




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
					import botoa  # noqa: F401




def 							_UpperCAmelCase  (UpperCamelCase_ : Any       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Tuple										=      botoa.client("""iam"""       )

				_lowerCAmelCase    :    Optional[Any]										=      {
				    """Version""": """2012-10-17""",
				    """Statement""": [
				        {"""Effect""": """Allow""", """Principal""": {"""Service""": """sagemaker.amazonaws.com"""}, """Action""": """sts:AssumeRole"""}
				    ],
				}
				try:
								# create the role, associated with the chosen trust policy
								iam_client.create_role(
								    RoleName=UpperCamelCase_      ,    AssumeRolePolicyDocument=json.dumps(UpperCamelCase_      ,    indent=2       )       )
								_lowerCAmelCase    :    List[Any]										=      {
								    """Version""": """2012-10-17""",
								    """Statement""": [
								        {
								            """Effect""": """Allow""",
								            """Action""": [
								                """sagemaker:*""",
								                """ecr:GetDownloadUrlForLayer""",
								                """ecr:BatchGetImage""",
								                """ecr:BatchCheckLayerAvailability""",
								                """ecr:GetAuthorizationToken""",
								                """cloudwatch:PutMetricData""",
								                """cloudwatch:GetMetricData""",
								                """cloudwatch:GetMetricStatistics""",
								                """cloudwatch:ListMetrics""",
								                """logs:CreateLogGroup""",
								                """logs:CreateLogStream""",
								                """logs:DescribeLogStreams""",
								                """logs:PutLogEvents""",
								                """logs:GetLogEvents""",
								                """s3:CreateBucket""",
								                """s3:ListBucket""",
								                """s3:GetBucketLocation""",
								                """s3:GetObject""",
								                """s3:PutObject""",
								            ],
								            """Resource""": """*""",
								        }
								    ],
								}
								# attach policy to role
								iam_client.put_role_policy(
								    RoleName=UpperCamelCase_      ,    PolicyName=F"{role_name}_policy_permission"      ,    PolicyDocument=json.dumps(UpperCamelCase_      ,    indent=2       )      ,    )
				except iam_client.exceptions.EntityAlreadyExistsException:
								print(F"role {role_name} already exists. Using existing one"       )




def 							_UpperCAmelCase  (UpperCamelCase_ : Tuple       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      botoa.client("""iam"""       )
				return iam_client.get_role(RoleName=UpperCamelCase_       )["Role"]["Arn"]





def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    str										=      _ask_options(
				    """How do you want to authorize?"""      ,    ["""AWS Profile""", """Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) """]      ,    UpperCamelCase_      ,    )
				_lowerCAmelCase    :    Tuple										=      None
				if credentials_configuration == 0:
								_lowerCAmelCase    :    Any										=      _ask_field("""Enter your AWS Profile name: [default] """      ,    default="""default"""       )
								_lowerCAmelCase    :    Optional[Any]										=      aws_profile
				else:
								print(
								    """Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"""
								    """`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`"""       )
								_lowerCAmelCase    :    Any										=      _ask_field("""AWS Access Key ID: """       )
								_lowerCAmelCase    :    Optional[Any]										=      aws_access_key_id

								_lowerCAmelCase    :    Optional[Any]										=      _ask_field("""AWS Secret Access Key: """       )
								_lowerCAmelCase    :    List[Any]										=      aws_secret_access_key

				_lowerCAmelCase    :    Any										=      _ask_field("""Enter your AWS Region: [us-east-1]"""      ,    default="""us-east-1"""       )
				_lowerCAmelCase    :    int										=      aws_region

				_lowerCAmelCase    :    Any										=      _ask_options(
				    """Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?"""      ,    ["""Provide IAM Role name""", """Create new IAM role using credentials"""]      ,    UpperCamelCase_      ,    )
				if role_management == 0:
								_lowerCAmelCase    :    Optional[int]										=      _ask_field("""Enter your IAM role name: """       )
				else:
								_lowerCAmelCase    :    Optional[int]										=      """accelerate_sagemaker_execution_role"""
								print(F"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"       )
								_create_iam_role_for_sagemaker(UpperCamelCase_       )

				_lowerCAmelCase    :    List[Any]										=      _ask_field(
				    """Do you want to use custom Docker image? [yes/NO]: """      ,    _convert_yes_no_to_bool      ,    default=UpperCamelCase_      ,    error_message="""Please enter yes or no."""      ,    )
				_lowerCAmelCase    :    Union[str, Any]										=      None
				if is_custom_docker_image:
								_lowerCAmelCase    :    str										=      _ask_field("""Enter your Docker image: """      ,    lambda UpperCamelCase_       : str(UpperCamelCase_       ).lower()       )

