




"""simple docstring"""

import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


UpperCamelCase__ :    Tuple 					=							torch.cuda.is_available()
UpperCamelCase__ :    Optional[Any] 					=							[" Sam ate lunch today.", "Sams lunch ingredients."]
UpperCamelCase__ :    List[str] 					=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
UpperCamelCase__ :    Dict 					=							{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1e-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}




def       snake_case__	(       lowerCAmelCase   :		Path				,					lowerCAmelCase   :		list		):
					__snake_case          =	"""\n""".join(lowerCAmelCase		)
					Path(lowerCAmelCase		).open("""w"""		).writelines(lowerCAmelCase		)




def       snake_case__	(       lowerCAmelCase   :		Any		):
					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(lowerCAmelCase				,					F'''{split}.source'''		)				,					lowerCAmelCase		)
										_dump_articles(os.path.join(lowerCAmelCase				,					F'''{split}.target'''		)				,					lowerCAmelCase		)
					return tmp_dir


class     A__      (      lowercase_     ):




	@classmethod
	def 							a_		(   cls  :	Optional[Any]	)					-> Tuple:
						return cls




	@require_torch_multi_gpu
	def 							a_		(   self  :	Union[str, Any]	)					-> Any:
						__snake_case          =	{
						    """no_teacher""": True,
						    """freeze_encoder""": True,
						    """gpus""": 2,
						    """overwrite_output_dir""": True,
						    """sortish_sampler""": True,
						}
						self._test_distiller_cli_fork(lowercase      ,    check_contents=lowercase	)




	def 							a_		(   self  :	Dict      ,    lowercase  :	Any      ,    lowercase  :	Optional[Any]=True	)					-> Union[str, Any]:
						__snake_case          =	{
						    """label_smoothing""": 0.0,
						    """early_stopping_patience""": -1,
						    """train_batch_size""": 1,
						    """eval_batch_size""": 2,
						    """max_epochs""": 2,
						    """alpha_mlm""": 0.2,
						    """alpha_ce""": 0.8,
						    """do_predict""": True,
						    """model_name_or_path""": """sshleifer/tinier_bart""",
						    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
						    """val_check_interval""": 0.5,
						}
						default_updates.update(lowercase	)
						__snake_case          =	CHEAP_ARGS.copy()
						__snake_case          =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
						__snake_case          =	self.get_auto_remove_tmp_dir()
						args_d.update(data_dir=lowercase      ,    output_dir=lowercase      ,    **lowercase	)

						def convert(lowercase  :	str      ,    lowercase  :	Union[str, Any]	):
											if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																return ""
											if v is False or v is None:
																return ""
											if v is True:  # or len(str(v))==0:
																return f'''--{k}'''
											return f'''--{k}={v}'''

						__snake_case          =	[x for x in (convert(lowercase      ,    lowercase	) for k, v in args_d.items()) if len(lowercase	)]
						__snake_case          =	[sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
						execute_subprocess_async(lowercase      ,    env=self.get_env()	)

						__snake_case          =	os.listdir(lowercase	)
						__snake_case          =	{os.path.basename(lowercase	) for p in contents}
						__snake_case          =	[p for p in contents if p.endswith("""ckpt"""	)]
						assert len(lowercase	) > 0

						self.assertIn("""test_generations.txt"""      ,    lowercase	)
						self.assertIn("""test_results.txt"""      ,    lowercase	)

						# get the following from the module, (we don't have access to `model` here)
						__snake_case          =	os.path.join(lowercase      ,    """metrics.json"""	)
						__snake_case          =	"""rouge2"""

						__snake_case          =	load_json(lowercase	)
						# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
						print(lowercase	)
						__snake_case          =	metrics["""val"""][-1]
						self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]      ,    0.01	)
						self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']      ,    lowercase	)
						self.assertEqual(len(metrics["""test"""]	)      ,    1	)
						__snake_case          =	int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1	)
						self.assertEqual(len(metrics["""val"""]	)      ,    lowercase	)








"""simple docstring"""

import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
		import torch

		from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
		import pyanvml.pyanvml as nvml


UpperCamelCase__ :    List[str] 					=							logging.get_logger(__name__)
class     A__      (      lowercase_     ):
	A_							:				PyTorchBenchmarkArguments
	A_							:				PretrainedConfig
	A_							:				str						     =      "PyTorch"




	@property
	def 							a_		(   self  :	Optional[Any]	)					-> Tuple:
						return torch.__version__




	def 							a_		(   self  :	int      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> float:
						__snake_case          =	self._prepare_inference_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_speed(_inference	)




	def 							a_		(   self  :	Dict      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> [Memory, Optional[MemorySummary]]:
						__snake_case          =	self._prepare_inference_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_memory(_inference	)




	def 							a_		(   self  :	str      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> float:
						__snake_case          =	self._prepare_train_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_speed(_train	)




	def 							a_		(   self  :	List[Any]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> [Memory, Optional[MemorySummary]]:
						__snake_case          =	self._prepare_train_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_memory(_train	)




