
"""simple docstring"""







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 			A__	(     _lowerCamelCase):
		A_						:	Any           =     (PNDMScheduler,)
		A_						:	str           =     (('num_inference_steps', 5_0),)

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Union[str, Any]						      =				{
			    'num_train_timesteps': 10_00,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			}

			config.update(**_SCREAMING_SNAKE_CASE			)
			return config

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=0	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				dict(self.forward_default_kwargs			)
			__lowerCAmelCase      :						Optional[int]						      =				kwargs.pop('num_inference_steps'	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				self.dummy_sample
			__lowerCAmelCase      :						List[Any]						      =				0.1 * sample
			__lowerCAmelCase      :						Any						      =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase      :						Optional[int]						      =				self.get_scheduler_config(**_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						int						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)
				scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
				# copy over dummy past residuals
				__lowerCAmelCase      :						Tuple						      =				dummy_past_residuals[:]

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(_SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase      :						str						      =				scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE			)
					new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
					# copy over dummy past residuals
					__lowerCAmelCase      :						str						      =				dummy_past_residuals[:]

				__lowerCAmelCase      :						Optional[Any]						      =				scheduler.step_prk(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample
				__lowerCAmelCase      :						str						      =				new_scheduler.step_prk(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample

				assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"

				__lowerCAmelCase      :						Dict						      =				scheduler.step_plms(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample
				__lowerCAmelCase      :						List[str]						      =				new_scheduler.step_plms(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample

				assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"

		def 							__lowerCamelCase							(				self			):
			pass

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=0	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Tuple						      =				dict(self.forward_default_kwargs			)
			__lowerCAmelCase      :						Union[str, Any]						      =				kwargs.pop('num_inference_steps'	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				self.dummy_sample
			__lowerCAmelCase      :						Dict						      =				0.1 * sample
			__lowerCAmelCase      :						int						      =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase      :						Tuple						      =				self.get_scheduler_config()
				__lowerCAmelCase      :						List[str]						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)
				scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

				# copy over dummy past residuals (must be after setting timesteps)
				__lowerCAmelCase      :						Tuple						      =				dummy_past_residuals[:]

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(_SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase      :						Optional[Any]						      =				scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE			)
					# copy over dummy past residuals
					new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

					# copy over dummy past residual (must be after setting timesteps)
					__lowerCAmelCase      :						Union[str, Any]						      =				dummy_past_residuals[:]

				__lowerCAmelCase      :						int						      =				scheduler.step_prk(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample
				__lowerCAmelCase      :						str						      =				new_scheduler.step_prk(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample

				assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"

				__lowerCAmelCase      :						Optional[int]						      =				scheduler.step_plms(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample
				__lowerCAmelCase      :						List[Any]						      =				new_scheduler.step_plms(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample

				assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[Any]						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						str						      =				self.get_scheduler_config(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				10
			__lowerCAmelCase      :						Tuple						      =				self.dummy_model()
			__lowerCAmelCase      :						Optional[Any]						      =				self.dummy_sample_deter
			scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

			for i, t in enumerate(scheduler.prk_timesteps			):
				__lowerCAmelCase      :						Optional[int]						      =				model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						int						      =				scheduler.step_prk(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			).prev_sample

			for i, t in enumerate(scheduler.plms_timesteps			):
				__lowerCAmelCase      :						Optional[Any]						      =				model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Union[str, Any]						      =				scheduler.step_plms(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			).prev_sample

			return sample

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				dict(self.forward_default_kwargs			)

			__lowerCAmelCase      :						str						      =				kwargs.pop('num_inference_steps'	,		_SCREAMING_SNAKE_CASE			)

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase      :						str						      =				self.get_scheduler_config()
				__lowerCAmelCase      :						Any						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						int						      =				self.dummy_sample
				__lowerCAmelCase      :						str						      =				0.1 * sample

				if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE	,		'set_timesteps'			):
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
				elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE	,		'set_timesteps'			):
					__lowerCAmelCase      :						List[Any]						      =				num_inference_steps

				# copy over dummy past residuals (must be done after set_timesteps)
				__lowerCAmelCase      :						Dict						      =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
				__lowerCAmelCase      :						Tuple						      =				dummy_past_residuals[:]

				__lowerCAmelCase      :						Dict						      =				scheduler.step_prk(_SCREAMING_SNAKE_CASE	,		0	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample
				__lowerCAmelCase      :						str						      =				scheduler.step_prk(_SCREAMING_SNAKE_CASE	,		1	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample

				self.assertEqual(output_a.shape	,		sample.shape			)
				self.assertEqual(output_a.shape	,		output_a.shape			)

