




import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=None   ):
				__magic_name__	:	int								=None
				if token is not None:
								__magic_name__	:	List[Any]								={"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

				__magic_name__	:	Optional[Any]								=F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
				__magic_name__	:	Dict								=requests.get(lowerCamelCase	,     headers=lowerCamelCase   ).json()
				__magic_name__	:	Union[str, Any]								={}

				try:
								job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )
								__magic_name__	:	Dict								=math.ceil((result["""total_count"""] - 100) / 100   )

								for i in range(lowerCamelCase   ):
												__magic_name__	:	List[Any]								=requests.get(url + F"&page={i + 2}"	,     headers=lowerCamelCase   ).json()
												job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )

								return job_links
				except Exception:
								print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"   )

				return {}




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=None   ):
				__magic_name__	:	int								=None
				if token is not None:
								__magic_name__	:	str								={"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

				__magic_name__	:	Optional[Any]								=F"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
				__magic_name__	:	Optional[Any]								=requests.get(lowerCamelCase	,     headers=lowerCamelCase   ).json()
				__magic_name__	:	List[Any]								={}

				try:
								artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )
								__magic_name__	:	Any								=math.ceil((result["""total_count"""] - 100) / 100   )

								for i in range(lowerCamelCase   ):
												__magic_name__	:	str								=requests.get(url + F"&page={i + 2}"	,     headers=lowerCamelCase   ).json()
												artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )

								return artifacts
				except Exception:
								print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"   )

				return {}




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Optional[Any]								=None
				if token is not None:
								__magic_name__	:	Any								={"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

				__magic_name__	:	int								=requests.get(lowerCamelCase	,     headers=lowerCamelCase	,     allow_redirects=lowerCamelCase   )
				__magic_name__	:	str								=result.headers["""Location"""]
				__magic_name__	:	int								=requests.get(lowerCamelCase	,     allow_redirects=lowerCamelCase   )
				__magic_name__	:	Optional[int]								=os.path.join(lowerCamelCase	,     F"{artifact_name}.zip"   )
				with open(lowerCamelCase	,     """wb"""   ) as fp:
								fp.write(response.content   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=None   ):
				__magic_name__	:	Any								=[]
				__magic_name__	:	Tuple								=[]
				__magic_name__	:	Any								=None

				with zipfile.ZipFile(lowerCamelCase   ) as z:
								for filename in z.namelist():
												if not os.path.isdir(lowerCamelCase   ):
																# read the file
																if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
																				with z.open(lowerCamelCase   ) as f:
																								for line in f:
																												__magic_name__	:	str								=line.decode("""UTF-8"""   ).strip()
																												if filename == "failures_line.txt":
																																try:
																																				# `error_line` is the place where `error` occurs
																																				__magic_name__	:	Union[str, Any]								=line[: line.index(""": """   )]
																																				__magic_name__	:	List[str]								=line[line.index(""": """   ) + len(""": """   ) :]
																																				errors.append([error_line, error]   )
																																except Exception:
																																				# skip un-related lines
																																				pass
																												elif filename == "summary_short.txt" and line.startswith("""FAILED """   ):
																																# `test` is the test method that failed
																																__magic_name__	:	Dict								=line[len("""FAILED """   ) :]
																																failed_tests.append(lowerCamelCase   )
																												elif filename == "job_name.txt":
																																__magic_name__	:	Optional[int]								=line

				if len(lowerCamelCase   ) != len(lowerCamelCase   ):
								raise ValueError(
								    F"`errors` and `failed_tests` should have the same number of elements. Got {len(lowerCamelCase   )} for `errors` "
								    F"and {len(lowerCamelCase   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"
								    """ problem."""   )

				__magic_name__	:	List[Any]								=None
				if job_name and job_links:
								__magic_name__	:	Union[str, Any]								=job_links.get(lowerCamelCase	,     lowerCamelCase   )

				# A list with elements of the form (line of error, error, failed test)
				__magic_name__	:	Union[str, Any]								=[x + [y] + [job_link] for x, y in zip(lowerCamelCase	,     lowerCamelCase   )]

				return result




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=None   ):
				__magic_name__	:	List[str]								=[]

				__magic_name__	:	Union[str, Any]								=[os.path.join(lowerCamelCase	,     lowerCamelCase   ) for p in os.listdir(lowerCamelCase   ) if p.endswith(""".zip"""   )]
				for p in paths:
								errors.extend(get_errors_from_single_artifact(lowerCamelCase	,     job_links=lowerCamelCase   )   )

				return errors




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=None   ):
				__magic_name__	:	Optional[Any]								=Counter()
				counter.update([x[1] for x in logs]   )
				__magic_name__	:	Any								=counter.most_common()
				__magic_name__	:	Union[str, Any]								={}
				for error, count in counts:
								if error_filter is None or error not in error_filter:
												__magic_name__	:	Optional[Any]								={"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

