
'''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 			UpperCAmelCase__    (							unittest.TestCase			):



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							Any  ):
          for model_result in results.values():
                for batch_size, sequence_length in zip(model_result["bs"],model_result["ss"]  ):
                      _lowerCamelCase					: Optional[Any]		    = model_result["result"][batch_size][sequence_length]
                      self.assertIsNotNone(__A  )



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: List[Any]		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: str		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],eager_mode=__A,multi_process=__A,)
          _lowerCamelCase					: Optional[Any]		    = TensorFlowBenchmark(__A  )
          _lowerCamelCase					: 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 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: Optional[int]		    = "sgugger/tiny-distilbert-classification"
          _lowerCamelCase					: List[str]		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,only_pretrain_model=__A,)
          _lowerCamelCase					: Dict		    = TensorFlowBenchmark(__A  )
          _lowerCamelCase					: Tuple		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase					: List[str]		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: Dict		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,)
          _lowerCamelCase					: str		    = TensorFlowBenchmark(__A  )
          _lowerCamelCase					: int		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



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



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



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



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: Dict		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: Dict		    = AutoConfig.from_pretrained(__A  )
          _lowerCamelCase					: Any		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,)
          _lowerCamelCase					: Union[str, Any]		    = TensorFlowBenchmark(__A,[config]  )
          _lowerCamelCase					: Any		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result  )
          self.check_results_dict_not_empty(results.memory_train_result  )



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: Optional[Any]		    = "patrickvonplaten/t5-tiny-random"
          _lowerCamelCase					: List[str]		    = AutoConfig.from_pretrained(__A  )
          _lowerCamelCase					: Tuple		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,)
          _lowerCamelCase					: Dict		    = TensorFlowBenchmark(__A,configs=[config]  )
          _lowerCamelCase					: List[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 	lowerCamelCase_	(						self   :							Optional[int]  ):
          _lowerCamelCase					: Tuple		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: List[str]		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],use_xla=__A,multi_process=__A,)
          _lowerCamelCase					: str		    = TensorFlowBenchmark(__A  )
          _lowerCamelCase					: List[str]		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



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



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: Optional[int]		    = "sshleifer/tiny-gpt2"

          def _check_summary_is_not_empty(__A   :							List[Any]  ):
                self.assertTrue(hasattr(__A,"sequential"  )  )
                self.assertTrue(hasattr(__A,"cumulative"  )  )
                self.assertTrue(hasattr(__A,"current"  )  )
                self.assertTrue(hasattr(__A,"total"  )  )

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






def   __lowerCamelCase   (				__a  :int = 4_0_0_0_0_0_0       )						->							int:






		"""simple docstring"""

		A__     =  []
		A__ ,     A__     =  0, 1
		while b <= n:
				if b % 2 == 0:
						even_fibs.append(__a       )
				A__ ,     A__     =  b, a + b
		return sum(__a       )


if __name__ == "__main__":
					print(F'''{solution() = }''')