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> Callable[[], None]:
						__snake_case          =	self.config_dict[model_name]

						if self.args.torchscript:
											__snake_case          =	True

						__snake_case          =	(
						    hasattr(lowercase      ,    """architectures"""	)
						    and isinstance(config.architectures      ,    lowercase	)
						    and len(config.architectures	) > 0
						)
						if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
																__snake_case          =	config.architectures[0]
																__snake_case          =	__import__("""transformers"""      ,    fromlist=[model_class]	)
																__snake_case          =	getattr(lowercase      ,    lowercase	)
																__snake_case          =	model_cls(lowercase	)
											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:
											__snake_case          =	MODEL_MAPPING[config.__class__](lowercase	)

						model.eval()
						model.to(self.args.device	)

						# encoder-decoder has vocab size saved differently
						__snake_case          =	config.vocab_size if hasattr(lowercase      ,    """vocab_size"""	) else config.encoder.vocab_size
						__snake_case          =	torch.randint(lowercase      ,    (batch_size, sequence_length)      ,    dtype=torch.long      ,    device=self.args.device	)

						if self.args.fpaa:
											logger.info("""Running training in Mixed Precision..."""	)
											if not self.args.is_gpu:
																raise ValueError("""Mixed precision is possible only for GPU."""	)
											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

						if self.args.torchscript:
											with torch.no_grad():
																__snake_case          =	torch.jit.trace(lowercase      ,    lowercase	)
						else:
											__snake_case          =	model

						def encoder_decoder_forward():
											with torch.no_grad():
																__snake_case          =	inference_model(lowercase      ,    decoder_input_ids=lowercase	)
											return outputs

						def encoder_forward():
											with torch.no_grad():
																__snake_case          =	inference_model(lowercase	)
											return outputs

						__snake_case          =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
						return _forward




	def 							a_		(   self  :	Optional[int]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> Callable[[], None]:
						__snake_case          =	self.config_dict[model_name]

						__snake_case          =	(
						    hasattr(lowercase      ,    """architectures"""	)
						    and isinstance(config.architectures      ,    lowercase	)
						    and len(config.architectures	) > 0
						)
						if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
																__snake_case          =	config.architectures[0]
																__snake_case          =	__import__("""transformers"""      ,    fromlist=[model_class]	)
																__snake_case          =	getattr(lowercase      ,    lowercase	)
																__snake_case          =	model_cls(lowercase	)
											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:
											__snake_case          =	MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase	)

						if self.args.torchscript:
											raise NotImplementedError("""Training for torchscript is currently not implemented"""	)
						else:
											__snake_case          =	model

						model.train()
						model.to(self.args.device	)

						# encoder-decoder has vocab size saved differently
						__snake_case          =	config.vocab_size if hasattr(lowercase      ,    """vocab_size"""	) else config.encoder.vocab_size
						__snake_case          =	torch.randint(lowercase      ,    (batch_size, sequence_length)      ,    dtype=torch.long      ,    device=self.args.device	)

						if self.args.fpaa:
											logger.info("""Running training in Mixed Precision..."""	)
											if not self.args.is_gpu:
																raise ValueError("""Mixed precision is possible only for GPU."""	)

											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

						def compute_loss_and_backprob_encoder():
											__snake_case          =	train_model(lowercase      ,    labels=lowercase	)[0]
											loss.backward()
											return loss

						def compute_loss_and_backprob_encoder_decoder():
											__snake_case          =	train_model(lowercase      ,    decoder_input_ids=lowercase      ,    labels=lowercase	)[0]
											loss.backward()
											return loss

						__snake_case          =	(
						    compute_loss_and_backprob_encoder_decoder
						    if config.is_encoder_decoder
						    else compute_loss_and_backprob_encoder
						)
						return _train




	def 							a_		(   self  :	Tuple      ,    lowercase  :	List[Any]	)					-> float:
						try:
											if self.args.is_tpu or self.args.torchscript:
																# run additional 10 times to stabilize compilation for tpu and torchscript
																logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""	)
																timeit.repeat(
																    lowercase      ,    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
											__snake_case          =	timeit.repeat(
											    lowercase      ,    repeat=self.args.repeat      ,    number=10      ,    )

											if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																import torch_xla.debug.metrics as met

																self.print_fn(met.metrics_report()	)

											return min(lowercase	) / 10.0
						except RuntimeError as e:
											self.print_fn(f'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A"




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	Callable[[], None]	)					-> [Memory, MemorySummary]:
						try:
											if self.args.trace_memory_line_by_line:
																__snake_case          =	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"""
																    """ `--no-memory` or `args.memory=False`"""	)
											elif self.args.is_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."""	)
																					__snake_case          =	"""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()
																					__snake_case          =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
																					__snake_case          =	nvml.nvmlDeviceGetMemoryInfo(lowercase	)
																					__snake_case          =	meminfo.used
																					__snake_case          =	Memory(lowercase	)
																					# shutdown nvml
																					nvml.nvmlShutdown()
											else:
																# cpu
																__snake_case          =	measure_peak_memory_cpu(lowercase	)
																__snake_case          =	Memory(lowercase	) if isinstance(lowercase      ,    lowercase	) else memory_bytes

											if self.args.trace_memory_line_by_line:
																__snake_case          =	stop_memory_tracing(lowercase	)
											else:
																__snake_case          =	None

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


