






'''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 _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				int     ):






														for model_result in results.values():
																					for batch_size, sequence_length in zip(model_result["""bs"""]			,							model_result["""ss"""]     ):
																												lowerCamelCase__					:	int         =       model_result["""result"""][batch_size][sequence_length]
																												self.assertIsNotNone(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       """sshleifer/tiny-gpt2"""
														lowerCamelCase__					:	str         =       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
														lowerCamelCase__					:	Dict         =       TensorFlowBenchmark(UpperCamelCase__     )
														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:				Optional[Any]     ):
														lowerCamelCase__					:	Tuple         =       """sgugger/tiny-distilbert-classification"""
														lowerCamelCase__					:	List[Any]         =       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							only_pretrain_model=UpperCamelCase__			,							)
														lowerCamelCase__					:	int         =       TensorFlowBenchmark(UpperCamelCase__     )
														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:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       """sshleifer/tiny-gpt2"""
														lowerCamelCase__					:	Any         =       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
														lowerCamelCase__					:	Any         =       TensorFlowBenchmark(UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       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:				Tuple     ):
														lowerCamelCase__					:	int         =       """sshleifer/tiny-gpt2"""
														lowerCamelCase__					:	int         =       AutoConfig.from_pretrained(UpperCamelCase__     )
														lowerCamelCase__					:	List[str]         =       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
														lowerCamelCase__					:	List[Any]         =       TensorFlowBenchmark(UpperCamelCase__			,							[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:				Any     ):
														lowerCamelCase__					:	Tuple         =       """sshleifer/tiny-gpt2"""
														lowerCamelCase__					:	Optional[Any]         =       AutoConfig.from_pretrained(UpperCamelCase__     )
														lowerCamelCase__					:	Union[str, Any]         =       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
														lowerCamelCase__					:	List[str]         =       TensorFlowBenchmark(UpperCamelCase__			,							[config]     )
														lowerCamelCase__					:	Dict         =       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[Any]     ):
														lowerCamelCase__					:	List[Any]         =       """sshleifer/tiny-gpt2"""
														lowerCamelCase__					:	List[Any]         =       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
														lowerCamelCase__					:	Optional[Any]         =       TensorFlowBenchmark(UpperCamelCase__     )
														lowerCamelCase__					:	List[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:				Optional[Any]     ):
														lowerCamelCase__					:	Union[str, Any]         =       """sshleifer/tiny-gpt2"""
														lowerCamelCase__					:	Optional[int]         =       AutoConfig.from_pretrained(UpperCamelCase__     )
														lowerCamelCase__					:	Optional[int]         =       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
														lowerCamelCase__					:	List[Any]         =       TensorFlowBenchmark(UpperCamelCase__			,							[config]     )
														lowerCamelCase__					:	Union[str, 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:				Union[str, Any]     ):
														lowerCamelCase__					:	Dict         =       """patrickvonplaten/t5-tiny-random"""
														lowerCamelCase__					:	Union[str, Any]         =       AutoConfig.from_pretrained(UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
														lowerCamelCase__					:	List[str]         =       TensorFlowBenchmark(UpperCamelCase__			,							configs=[config]     )
														lowerCamelCase__					:	int         =       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:				Tuple     ):
														lowerCamelCase__					:	List[Any]         =       """sshleifer/tiny-gpt2"""
														lowerCamelCase__					:	Dict         =       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							use_xla=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
														lowerCamelCase__					:	List[str]         =       TensorFlowBenchmark(UpperCamelCase__     )
														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:				Union[str, Any]     ):
														lowerCamelCase__					:	Any         =       """sshleifer/tiny-gpt2"""
														with tempfile.TemporaryDirectory() as tmp_dir:
																					lowerCamelCase__					:	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__			,							)
																					lowerCamelCase__					:	List[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 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[int]         =       """sshleifer/tiny-gpt2"""

														def _check_summary_is_not_empty(UpperCamelCase__:				Dict     ):
																					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:
																					lowerCamelCase__					:	List[str]         =       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__			,							)
																					lowerCamelCase__					:	int         =       TensorFlowBenchmark(UpperCamelCase__     )
																					lowerCamelCase__					:	Dict         =       benchmark.run()
																					_check_summary_is_not_empty(result.inference_summary     )
																					self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""log.txt"""     )     ).exists()     )



from __future__ import annotations

from collections.abc import Iterator




class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Dict			,							UpperCamelCase__			: int    )			->	None:







    """simple docstring"""






    __magic_name__				     =       value
    __magic_name__				     =       None
    __magic_name__				     =       None







class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Union[str, Any]			,							UpperCamelCase__			: Node    )			->	None:







    """simple docstring"""






    __magic_name__				     =       tree
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: Node | None    )			->	int:







    """simple docstring"""






    if node is None:
      return 0
    return node.value + (
        self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
    )





  def __iter__( self			: int    )			->	Iterator[int]:







    """simple docstring"""






    yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

