


"""simple docstring"""


import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu

class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self			):
										super().__init__()
										__lowerCAmelCase       					=    nn.Linear(3					,     4			)
										__lowerCAmelCase       					=    nn.BatchNormad(4			)
										__lowerCAmelCase       					=    nn.Linear(4					,     5			)



							def     snake_case       (  self					,     __a			):
										return self.lineara(self.batchnorm(self.lineara(__a			)			)			)

class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     *__a					,     **__a			):
										return (args[0] + 1,) + args[1:], kwargs

class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a			):
										return output + 1

class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ModelForTest()
										__lowerCAmelCase       					=    ModelHook()

										add_hook_to_module(__a					,     __a			)
										self.assertEqual(test_model._hf_hook					,     __a			)
										self.assertTrue(hasattr(__a					,     "_old_forward"			)			)

										# Check adding the hook did not change the name or the signature
										self.assertEqual(test_model.forward.__name__					,     "forward"			)
										self.assertListEqual(list(inspect.signature(test_model.forward			).parameters			)					,     ["x"]			)

										remove_hook_from_module(__a			)
										self.assertFalse(hasattr(__a					,     "_hf_hook"			)			)
										self.assertFalse(hasattr(__a					,     "_old_forward"			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ModelForTest()
										__lowerCAmelCase       					=    ModelHook()

										add_hook_to_module(__a					,     __a			)
										add_hook_to_module(__a					,     __a					,     append=__a			)

										self.assertEqual(isinstance(test_model._hf_hook					,     __a			)					,     __a			)
										self.assertEqual(len(test_model._hf_hook.hooks			)					,     2			)
										self.assertTrue(hasattr(__a					,     "_old_forward"			)			)

										# Check adding the hook did not change the name or the signature
										self.assertEqual(test_model.forward.__name__					,     "forward"			)
										self.assertListEqual(list(inspect.signature(test_model.forward			).parameters			)					,     ["x"]			)

										remove_hook_from_module(__a			)
										self.assertFalse(hasattr(__a					,     "_hf_hook"			)			)
										self.assertFalse(hasattr(__a					,     "_old_forward"			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ModelForTest()
										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    test_model(x + 1			)
										__lowerCAmelCase       					=    test_model(x + 2			)

										__lowerCAmelCase       					=    PreForwardHook()
										add_hook_to_module(__a					,     __a			)
										__lowerCAmelCase       					=    test_model(__a			)
										self.assertTrue(torch.allclose(__a					,     __a					,     atol=1e-5			)			)

										# Attaching a hook to a model when it already has one replaces, does not chain
										__lowerCAmelCase       					=    PreForwardHook()
										add_hook_to_module(__a					,     __a			)
										__lowerCAmelCase       					=    test_model(__a			)
										self.assertTrue(torch.allclose(__a					,     __a					,     atol=1e-5			)			)

										# You need to use the sequential hook to chain two or more hooks
										__lowerCAmelCase       					=    SequentialHook(PreForwardHook()					,     PreForwardHook()			)
										add_hook_to_module(__a					,     __a			)

										__lowerCAmelCase       					=    test_model(__a			)
										assert torch.allclose(__a					,     __a					,     atol=1e-5			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ModelForTest()
										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    test_model(__a			)

										__lowerCAmelCase       					=    PostForwardHook()
										add_hook_to_module(__a					,     __a			)
										__lowerCAmelCase       					=    test_model(__a			)
										self.assertTrue(torch.allclose(__a					,     output + 1					,     atol=1e-5			)			)

										# Attaching a hook to a model when it already has one replaces, does not chain
										__lowerCAmelCase       					=    PostForwardHook()
										add_hook_to_module(__a					,     __a			)
										__lowerCAmelCase       					=    test_model(__a			)
										self.assertTrue(torch.allclose(__a					,     output + 1					,     atol=1e-5			)			)

										# You need to use the sequential hook to chain two or more hooks
										__lowerCAmelCase       					=    SequentialHook(PostForwardHook()					,     PostForwardHook()			)
										add_hook_to_module(__a					,     __a			)

										__lowerCAmelCase       					=    test_model(__a			)
										assert torch.allclose(__a					,     output + 2					,     atol=1e-5			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ModelForTest()
										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    test_model(__a			)

										__lowerCAmelCase       					=    PostForwardHook()
										add_hook_to_module(__a					,     __a			)
										__lowerCAmelCase       					=    test_model(__a			)
										self.assertTrue(torch.allclose(__a					,     output + 1			)			)
										self.assertTrue(outputa.requires_grad			)

										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    test_model(__a			)
										self.assertFalse(outputa.requires_grad			)

							@require_multi_gpu
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ModelForTest()
										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)

										# This will move each submodule on different devices
										add_hook_to_module(model.lineara					,     AlignDevicesHook(execution_device=0			)			)
										add_hook_to_module(model.batchnorm					,     AlignDevicesHook(execution_device=0			)			)
										add_hook_to_module(model.lineara					,     AlignDevicesHook(execution_device=1			)			)

										self.assertEqual(model.lineara.weight.device					,     torch.device(0			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device(0			)			)
										self.assertEqual(model.batchnorm.running_mean.device					,     torch.device(0			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device(1			)			)