				__lowerCAmelCase      :						Optional[Any]						      =				scheduler.step_plms(_SCREAMING_SNAKE_CASE	,		0	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample
				__lowerCAmelCase      :						Union[str, Any]						      =				scheduler.step_plms(_SCREAMING_SNAKE_CASE	,		1	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).prev_sample

				self.assertEqual(output_a.shape	,		sample.shape			)
				self.assertEqual(output_a.shape	,		output_a.shape			)

		def 							__lowerCamelCase							(				self			):
			for timesteps in [1_00, 10_00]:
				self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for steps_offset in [0, 1]:
				self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[int]						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						Tuple						      =				self.get_scheduler_config(steps_offset=1			)
			__lowerCAmelCase      :						int						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)
			scheduler.set_timesteps(10			)
			assert torch.equal(
			    scheduler.timesteps	,		torch.LongTensor(
			        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]			)	,		)

		def 							__lowerCamelCase							(				self			):
			for beta_start, beta_end in zip([0.0001, 0.001]	,		[0.002, 0.02]			):
				self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE	,		beta_end=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for schedule in ["linear", "squaredcos_cap_v2"]:
				self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for t in [1, 5, 10]:
				self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for t, num_inference_steps in zip([1, 5, 10]	,		[10, 50, 1_00]			):
				self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
			__lowerCAmelCase      :						List[str]						      =				27

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase      :						Optional[Any]						      =				self.dummy_sample
				__lowerCAmelCase      :						Dict						      =				0.1 * sample

				__lowerCAmelCase      :						List[str]						      =				self.get_scheduler_config()
				__lowerCAmelCase      :						Any						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

				scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

				# before power of 3 fix, would error on first step, so we only need to do two
				for i, t in enumerate(scheduler.prk_timesteps[:2]			):
					__lowerCAmelCase      :						int						      =				scheduler.step_prk(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			).prev_sample

		def 							__lowerCamelCase							(				self			):
			with self.assertRaises(_SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase      :						List[str]						      =				self.scheduler_classes[0]
				__lowerCAmelCase      :						Any						      =				self.get_scheduler_config()
				__lowerCAmelCase      :						Union[str, Any]						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

				scheduler.step_plms(self.dummy_sample	,		1	,		self.dummy_sample			).prev_sample

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				self.full_loop()
			__lowerCAmelCase      :						Tuple						      =				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						Union[str, Any]						      =				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

			assert abs(result_sum.item() - 198.1318			) < 1E-2
			assert abs(result_mean.item() - 0.2580			) < 1E-3

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				self.full_loop(prediction_type='v_prediction'			)
			__lowerCAmelCase      :						Union[str, Any]						      =				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						Any						      =				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

			assert abs(result_sum.item() - 67.3986			) < 1E-2
			assert abs(result_mean.item() - 0.0878			) < 1E-3

		def 							__lowerCamelCase							(				self			):
			# We specify different beta, so that the first alpha is 0.99
			__lowerCAmelCase      :						Optional[int]						      =				self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE	,		beta_start=0.01			)
			__lowerCAmelCase      :						List[str]						      =				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						Optional[int]						      =				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

			assert abs(result_sum.item() - 230.0399			) < 1E-2
			assert abs(result_mean.item() - 0.2995			) < 1E-3







		def 							__lowerCamelCase							(				self			):
			# We specify different beta, so that the first alpha is 0.99
			__lowerCAmelCase      :						str						      =				self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE	,		beta_start=0.01			)
			__lowerCAmelCase      :						Union[str, Any]						      =				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						Any						      =				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

			assert abs(result_sum.item() - 186.9482			) < 1E-2
			assert abs(result_mean.item() - 0.2434			) < 1E-3

"""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 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			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(_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						int						      =				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	,		)
			__lowerCAmelCase      :						Tuple						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[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			):
			__lowerCAmelCase      :						int						      =				'sgugger/tiny-distilbert-classification'
			__lowerCAmelCase      :						Any						      =				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	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__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			):
			__lowerCAmelCase      :						Optional[int]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__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			):
			__lowerCAmelCase      :						Union[str, Any]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						int						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				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	,		)
			__lowerCAmelCase      :						Dict						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						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			):
			__lowerCAmelCase      :						List[Any]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Tuple						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[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			):
			__lowerCAmelCase      :						Any						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				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			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Any						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[Any]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__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			):
			__lowerCAmelCase      :						Optional[int]						      =				'patrickvonplaten/t5-tiny-random'
			__lowerCAmelCase      :						int						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Dict						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		configs=[config]			)
			__lowerCAmelCase      :						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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						List[Any]						      =				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	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__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			):
			__lowerCAmelCase      :						Optional[int]						      =				'sshleifer/tiny-gpt2'
			with tempfile.TemporaryDirectory() as tmp_dir:
				__lowerCAmelCase      :						List[str]						      =				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	,		)
				__lowerCAmelCase      :						Tuple						      =				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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				'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:
				__lowerCAmelCase      :						Any						      =				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	,		)
				__lowerCAmelCase      :						Any						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						int						      =				benchmark.run()
				_check_summary_is_not_empty(result.inference_summary			)
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'log.txt'			)			).exists()			)