



"""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


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

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				42
					_a       =				42
					_a       =				"PyTorch"



					@property
					def 						snake_case			(			self				:    Dict	)->   List[str]:
								return torch.__version__



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   float:
								lowerCamelCase__						:						List[Any]						      =self._prepare_inference_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_speed(_inference	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   [Memory, Optional[MemorySummary]]:
								lowerCamelCase__						:						List[Any]						      =self._prepare_inference_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_memory(_inference	)



					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   float:
								lowerCamelCase__						:						int						      =self._prepare_train_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_speed(_train	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   [Memory, Optional[MemorySummary]]:
								lowerCamelCase__						:						str						      =self._prepare_train_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_memory(_train	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   Callable[[], None]:
								lowerCamelCase__						:						List[str]						      =self.config_dict[model_name]

								if self.args.torchscript:
											lowerCamelCase__						:						int						      =True

								lowerCamelCase__						:						Optional[Any]						      =(
								    hasattr(lowerCamelCase,    '''architectures'''	)
								    and isinstance(config.architectures,    lowerCamelCase	)
								    and len(config.architectures	) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														lowerCamelCase__						:						int						      =config.architectures[0]
														lowerCamelCase__						:						Dict						      =__import__('''transformers''',    fromlist=[model_class]	)
														lowerCamelCase__						:						str						      =getattr(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Tuple						      =model_cls(lowerCamelCase	)
											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:
											lowerCamelCase__						:						List[str]						      =MODEL_MAPPING[config.__class__](lowerCamelCase	)

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

								# encoder-decoder has vocab size saved differently
								lowerCamelCase__						:						Optional[Any]						      =config.vocab_size if hasattr(lowerCamelCase,    '''vocab_size'''	) else config.encoder.vocab_size
								lowerCamelCase__						:						int						      =torch.randint(lowerCamelCase,    (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():
														lowerCamelCase__						:						Optional[Any]						      =torch.jit.trace(lowerCamelCase,    lowerCamelCase	)
								else:
											lowerCamelCase__						:						Tuple						      =model

								def encoder_decoder_forward():
											with torch.no_grad():
														lowerCamelCase__						:						Optional[Any]						      =inference_model(lowerCamelCase,    decoder_input_ids=lowerCamelCase	)
											return outputs

								def encoder_forward():
											with torch.no_grad():
														lowerCamelCase__						:						Any						      =inference_model(lowerCamelCase	)
											return outputs

								lowerCamelCase__						:						Optional[int]						      =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
								return _forward



					def 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   Callable[[], None]:
								lowerCamelCase__						:						Any						      =self.config_dict[model_name]

								lowerCamelCase__						:						Tuple						      =(
								    hasattr(lowerCamelCase,    '''architectures'''	)
								    and isinstance(config.architectures,    lowerCamelCase	)
								    and len(config.architectures	) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														lowerCamelCase__						:						List[Any]						      =config.architectures[0]
														lowerCamelCase__						:						int						      =__import__('''transformers''',    fromlist=[model_class]	)
														lowerCamelCase__						:						int						      =getattr(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Dict						      =model_cls(lowerCamelCase	)
											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:
											lowerCamelCase__						:						List[str]						      =MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase	)

								if self.args.torchscript:
											raise NotImplementedError('''Training for torchscript is currently not implemented'''	)
								else:
											lowerCamelCase__						:						str						      =model

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

								# encoder-decoder has vocab size saved differently
								lowerCamelCase__						:						Dict						      =config.vocab_size if hasattr(lowerCamelCase,    '''vocab_size'''	) else config.encoder.vocab_size
								lowerCamelCase__						:						int						      =torch.randint(lowerCamelCase,    (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():
											lowerCamelCase__						:						Optional[int]						      =train_model(lowerCamelCase,    labels=lowerCamelCase	)[0]
											loss.backward()
											return loss

								def compute_loss_and_backprob_encoder_decoder():
											lowerCamelCase__						:						Dict						      =train_model(lowerCamelCase,    decoder_input_ids=lowerCamelCase,    labels=lowerCamelCase	)[0]
											loss.backward()
											return loss

								lowerCamelCase__						:						Optional[Any]						      =(
								    compute_loss_and_backprob_encoder_decoder
								    if config.is_encoder_decoder
								    else compute_loss_and_backprob_encoder
								)
								return _train



					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Optional[int]	)->   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(
														    lowerCamelCase,    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
											lowerCamelCase__						:						List[str]						      =timeit.repeat(
											    lowerCamelCase,    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(lowerCamelCase	) / 10.0
								except RuntimeError as e:
											self.print_fn(F'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A"




