self.assertIsNotNone(__lowerCamelCase  )
 def 							__UpperCAmelCase						(       self  )    ->							List[Any]:
      _a						=							'''sshleifer/tiny-gpt2'''
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(       self  )    ->							Tuple:
      _a						=							'''sgugger/tiny-distilbert-classification'''
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, only_pretrain_model=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(       self  )    ->							Tuple:
      _a						=							'''sshleifer/tiny-gpt2'''
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, torchscript=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 @unittest.skipIf(torch_device == 'cpu'					, 'Cant do half precision'  )
 def 							__UpperCAmelCase						(       self  )    ->							Optional[Any]:
      _a						=							'''sshleifer/tiny-gpt2'''
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, fpaa=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(       self  )    ->							List[Any]:
      _a						=							'''sshleifer/tiny-gpt2'''
      _a						=							AutoConfig.from_pretrained(__lowerCamelCase  )
      # set architectures equal to `None`
      _a						=							None
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase					, configs=[config]  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(       self  )    ->							List[Any]:
      _a						=							'''sshleifer/tiny-gpt2'''
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result  )
      self.check_results_dict_not_empty(results.memory_train_result  )
 @unittest.skipIf(torch_device == 'cpu'					, 'Can\'t do half precision'  )
 def 							__UpperCAmelCase						(       self  )    ->							Tuple:
      _a						=							'''sshleifer/tiny-gpt2'''
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, fpaa=__lowerCamelCase					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result  )
      self.check_results_dict_not_empty(results.memory_train_result  )
 def 							__UpperCAmelCase						(       self  )    ->							Optional[int]:
      _a						=							'''sshleifer/tiny-gpt2'''
      _a						=							AutoConfig.from_pretrained(__lowerCamelCase  )
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase					, configs=[config]  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(       self  )    ->							Optional[int]:
      _a						=							'''sshleifer/tinier_bart'''
      _a						=							AutoConfig.from_pretrained(__lowerCamelCase  )
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase					, configs=[config]  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result  )
      self.check_results_dict_not_empty(results.memory_inference_result  )
 def 							__UpperCAmelCase						(       self  )    ->							List[str]:
      _a						=							'''sshleifer/tiny-gpt2'''
      _a						=							AutoConfig.from_pretrained(__lowerCamelCase  )
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase					, configs=[config]  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result  )
      self.check_results_dict_not_empty(results.memory_train_result  )
 def 							__UpperCAmelCase						(       self  )    ->							List[str]:
      _a						=							'''sshleifer/tinier_bart'''
      _a						=							AutoConfig.from_pretrained(__lowerCamelCase  )
      _a						=							PyTorchBenchmarkArguments(
          models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=__lowerCamelCase					, )
      _a						=							PyTorchBenchmark(__lowerCamelCase					, configs=[config]  )
      _a						=							benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result  )
      self.check_results_dict_not_empty(results.memory_train_result  )
 def 							__UpperCAmelCase						(       self  )    ->							List[str]:
      _a						=							'''sshleifer/tiny-gpt2'''
      with tempfile.TemporaryDirectory() as tmp_dir:
           _a						=							PyTorchBenchmarkArguments(
               models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, save_to_csv=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, inference_time_csv_file=os.path.join(__lowerCamelCase					, 'inf_time.csv'  )					, train_memory_csv_file=os.path.join(__lowerCamelCase					, 'train_mem.csv'  )					, inference_memory_csv_file=os.path.join(__lowerCamelCase					, 'inf_mem.csv'  )					, train_time_csv_file=os.path.join(__lowerCamelCase					, 'train_time.csv'  )					, env_info_csv_file=os.path.join(__lowerCamelCase					, 'env.csv'  )					, multi_process=__lowerCamelCase					, )
           _a						=							PyTorchBenchmark(__lowerCamelCase  )
           benchmark.run()
           self.assertTrue(Path(os.path.join(__lowerCamelCase					, 'inf_time.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__lowerCamelCase					, 'train_time.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__lowerCamelCase					, 'inf_mem.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__lowerCamelCase					, 'train_mem.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__lowerCamelCase					, 'env.csv'  )  ).exists()  )




 def 							__UpperCAmelCase						(       self  )    ->							List[str]:
      _a						=							'''sshleifer/tiny-gpt2'''

      def _check_summary_is_not_empty(__magic_name__  ):
           self.assertTrue(hasattr(__lowerCamelCase					, 'sequential'  )  )
           self.assertTrue(hasattr(__lowerCamelCase					, 'cumulative'  )  )
           self.assertTrue(hasattr(__lowerCamelCase					, 'current'  )  )
           self.assertTrue(hasattr(__lowerCamelCase					, 'total'  )  )

      with tempfile.TemporaryDirectory() as tmp_dir:
           _a						=							PyTorchBenchmarkArguments(
               models=[MODEL_ID]					, training=__lowerCamelCase					, inference=__lowerCamelCase					, sequence_lengths=[8]					, batch_sizes=[1]					, log_filename=os.path.join(__lowerCamelCase					, 'log.txt'  )					, log_print=__lowerCamelCase					, trace_memory_line_by_line=__lowerCamelCase					, multi_process=__lowerCamelCase					, )
           _a						=							PyTorchBenchmark(__lowerCamelCase  )
           _a						=							benchmark.run()
           _check_summary_is_not_empty(result.inference_summary  )
           _check_summary_is_not_empty(result.train_summary  )
           self.assertTrue(Path(os.path.join(__lowerCamelCase					, 'log.txt'  )  ).exists()  )








"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


a_ =			logging.getLogger(__name__)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """masked_bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="topK"					,						__lowerCamelCase="constant"					,						__lowerCamelCase=0.0					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Union[str, Any]						 =     hidden_size
     __A    :						Tuple						 =     num_hidden_layers
     __A    :						Tuple						 =     num_attention_heads
     __A    :						Optional[Any]						 =     hidden_act
     __A    :						List[str]						 =     intermediate_size
     __A    :						Any						 =     hidden_dropout_prob
     __A    :						Optional[Any]						 =     attention_probs_dropout_prob
     __A    :						Any						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						List[Any]						 =     initializer_range
     __A    :						str						 =     layer_norm_eps
     __A    :						Optional[int]						 =     pruning_method
     __A    :						str						 =     mask_init
     __A    :						Any						 =     mask_scale




