






"""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   A  (				self     :						List[Any]    ,						A     :						Optional[int]   )  ->  Optional[Any]:
							for model_result in results.values():
									for batch_size, sequence_length in zip(model_result['''bs''']    ,						model_result['''ss''']   ):
											lowercase_ : Union[str, Any]							       =   model_result['''result'''][batch_size][sequence_length]
											self.assertIsNotNone(A   )




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




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




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




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




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




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




					def   A  (				self     :						Optional[Any]   )  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   '''patrickvonplaten/t5-tiny-random'''
							lowercase_ : Optional[Any]							       =   AutoConfig.from_pretrained(A   )
							lowercase_ : Dict							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : List[Any]							       =   TensorFlowBenchmark(A    ,						configs=[config]   )
							lowercase_ : 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   )




					@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''   )   ) == 0    ,						'''Cannot do xla on CPU.'''   )
					def   A  (				self     :						int   )  ->  List[str]:
							lowercase_ : int							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Tuple							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						use_xla=A    ,						multi_process=A    ,						)
							lowercase_ : Optional[Any]							       =   TensorFlowBenchmark(A   )
							lowercase_ : Tuple							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						Any   )  ->  Optional[int]:
							lowercase_ : Union[str, Any]							       =   '''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
									lowercase_ : str							       =   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    ,						)
									lowercase_ : List[str]							       =   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   A  (				self     :						str   )  ->  List[Any]:
							lowercase_ : Tuple							       =   '''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(A     :						Optional[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:
									lowercase_ : Optional[int]							       =   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    ,						)
									lowercase_ : int							       =   TensorFlowBenchmark(A   )
									lowercase_ : List[Any]							       =   benchmark.run()
									_check_summary_is_not_empty(result.inference_summary   )
									self.assertTrue(Path(os.path.join(A    ,						'''log.txt'''   )   ).exists()   )











"""simple docstring"""



from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
						from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