										# We can still make a forward pass. The input does not need to be on any particular device
										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    model(__a			)
										self.assertEqual(output.device					,     torch.device(1			)			)

										# We can add a general hook to put back output on same device as input.
										add_hook_to_module(__a					,     AlignDevicesHook(io_same_device=__a			)			)
										__lowerCAmelCase       					=    torch.randn(2					,     3			).to(0			)
										__lowerCAmelCase       					=    model(__a			)
										self.assertEqual(output.device					,     torch.device(0			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)

										# This will move each submodule on different devices
										__lowerCAmelCase       					=    {"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}

										add_hook_to_module(model.lineara					,     AlignDevicesHook(**__a			)			)
										add_hook_to_module(model.batchnorm					,     AlignDevicesHook(**__a			)			)
										add_hook_to_module(model.lineara					,     AlignDevicesHook(**__a			)			)

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										# Buffers are not included in the offload by default, so are on the execution device
										__lowerCAmelCase       					=    torch.device(hook_kwargs["execution_device"]			)
										self.assertEqual(model.batchnorm.running_mean.device					,     __a			)

										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    model(__a			)
										self.assertEqual(output.device					,     __a			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_module(model.lineara			)
										remove_hook_from_module(model.batchnorm			)
										remove_hook_from_module(model.lineara			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)

										# Now test with buffers included in the offload
										__lowerCAmelCase       					=    {
										    "execution_device": 0 if torch.cuda.is_available() else "cpu",
										    "offload": True,
										    "offload_buffers": True,
										}

										add_hook_to_module(model.lineara					,     AlignDevicesHook(**__a			)			)
										add_hook_to_module(model.batchnorm					,     AlignDevicesHook(**__a			)			)
										add_hook_to_module(model.lineara					,     AlignDevicesHook(**__a			)			)

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.batchnorm.running_mean.device					,     torch.device("meta"			)			)

										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    model(__a			)
										self.assertEqual(output.device					,     __a			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_module(model.lineara			)
										remove_hook_from_module(model.batchnorm			)
										remove_hook_from_module(model.lineara			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)

										# This will move each submodule on different devices
										__lowerCAmelCase       					=    0 if torch.cuda.is_available() else "cpu"
										attach_align_device_hook(__a					,     execution_device=__a					,     offload=__a			)

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										# Buffers are not included in the offload by default, so are on the execution device
										__lowerCAmelCase       					=    torch.device(__a			)
										self.assertEqual(model.batchnorm.running_mean.device					,     __a			)

										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    model(__a			)
										self.assertEqual(output.device					,     __a			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__a			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)

										# Now test with buffers included in the offload
										attach_align_device_hook(__a					,     execution_device=__a					,     offload=__a					,     offload_buffers=__a			)

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.batchnorm.running_mean.device					,     torch.device("meta"			)			)

										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    model(__a			)
										self.assertEqual(output.device					,     __a			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__a			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)

										# This will move each submodule on different devices
										__lowerCAmelCase       					=    0 if torch.cuda.is_available() else "cpu"
										attach_align_device_hook(
										    __a					,     execution_device=__a					,     offload=__a					,     weights_map=model.state_dict()			)

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										# Buffers are not included in the offload by default, so are on the execution device
										__lowerCAmelCase       					=    torch.device(__a			)
										self.assertEqual(model.batchnorm.running_mean.device					,     __a			)

										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    model(__a			)
										self.assertEqual(output.device					,     __a			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__a			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)

										# Now test with buffers included in the offload
										attach_align_device_hook(
										    __a					,     execution_device=__a					,     offload=__a					,     weights_map=model.state_dict()					,     offload_buffers=__a					,     )

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("meta"			)			)
										self.assertEqual(model.batchnorm.running_mean.device					,     torch.device("meta"			)			)

										__lowerCAmelCase       					=    torch.randn(2					,     3			)
										__lowerCAmelCase       					=    model(__a			)
										self.assertEqual(output.device					,     __a			)

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__a			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.batchnorm.weight.device					,     torch.device("cpu"			)			)
										self.assertEqual(model.lineara.weight.device					,     torch.device("cpu"			)			)








"""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	):
							'''simple docstring'''







							def     snake_case       (  self					,     __a			):
										for model_result in results.values():
													for batch_size, sequence_length in zip(model_result["bs"]					,     model_result["ss"]			):
																__lowerCAmelCase       					=    model_result["result"][batch_size][sequence_length]
																self.assertIsNotNone(__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sgugger/tiny-distilbert-classification"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     only_pretrain_model=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "patrickvonplaten/t5-tiny-random"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     configs=[config]			)
										__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     use_xla=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    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					,     )
													__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"

										def _check_summary_is_not_empty(__a			):
													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:
													__lowerCAmelCase       					=    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					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													__lowerCAmelCase       					=    benchmark.run()
													_check_summary_is_not_empty(result.inference_summary			)
													self.assertTrue(Path(os.path.join(__a					,     "log.txt"			)			).exists()			)