				__magic_name__	:	Any								=dict(sorted(r.items()	,     key=lambda lowerCamelCase   : item[1]["count"]	,     reverse=lowerCamelCase   )   )
				return r




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	str								=test.split("""::"""   )[0]
				if test.startswith("""tests/models/"""   ):
								__magic_name__	:	List[Any]								=test.split("""/"""   )[2]
				else:
								__magic_name__	:	str								=None

				return test




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=None   ):
				__magic_name__	:	str								=[(x[0], x[1], get_model(x[2]   )) for x in logs]
				__magic_name__	:	List[str]								=[x for x in logs if x[2] is not None]
				__magic_name__	:	List[str]								={x[2] for x in logs}

				__magic_name__	:	int								={}
				for test in tests:
								__magic_name__	:	Any								=Counter()
								# count by errors in `test`
								counter.update([x[1] for x in logs if x[2] == test]   )
								__magic_name__	:	int								=counter.most_common()
								__magic_name__	:	int								={error: count for error, count in counts if (error_filter is None or error not in error_filter)}
								__magic_name__	:	List[str]								=sum(error_counts.values()   )
								if n_errors > 0:
												__magic_name__	:	List[str]								={"""count""": n_errors, """errors""": error_counts}

				__magic_name__	:	List[Any]								=dict(sorted(r.items()	,     key=lambda lowerCamelCase   : item[1]["count"]	,     reverse=lowerCamelCase   )   )
				return r




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	List[Any]								="""| no. | error | status |"""
				__magic_name__	:	Any								="""|-:|:-|:-|"""
				__magic_name__	:	Optional[int]								=[header, sep]
				for error in reduced_by_error:
								__magic_name__	:	Optional[int]								=reduced_by_error[error]["""count"""]
								__magic_name__	:	str								=F"| {count} | {error[:100]} |  |"
								lines.append(lowerCamelCase   )

				return "\n".join(lowerCamelCase   )




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								="""| model | no. of errors | major error | count |"""
				__magic_name__	:	List[str]								="""|-:|-:|-:|-:|"""
				__magic_name__	:	Dict								=[header, sep]
				for model in reduced_by_model:
								__magic_name__	:	Dict								=reduced_by_model[model]["""count"""]
								__magic_name__				, __magic_name__	:	Optional[int]								=list(reduced_by_model[model]["""errors"""].items()   )[0]
								__magic_name__	:	int								=F"| {model} | {count} | {error[:60]} | {_count} |"
								lines.append(lowerCamelCase   )

				return "\n".join(lowerCamelCase   )


if __name__ == "__main__":
							UpperCAmelCase_		:				List[str]									=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
							parser.add_argument(
							    "--output_dir",
							    type=str,
							    required=True,
							    help="Where to store the downloaded artifacts and other result files.",
							)
							parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
							UpperCAmelCase_		:				Optional[int]									=     parser.parse_args()

							os.makedirs(args.output_dir, exist_ok=True)

							UpperCAmelCase_		:				Optional[Any]									=     get_job_links(args.workflow_run_id, token=args.token)
							UpperCAmelCase_		:				Any									=     {}
							# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
							# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
							if _job_links:
														for k, v in _job_links.items():
																					# This is how GitHub actions combine job names.
																					if " / " in k:
																												UpperCAmelCase_		:				List[str]									=     k.find(" / ")
																												UpperCAmelCase_		:				str									=     k[index + len(" / ") :]
																					UpperCAmelCase_		:				Optional[Any]									=     v
							with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
														json.dump(job_links, fp, ensure_ascii=False, indent=4)

							UpperCAmelCase_		:				List[Any]									=     get_artifacts_links(args.workflow_run_id, token=args.token)
							with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
														json.dump(artifacts, fp, ensure_ascii=False, indent=4)

							for idx, (name, url) in enumerate(artifacts.items()):
														download_artifact(name, url, args.output_dir, args.token)
														# Be gentle to GitHub
														time.sleep(1)

							UpperCAmelCase_		:				Dict									=     get_all_errors(args.output_dir, job_links=job_links)

							# `e[1]` is the error
							UpperCAmelCase_		:				List[Any]									=     Counter()
							counter.update([e[1] for e in errors])

							# print the top 30 most common test errors
							UpperCAmelCase_		:				Optional[int]									=     counter.most_common(30)
							for item in most_common:
														print(item)

							with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
														json.dump(errors, fp, ensure_ascii=False, indent=4)

							UpperCAmelCase_		:				Optional[int]									=     reduce_by_error(errors)
							UpperCAmelCase_		:				Optional[Any]									=     reduce_by_model(errors)

