





'''simple docstring'''




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf

  from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          for model_result in results.values():
               for batch_size, sequence_length in zip(model_result['bs']		,      model_result['ss']):
                    __A		:					List[str]						=			model_result['result'][batch_size][sequence_length]
                    self.assertIsNotNone(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			'sshleifer/tiny-gpt2'
          __A		:					int						=			TensorFlowBenchmarkArguments(
              models=[MODEL_ID]		,      training=_UpperCAmelCase		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=_UpperCAmelCase		,      multi_process=_UpperCAmelCase		,      )
          __A		:					Optional[int]						=			TensorFlowBenchmark(_UpperCAmelCase)
          __A		:					Any						=			benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			'sgugger/tiny-distilbert-classification'
          __A		:					Tuple						=			TensorFlowBenchmarkArguments(
              models=[MODEL_ID]		,      training=_UpperCAmelCase		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=_UpperCAmelCase		,      only_pretrain_model=_UpperCAmelCase		,      )
          __A		:					List[str]						=			TensorFlowBenchmark(_UpperCAmelCase)
          __A		:					Tuple						=			benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			'sshleifer/tiny-gpt2'
          __A		:					Any						=			TensorFlowBenchmarkArguments(
              models=[MODEL_ID]		,      training=_UpperCAmelCase		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=_UpperCAmelCase		,      )
          __A		:					int						=			TensorFlowBenchmark(_UpperCAmelCase)
          __A		:					str						=			benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			'sshleifer/tiny-gpt2'
          __A		:					Optional[int]						=			AutoConfig.from_pretrained(_UpperCAmelCase)
          __A		:					int						=			TensorFlowBenchmarkArguments(
              models=[MODEL_ID]		,      training=_UpperCAmelCase		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=_UpperCAmelCase		,      multi_process=_UpperCAmelCase		,      )
          __A		:					Dict						=			TensorFlowBenchmark(_UpperCAmelCase		,      [config])
          __A		:					Union[str, Any]						=			benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			'sshleifer/tiny-gpt2'
          __A		:					Tuple						=			AutoConfig.from_pretrained(_UpperCAmelCase)
          __A		:					List[Any]						=			TensorFlowBenchmarkArguments(
              models=[MODEL_ID]		,      training=_UpperCAmelCase		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=_UpperCAmelCase		,      )
          __A		:					Union[str, Any]						=			TensorFlowBenchmark(_UpperCAmelCase		,      [config])
          __A		:					int						=			benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			'sshleifer/tiny-gpt2'
          __A		:					Union[str, Any]						=			TensorFlowBenchmarkArguments(
              models=[MODEL_ID]		,      training=_UpperCAmelCase		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=_UpperCAmelCase		,      )
          __A		:					Any						=			TensorFlowBenchmark(_UpperCAmelCase)
          __A		:					Optional[int]						=			benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result)
          self.check_results_dict_not_empty(results.memory_train_result)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			'sshleifer/tiny-gpt2'
          __A		:					Optional[Any]						=			AutoConfig.from_pretrained(_UpperCAmelCase)
          __A		:					List[Any]						=			TensorFlowBenchmarkArguments(
              models=[MODEL_ID]		,      training=_UpperCAmelCase		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=_UpperCAmelCase		,      )
          __A		:					Tuple						=			TensorFlowBenchmark(_UpperCAmelCase		,      [config])
          __A		:					str						=			benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result)
          self.check_results_dict_not_empty(results.memory_train_result)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			'patrickvonplaten/t5-tiny-random'
          __A		:					Optional[Any]						=			AutoConfig.from_pretrained(_UpperCAmelCase)
          __A		:					Dict						=			TensorFlowBenchmarkArguments(
              models=[MODEL_ID]		,      training=_UpperCAmelCase		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=_UpperCAmelCase		,      )
          __A		:					Union[str, Any]						=			TensorFlowBenchmark(_UpperCAmelCase		,      configs=[config])
          __A		:					str						=			benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)


     @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU')) == 0		,      'Cannot do xla on CPU.')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			'sshleifer/tiny-gpt2'
          __A		:					List[Any]						=			TensorFlowBenchmarkArguments(
              models=[MODEL_ID]		,      training=_UpperCAmelCase		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      use_xla=_UpperCAmelCase		,      multi_process=_UpperCAmelCase		,      )
          __A		:					Tuple						=			TensorFlowBenchmark(_UpperCAmelCase)
          __A		:					List[Any]						=			benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			'sshleifer/tiny-gpt2'
          with tempfile.TemporaryDirectory() as tmp_dir:
               __A		:					Any						=			TensorFlowBenchmarkArguments(
                   models=[MODEL_ID]		,      inference=_UpperCAmelCase		,      save_to_csv=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      inference_time_csv_file=os.path.join(_UpperCAmelCase		,      'inf_time.csv')		,      inference_memory_csv_file=os.path.join(_UpperCAmelCase		,      'inf_mem.csv')		,      env_info_csv_file=os.path.join(_UpperCAmelCase		,      'env.csv')		,      multi_process=_UpperCAmelCase		,      )
               __A		:					str						=			TensorFlowBenchmark(_UpperCAmelCase)
               benchmark.run()
               self.assertTrue(Path(os.path.join(_UpperCAmelCase		,      'inf_time.csv')).exists())
               self.assertTrue(Path(os.path.join(_UpperCAmelCase		,      'inf_mem.csv')).exists())
               self.assertTrue(Path(os.path.join(_UpperCAmelCase		,      'env.csv')).exists())


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			'sshleifer/tiny-gpt2'

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

          with tempfile.TemporaryDirectory() as tmp_dir:
               __A		:					Tuple						=			TensorFlowBenchmarkArguments(
                   models=[MODEL_ID]		,      inference=_UpperCAmelCase		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      log_filename=os.path.join(_UpperCAmelCase		,      'log.txt')		,      log_print=_UpperCAmelCase		,      trace_memory_line_by_line=_UpperCAmelCase		,      eager_mode=_UpperCAmelCase		,      multi_process=_UpperCAmelCase		,      )
               __A		:					int						=			TensorFlowBenchmark(_UpperCAmelCase)
               __A		:					Union[str, Any]						=			benchmark.run()
               _check_summary_is_not_empty(result.inference_summary)
               self.assertTrue(Path(os.path.join(_UpperCAmelCase		,      'log.txt')).exists())






'''simple docstring'''




import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str					,				**__snake_case    :					List[Any]     )	->	Any:
     __A		:					Optional[Any]						=			AutoConfig.from_pretrained(__snake_case					,				**__snake_case     )
     __A		:					int						=			AutoModelForSeqaSeqLM.from_config(__snake_case     )
     model.save_pretrained(__snake_case     )
     AutoTokenizer.from_pretrained(__snake_case     ).save_pretrained(__snake_case     )
     return model


if __name__ == "__main__":
  fire.Fire(save_randomly_initialized_version)