


'''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__  )   ->							List[Any]:
      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(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _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  )   ->							str:
      _a      =							'sgugger/tiny-distilbert-classification'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			only_pretrain_model=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _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  )   ->							Union[str, Any]:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			torchscript=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _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  )   ->							List[str]:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			fpaa=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _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(__magic_name__  )
      # set architectures equal to `None`
      _a      =							None
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			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  )   ->							Union[str, Any]:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _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=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			fpaa=__magic_name__ ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__  )
      _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  )   ->							int:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							AutoConfig.from_pretrained(__magic_name__  )
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			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/tinier_bart'
      _a      =							AutoConfig.from_pretrained(__magic_name__  )
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			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  )   ->							Any:
      _a      =							'sshleifer/tiny-gpt2'
      _a      =							AutoConfig.from_pretrained(__magic_name__  )
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			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  )   ->							Optional[Any]:
      _a      =							'sshleifer/tinier_bart'
      _a      =							AutoConfig.from_pretrained(__magic_name__  )
      _a      =							PyTorchBenchmarkArguments(
          models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__magic_name__ ,			)
      _a      =							PyTorchBenchmark(__magic_name__ ,			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  )   ->							Dict:
      _a      =							'sshleifer/tiny-gpt2'
      with tempfile.TemporaryDirectory() as tmp_dir:
           _a      =							PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			save_to_csv=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			inference_time_csv_file=os.path.join(__magic_name__ ,			'inf_time.csv'  ) ,			train_memory_csv_file=os.path.join(__magic_name__ ,			'train_mem.csv'  ) ,			inference_memory_csv_file=os.path.join(__magic_name__ ,			'inf_mem.csv'  ) ,			train_time_csv_file=os.path.join(__magic_name__ ,			'train_time.csv'  ) ,			env_info_csv_file=os.path.join(__magic_name__ ,			'env.csv'  ) ,			multi_process=__magic_name__ ,			)
           _a      =							PyTorchBenchmark(__magic_name__  )
           benchmark.run()
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'inf_time.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'train_time.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'inf_mem.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'train_mem.csv'  )  ).exists()  )
           self.assertTrue(Path(os.path.join(__magic_name__ ,			'env.csv'  )  ).exists()  )




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

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

      with tempfile.TemporaryDirectory() as tmp_dir:
           _a      =							PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=__magic_name__ ,			inference=__magic_name__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			log_filename=os.path.join(__magic_name__ ,			'log.txt'  ) ,			log_print=__magic_name__ ,			trace_memory_line_by_line=__magic_name__ ,			multi_process=__magic_name__ ,			)
           _a      =							PyTorchBenchmark(__magic_name__  )
           _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(__magic_name__ ,			'log.txt'  )  ).exists()  )





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      int				    =      {
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      [
     "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXJapaneseForCausalLM",
     "GPTNeoXJapaneseLayer",
     "GPTNeoXJapaneseModel",
     "GPTNeoXJapanesePreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 a_   :      int				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