							UpperCAmelCase_		:				Tuple									=     make_github_table(reduced_by_error)
							UpperCAmelCase_		:				Union[str, Any]									=     make_github_table_per_model(reduced_by_model)

							with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
														fp.write(sa)
							with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
														fp.write(sa)






import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.framework.errors_impl import ResourceExhaustedError

							from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
							import pyanvml.pyanvml as nvml

UpperCAmelCase_		:				Optional[Any]									=     logging.get_logger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				def run_func(lowerCamelCase   ):
								@wraps(lowerCamelCase   )
								def run_in_eager_mode(*lowerCamelCase	,     **lowerCamelCase   ):
												return func(*lowerCamelCase	,     **lowerCamelCase   )

								@wraps(lowerCamelCase   )
								@tf.function(experimental_compile=lowerCamelCase   )
								def run_in_graph_mode(*lowerCamelCase	,     **lowerCamelCase   ):
												return func(*lowerCamelCase	,     **lowerCamelCase   )

								if do_eager_mode is True:
												if use_xla is not False:
																raise ValueError(
																    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."""   )
												return run_in_eager_mode
								else:
												return run_in_graph_mode

				return run_func




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Dict								=random.Random()
				__magic_name__	:	Union[str, Any]								=[rng.randint(0	,     vocab_size - 1   ) for i in range(batch_size * sequence_length   )]
				return tf.constant(lowerCamelCase	,     shape=(batch_size, sequence_length)	,     dtype=tf.intaa   )




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					42
						UpperCamelCase     						=					42
						UpperCamelCase     						=					"TensorFlow"

						@property
						def     A__						(		self   :str ):
										'''simple docstring'''

										return tf.__version__

						def     A__						(		self   :str    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
										__magic_name__	:	Union[str, Any]								=self._prepare_inference_func(__snake_case    , __snake_case    , __snake_case )
										return self._measure_speed(_inference )

						def     A__						(		self   :int    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
										__magic_name__	:	Any								=self._prepare_train_func(__snake_case    , __snake_case    , __snake_case )
										return self._measure_speed(_train )

						def     A__						(		self   :str    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										if self.args.is_gpu:
														tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]    , __snake_case )
										__magic_name__	:	int								=self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
										__magic_name__	:	Tuple								=self._prepare_inference_func(__snake_case    , __snake_case    , __snake_case )
										return self._measure_memory(_inference )

						def     A__						(		self   :str    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										if self.args.is_gpu:
														tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]    , __snake_case )
										__magic_name__	:	Tuple								=self.args.strategy
										if strategy is None:
														raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )

										__magic_name__	:	Any								=self._prepare_train_func(__snake_case    , __snake_case    , __snake_case )
										return self._measure_memory(_train )

						def     A__						(		self   :int    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	int								=self.config_dict[model_name]

										if self.args.fpaa:
														raise NotImplementedError("""Mixed precision is currently not supported.""" )

										__magic_name__	:	Any								=(
										    hasattr(__snake_case    , """architectures""" )
										    and isinstance(config.architectures    , __snake_case )
										    and len(config.architectures ) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																		__magic_name__	:	Optional[int]								="""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																		__magic_name__	:	Optional[int]								=__import__("""transformers"""    , fromlist=[model_class] )
																		__magic_name__	:	Optional[Any]								=getattr(__snake_case    , __snake_case )
																		__magic_name__	:	Optional[Any]								=model_cls(__snake_case )
														except ImportError:
																		raise ImportError(
																		    f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																		    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
										else:
														__magic_name__	:	Optional[int]								=TF_MODEL_MAPPING[config.__class__](__snake_case )

										# encoder-decoder has vocab size saved differently
										__magic_name__	:	List[str]								=config.vocab_size if hasattr(__snake_case    , """vocab_size""" ) else config.encoder.vocab_size
										__magic_name__	:	Any								=random_input_ids(__snake_case    , __snake_case    , __snake_case )

										@run_with_tf_optimizations(self.args.eager_mode    , self.args.use_xla )
										def encoder_decoder_forward():
														return model(__snake_case    , decoder_input_ids=__snake_case    , training=__snake_case )

										@run_with_tf_optimizations(self.args.eager_mode    , self.args.use_xla )
										def encoder_forward():
														return model(__snake_case    , training=__snake_case )

										__magic_name__	:	Tuple								=encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

										return _inference

						def     A__						(		self   :int    , __snake_case   :str    , __snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.config_dict[model_name]

										if self.args.eager_mode is not False:
														raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )

										if self.args.fpaa:
														raise NotImplementedError("""Mixed precision is currently not supported.""" )

