






"""simple docstring"""





from ... import PretrainedConfig


__lowerCamelCase							=  {
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
					UpperCAmelCase__								=				"nezha"

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      Union[str, Any]=2_1_1_2_8     ,		__snake_case  :      Optional[int]=7_6_8     ,		__snake_case  :      Dict=1_2     ,		__snake_case  :      Union[str, Any]=1_2     ,		__snake_case  :      Tuple=3_0_7_2     ,		__snake_case  :      List[str]="gelu"     ,		__snake_case  :      Union[str, Any]=0.1     ,		__snake_case  :      List[Any]=0.1     ,		__snake_case  :      Tuple=5_1_2     ,		__snake_case  :      str=6_4     ,		__snake_case  :      Optional[Any]=2     ,		__snake_case  :      List[Any]=0.02     ,		__snake_case  :      List[str]=1E-12     ,		__snake_case  :      List[Any]=0.1     ,		__snake_case  :      Optional[int]=0     ,		__snake_case  :      Any=2     ,		__snake_case  :      int=3     ,		__snake_case  :      Any=True     ,		**__snake_case  :      Tuple     ,		)			->							List[Any]:
											super().__init__(pad_token_id=__snake_case     ,		bos_token_id=__snake_case     ,		eos_token_id=__snake_case     ,		**__snake_case					)

											__magic_name__:	List[str]							=       vocab_size
											__magic_name__:	Optional[int]							=       hidden_size
											__magic_name__:	Optional[Any]							=       num_hidden_layers
											__magic_name__:	Union[str, Any]							=       num_attention_heads
											__magic_name__:	str							=       hidden_act
											__magic_name__:	Optional[int]							=       intermediate_size
											__magic_name__:	Dict							=       hidden_dropout_prob
											__magic_name__:	Dict							=       attention_probs_dropout_prob
											__magic_name__:	List[Any]							=       max_position_embeddings
											__magic_name__:	Any							=       max_relative_position
											__magic_name__:	Tuple							=       type_vocab_size
											__magic_name__:	Optional[Any]							=       initializer_range
											__magic_name__:	str							=       layer_norm_eps
											__magic_name__:	Tuple							=       classifier_dropout
											__magic_name__:	Tuple							=       use_cache







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 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									for model_result in results.values():
																for batch_size, sequence_length in zip(model_result['bs']     ,       model_result['ss']		):
																							_UpperCAmelCase											=     model_result['result'][batch_size][sequence_length]
																							self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       eager_mode=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     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		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     'sgugger/tiny-distilbert-classification'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       only_pretrain_model=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     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[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     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[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       eager_mode=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       [config]		)
									_UpperCAmelCase											=     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[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       [config]		)
									_UpperCAmelCase											=     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[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     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		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       [config]		)
									_UpperCAmelCase											=     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		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     'patrickvonplaten/t5-tiny-random'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       configs=[config]		)
									_UpperCAmelCase											=     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       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       use_xla=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     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:




									"""simple docstring"""





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






		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'

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

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