





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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):




														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(_snake_case					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sshleifer/tiny-gpt2"""
														UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,	training=_snake_case					,	inference=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	eager_mode=_snake_case					,	multi_process=_snake_case					,	)
														UpperCamelCase__			       =     TensorFlowBenchmark(_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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sgugger/tiny-distilbert-classification"""
														UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,	training=_snake_case					,	inference=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	multi_process=_snake_case					,	only_pretrain_model=_snake_case					,	)
														UpperCamelCase__			       =     TensorFlowBenchmark(_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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sshleifer/tiny-gpt2"""
														UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,	training=_snake_case					,	inference=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	multi_process=_snake_case					,	)
														UpperCamelCase__			       =     TensorFlowBenchmark(_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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sshleifer/tiny-gpt2"""
														UpperCamelCase__			       =     AutoConfig.from_pretrained(_snake_case					)
														UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,	training=_snake_case					,	inference=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	eager_mode=_snake_case					,	multi_process=_snake_case					,	)
														UpperCamelCase__			       =     TensorFlowBenchmark(_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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sshleifer/tiny-gpt2"""
														UpperCamelCase__			       =     AutoConfig.from_pretrained(_snake_case					)
														UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,	training=_snake_case					,	inference=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	multi_process=_snake_case					,	)
														UpperCamelCase__			       =     TensorFlowBenchmark(_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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sshleifer/tiny-gpt2"""
														UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,	training=_snake_case					,	inference=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	multi_process=_snake_case					,	)
														UpperCamelCase__			       =     TensorFlowBenchmark(_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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sshleifer/tiny-gpt2"""
														UpperCamelCase__			       =     AutoConfig.from_pretrained(_snake_case					)
														UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,	training=_snake_case					,	inference=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	multi_process=_snake_case					,	)
														UpperCamelCase__			       =     TensorFlowBenchmark(_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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """patrickvonplaten/t5-tiny-random"""
														UpperCamelCase__			       =     AutoConfig.from_pretrained(_snake_case					)
														UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,	training=_snake_case					,	inference=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	multi_process=_snake_case					,	)
														UpperCamelCase__			       =     TensorFlowBenchmark(_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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sshleifer/tiny-gpt2"""
														UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]					,	training=_snake_case					,	inference=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	use_xla=_snake_case					,	multi_process=_snake_case					,	)
														UpperCamelCase__			       =     TensorFlowBenchmark(_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 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sshleifer/tiny-gpt2"""
														with tempfile.TemporaryDirectory() as tmp_dir:
																					UpperCamelCase__			       =     TensorFlowBenchmarkArguments(
																					    models=[MODEL_ID]					,	inference=_snake_case					,	save_to_csv=_snake_case					,	sequence_lengths=[8]					,	batch_sizes=[1]					,	inference_time_csv_file=os.path.join(_snake_case					,	"""inf_time.csv"""					)					,	inference_memory_csv_file=os.path.join(_snake_case					,	"""inf_mem.csv"""					)					,	env_info_csv_file=os.path.join(_snake_case					,	"""env.csv"""					)					,	multi_process=_snake_case					,	)
																					UpperCamelCase__			       =     TensorFlowBenchmark(_snake_case					)
																					benchmark.run()
																					self.assertTrue(Path(os.path.join(_snake_case					,	"""inf_time.csv"""					)					).exists()					)
																					self.assertTrue(Path(os.path.join(_snake_case					,	"""inf_mem.csv"""					)					).exists()					)
																					self.assertTrue(Path(os.path.join(_snake_case					,	"""env.csv"""					)					).exists()					)







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """sshleifer/tiny-gpt2"""

														def _check_summary_is_not_empty(__lowerCAmelCase					):
																					self.assertTrue(hasattr(_snake_case					,	"""sequential"""					)					)
																					self.assertTrue(hasattr(_snake_case					,	"""cumulative"""					)					)
																					self.assertTrue(hasattr(_snake_case					,	"""current"""					)					)
																					self.assertTrue(hasattr(_snake_case					,	"""total"""					)					)

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCamelCase__     =	{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	["GPTSw3Tokenizer"]


if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_swa import GPTSwaTokenizer

else:
					import sys

					UpperCamelCase__     =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