				_lowerCAmelCase    :    Union[str, Any]										=      _ask_field(
				    """Do you want to provide SageMaker input channels with data locations? [yes/NO]: """      ,    _convert_yes_no_to_bool      ,    default=UpperCamelCase_      ,    error_message="""Please enter yes or no."""      ,    )
				_lowerCAmelCase    :    Optional[Any]										=      None
				if is_sagemaker_inputs_enabled:
								_lowerCAmelCase    :    Union[str, Any]										=      _ask_field(
								    """Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): """      ,    lambda UpperCamelCase_       : str(UpperCamelCase_       ).lower()      ,    )

				_lowerCAmelCase    :    Tuple										=      _ask_field(
				    """Do you want to enable SageMaker metrics? [yes/NO]: """      ,    _convert_yes_no_to_bool      ,    default=UpperCamelCase_      ,    error_message="""Please enter yes or no."""      ,    )
				_lowerCAmelCase    :    str										=      None
				if is_sagemaker_metrics_enabled:
								_lowerCAmelCase    :    str										=      _ask_field(
								    """Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): """      ,    lambda UpperCamelCase_       : str(UpperCamelCase_       ).lower()      ,    )

				_lowerCAmelCase    :    Any										=      _ask_options(
				    """What is the distributed mode?"""      ,    ["""No distributed training""", """Data parallelism"""]      ,    _convert_sagemaker_distributed_mode      ,    )
				_lowerCAmelCase    :    Optional[int]										=      {}
				_lowerCAmelCase    :    Tuple										=      _ask_field(
				    """Do you wish to optimize your script with torch dynamo?[yes/NO]:"""      ,    _convert_yes_no_to_bool      ,    default=UpperCamelCase_      ,    error_message="""Please enter yes or no."""      ,    )
				if use_dynamo:
								_lowerCAmelCase    :    Optional[int]										=      """dynamo_"""
								_lowerCAmelCase    :    Tuple										=      _ask_options(
								    """Which dynamo backend would you like to use?"""      ,    [x.lower() for x in DYNAMO_BACKENDS]      ,    _convert_dynamo_backend      ,    default=2      ,    )
								_lowerCAmelCase    :    Optional[Any]										=      _ask_field(
								    """Do you want to customize the defaults sent to torch.compile? [yes/NO]: """      ,    _convert_yes_no_to_bool      ,    default=UpperCamelCase_      ,    error_message="""Please enter yes or no."""      ,    )

								if use_custom_options:
												_lowerCAmelCase    :    Optional[Any]										=      _ask_options(
												    """Which mode do you want to use?"""      ,    UpperCamelCase_      ,    lambda UpperCamelCase_       : TORCH_DYNAMO_MODES[int(UpperCamelCase_       )]      ,    default="""default"""      ,    )
												_lowerCAmelCase    :    List[Any]										=      _ask_field(
												    """Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: """      ,    _convert_yes_no_to_bool      ,    default=UpperCamelCase_      ,    error_message="""Please enter yes or no."""      ,    )
												_lowerCAmelCase    :    Dict										=      _ask_field(
												    """Do you want to enable dynamic shape tracing? [yes/NO]: """      ,    _convert_yes_no_to_bool      ,    default=UpperCamelCase_      ,    error_message="""Please enter yes or no."""      ,    )
				_lowerCAmelCase    :    Optional[Any]										=      """Which EC2 instance type you want to use for your training?"""
				if distributed_type != SageMakerDistributedType.NO:
								_lowerCAmelCase    :    Optional[int]										=      _ask_options(
								    UpperCamelCase_      ,    UpperCamelCase_      ,    lambda UpperCamelCase_       : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCamelCase_       )]       )
				else:
								eca_instance_query += "? [ml.p3.2xlarge]:"
								_lowerCAmelCase    :    int										=      _ask_field(UpperCamelCase_      ,    lambda UpperCamelCase_       : str(UpperCamelCase_       ).lower()      ,    default="""ml.p3.2xlarge"""       )

				_lowerCAmelCase    :    Any										=      1
				if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
								_lowerCAmelCase    :    int										=      _ask_field(
								    """How many machines do you want use? [1]: """      ,    UpperCamelCase_      ,    default=1      ,    )