										__magic_name__	:	int								=(
										    hasattr(__snake_case    , """architectures""" )
										    and isinstance(config.architectures    , __snake_case )
										    and len(config.architectures ) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																		__magic_name__	:	List[Any]								="""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																		__magic_name__	:	Optional[int]								=__import__("""transformers"""    , fromlist=[model_class] )
																		__magic_name__	:	str								=getattr(__snake_case    , __snake_case )
																		__magic_name__	:	List[Any]								=model_cls(__snake_case )
														except ImportError:
																		raise ImportError(
																		    f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																		    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
										else:
														__magic_name__	:	int								=TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__snake_case )

										# encoder-decoder has vocab size saved differently
										__magic_name__	:	int								=config.vocab_size if hasattr(__snake_case    , """vocab_size""" ) else config.encoder.vocab_size
										__magic_name__	:	Optional[Any]								=random_input_ids(__snake_case    , __snake_case    , __snake_case )

										@run_with_tf_optimizations(self.args.eager_mode    , self.args.use_xla )
										def encoder_decoder_train():
														__magic_name__	:	List[str]								=model(__snake_case    , decoder_input_ids=__snake_case    , labels=__snake_case    , training=__snake_case )[0]
														__magic_name__	:	int								=tf.gradients(__snake_case    , model.trainable_variables )
														return gradients

										@run_with_tf_optimizations(self.args.eager_mode    , self.args.use_xla )
										def encoder_train():
														__magic_name__	:	str								=model(__snake_case    , labels=__snake_case    , training=__snake_case )[0]
														__magic_name__	:	int								=tf.gradients(__snake_case    , model.trainable_variables )
														return gradients

										__magic_name__	:	Union[str, Any]								=encoder_decoder_train if config.is_encoder_decoder else encoder_train

										return _train

						def     A__						(		self   :Any    , __snake_case   :Union[str, Any] ):
										'''simple docstring'''

										with self.args.strategy.scope():
														try:
																		if self.args.is_tpu or self.args.use_xla:
																						# run additional 10 times to stabilize compilation for tpu
																						logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
																						timeit.repeat(__snake_case    , repeat=1    , number=5 )

																		# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																		__magic_name__	:	Union[str, Any]								=timeit.repeat(
																		    __snake_case    , repeat=self.args.repeat    , number=10    , )

																		return min(__snake_case ) / 10.0
														except ResourceExhaustedError as e:
																		self.print_fn(f"Doesn't fit on GPU. {e}" )


						def     A__						(		self   :Any    , __snake_case   :Callable[[], None] ):
										'''simple docstring'''

										logger.info(
										    """Note that TensorFlow allocates more memory than """
										    """it might need to speed up computation. """
										    """The memory reported here corresponds to the memory """
										    """reported by `nvidia-smi`, which can vary depending """
										    """on total available memory on the GPU that is used.""" )
										with self.args.strategy.scope():
														try:
																		if self.args.trace_memory_line_by_line:
																						if not self.args.eager_mode:
																										raise ValueError(
																										    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
																										    """ consumption line by line.""" )
																						__magic_name__	:	Union[str, Any]								=start_memory_tracing("""transformers""" )

																		if self.args.is_tpu:
																						# tpu
																						raise NotImplementedError(
																						    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
																						    """ with `args.memory=False`""" )
																		elif self.args.is_gpu:
																						# gpu
																						if not is_pyanvml_available():
																										logger.warning(
																										    """py3nvml not installed, we won't log GPU memory usage. """
																										    """Install py3nvml (pip install py3nvml) to log information about GPU.""" )
																										__magic_name__	:	str								="""N/A"""
																						else:
																										logger.info(
																										    """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
																										    """ running on the same GPU.""" )
																										# init nvml
																										nvml.nvmlInit()
																										func()
																										__magic_name__	:	List[str]								=nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
																										__magic_name__	:	Tuple								=nvml.nvmlDeviceGetMemoryInfo(__snake_case )
																										__magic_name__	:	Any								=meminfo.used
																										__magic_name__	:	str								=Memory(__snake_case )
																										# shutdown nvml
																										nvml.nvmlShutdown()
																		else:
																						# cpu
																						if self.args.trace_memory_line_by_line:
																										logger.info(
																										    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
																										    """ TensorFlow.""" )
																										__magic_name__	:	List[str]								=None
																						else:
																										__magic_name__	:	List[Any]								=measure_peak_memory_cpu(__snake_case )
																										__magic_name__	:	str								=Memory(__snake_case ) if isinstance(__snake_case    , __snake_case ) else memory_bytes
																		if self.args.trace_memory_line_by_line:
																						__magic_name__	:	List[Any]								=stop_memory_tracing(__snake_case )
																						if memory is None:
																										__magic_name__	:	Any								=summary.total
																		else:
																						__magic_name__	:	Optional[Any]								=None

																		return memory, summary
														except ResourceExhaustedError as e:
																		self.print_fn(f"Doesn't fit on GPU. {e}" )
																		return "N/A", None