				_lowerCAmelCase    :    List[str]										=      _ask_options(
				    """Do you wish to use FP16 or BF16 (mixed precision)?"""      ,    ["""no""", """fp16""", """bf16""", """fp8"""]      ,    _convert_mixed_precision      ,    )

				if use_dynamo and mixed_precision == "no":
								print(
								    """Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts."""       )

				return SageMakerConfig(
				    image_uri=UpperCamelCase_      ,    compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER      ,    distributed_type=UpperCamelCase_      ,    use_cpu=UpperCamelCase_      ,    dynamo_config=UpperCamelCase_      ,    eca_instance_type=UpperCamelCase_      ,    profile=UpperCamelCase_      ,    region=UpperCamelCase_      ,    iam_role_name=UpperCamelCase_      ,    mixed_precision=UpperCamelCase_      ,    num_machines=UpperCamelCase_      ,    sagemaker_inputs_file=UpperCamelCase_      ,    sagemaker_metrics_file=UpperCamelCase_      ,    )







import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
	from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    List[str]:





								'''simple docstring'''




								for model_result in results.values():
												for batch_size, sequence_length in zip(model_result["""bs"""]		,	model_result["""ss"""]      ):
																_lowerCAmelCase    :    List[Any]										=      model_result["""result"""][batch_size][sequence_length]
																self.assertIsNotNone(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      PyTorchBenchmark(_UpperCAmelCase      )
								_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       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      """sgugger/tiny-distilbert-classification"""
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	only_pretrain_model=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_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       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	torchscript=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_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      )



				@unittest.skipIf(torch_device == """cpu"""		,	"""Cant do half precision"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	fpaa=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_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       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    int										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								# set architectures equal to `None`
								_lowerCAmelCase    :    Optional[int]										=      None
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    List[Any]										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result      )
								self.check_results_dict_not_empty(results.memory_inference_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result      )
								self.check_results_dict_not_empty(results.memory_train_result      )



				@unittest.skipIf(torch_device == """cpu"""		,	"""Can't do half precision"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	fpaa=_UpperCAmelCase		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_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       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    List[Any]										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_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       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Tuple										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmark(_UpperCAmelCase		,	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      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[int]										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    Optional[Any]										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result      )
								self.check_results_dict_not_empty(results.memory_train_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Dict										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    int										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result      )
								self.check_results_dict_not_empty(results.memory_train_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
												_lowerCAmelCase    :    Optional[int]										=      PyTorchBenchmarkArguments(
												    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	save_to_csv=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	inference_time_csv_file=os.path.join(_UpperCAmelCase		,	"""inf_time.csv"""      )		,	train_memory_csv_file=os.path.join(_UpperCAmelCase		,	"""train_mem.csv"""      )		,	inference_memory_csv_file=os.path.join(_UpperCAmelCase		,	"""inf_mem.csv"""      )		,	train_time_csv_file=os.path.join(_UpperCAmelCase		,	"""train_time.csv"""      )		,	env_info_csv_file=os.path.join(_UpperCAmelCase		,	"""env.csv"""      )		,	multi_process=_UpperCAmelCase		,	)
												_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
												benchmark.run()
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""inf_time.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""train_time.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""inf_mem.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""train_mem.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""env.csv"""      )      ).exists()      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(_UpperCAmelCase      :		int      ):
												self.assertTrue(hasattr(_UpperCAmelCase		,	"""sequential"""      )      )
												self.assertTrue(hasattr(_UpperCAmelCase		,	"""cumulative"""      )      )
												self.assertTrue(hasattr(_UpperCAmelCase		,	"""current"""      )      )
												self.assertTrue(hasattr(_UpperCAmelCase		,	"""total"""      )      )

								with tempfile.TemporaryDirectory() as tmp_dir:
												_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
												    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	log_filename=os.path.join(_UpperCAmelCase		,	"""log.txt"""      )		,	log_print=_UpperCAmelCase		,	trace_memory_line_by_line=_UpperCAmelCase		,	multi_process=_UpperCAmelCase		,	)
												_lowerCAmelCase    :    str										=      PyTorchBenchmark(_UpperCAmelCase      )
												_lowerCAmelCase    :    Any										=      benchmark.run()
												_check_summary_is_not_empty(result.inference_summary      )
												_check_summary_is_not_empty(result.train_summary      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""log.txt"""      )      ).exists()      )