					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Callable[[], None]	)->   [Memory, MemorySummary]:
								try:
											if self.args.trace_memory_line_by_line:
														lowerCamelCase__						:						Optional[int]						      =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.'''	)
																	lowerCamelCase__						:						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()
																	lowerCamelCase__						:						int						      =nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
																	lowerCamelCase__						:						Optional[Any]						      =nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase	)
																	lowerCamelCase__						:						str						      =meminfo.used
																	lowerCamelCase__						:						List[str]						      =Memory(lowerCamelCase	)
																	# shutdown nvml
																	nvml.nvmlShutdown()
											else:
														# cpu
														lowerCamelCase__						:						List[Any]						      =measure_peak_memory_cpu(lowerCamelCase	)
														lowerCamelCase__						:						List[Any]						      =Memory(lowerCamelCase	) if isinstance(lowerCamelCase,    lowerCamelCase	) else memory_bytes

											if self.args.trace_memory_line_by_line:
														lowerCamelCase__						:						List[str]						      =stop_memory_tracing(lowerCamelCase	)
											else:
														lowerCamelCase__						:						Optional[int]						      =None

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







import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
lowerCAmelCase__             =		{
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]						)    ->					Any:




	'''simple docstring'''




	_UpperCamelCase :		str								=    search.split()
	_UpperCamelCase :		str								=    dict((g.split('='						) for g in groups)						)
	_UpperCamelCase :		List[Any]								=    list(entries.keys()						)
	_UpperCamelCase :		Dict								=    [[F'''--{k} {v}''' for v in vs.split(':'						)] for k, vs in entries.items()]
	_UpperCamelCase :		Dict								=    [list(UpperCAmelCase_						) for x in itertools.product(*UpperCAmelCase_						)]
	return matrix, entry_names






def        lowerCamelCase_  (				)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    sys.argv[0]

	_UpperCamelCase :		Tuple								=    argparse.ArgumentParser(
	    usage=(
	        '\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'
	        ' refer to `run_eval.py -h` for the complete list.'
	    )						)
	parser.add_argument(
	    '--search'				,							type=UpperCAmelCase_				,							required=UpperCAmelCase_				,							help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'				,							)
	parser.add_argument(
	    '--bs'				,							type=UpperCAmelCase_				,							default=8				,							required=UpperCAmelCase_				,							help='initial batch size (may get reduced if it\'s too big)'						)
	parser.add_argument('--task'				,							type=UpperCAmelCase_				,							help='used for task_specific_params + metrics'						)
	parser.add_argument(
	    '--info'				,							nargs='?'				,							type=UpperCAmelCase_				,							const=datetime_now()				,							help=(
	        'add custom notes to be printed before the results table. If no value is passed, the current datetime'
	        ' string will be used.'
	    )				,							)
	_UpperCamelCase						,		_UpperCamelCase :		str								=    parser.parse_known_args()
	# we share some of the args
	args_main.extend(['--task', args.task]						)
	_UpperCamelCase :		str								=    [prog] + args_main

	# to support variations like translation_en_to_de"
	_UpperCamelCase :		Tuple								=    'translation' if 'translation' in args.task else 'summarization'

	_UpperCamelCase						,		_UpperCamelCase :		Optional[int]								=    parse_search_arg(args.search						)
	_UpperCamelCase :		List[Any]								=    task_score_names[task]  # score cols first
	_UpperCamelCase :		Optional[int]								=    {col: len(str(UpperCAmelCase_						)						) for col in col_names}
	_UpperCamelCase :		str								=    []
	for r in matrix:
		_UpperCamelCase :		Union[str, Any]								=    dict((x.replace('--'				,							''						).split() for x in r)						)
		_UpperCamelCase :		List[str]								=    ' '.join(UpperCAmelCase_						).split()
		args_exp.extend(['--bs', str(args.bs						)]						)  # in case we need to reduce its size due to CUDA OOM
		_UpperCamelCase :		Union[str, Any]								=    args_normal + args_exp

		# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

		_UpperCamelCase :		Any								=    run_generate(verbose=UpperCAmelCase_						)
		# make sure scores are first in the table
		_UpperCamelCase :		Optional[int]								=    OrderedDict()
		for score in task_score_names[task]:
			_UpperCamelCase :		Tuple								=    scores[score]
		result.update(UpperCAmelCase_						)
		results.append(UpperCAmelCase_						)

		# find widest entries
		for k, v in result.items():
			_UpperCamelCase :		Tuple								=    len(str(UpperCAmelCase_						)						)
			if l > col_widths[k]:
				_UpperCamelCase :		str								=    l

	_UpperCamelCase :		Optional[int]								=    sorted(UpperCAmelCase_				,							key=operator.itemgetter(*task_score_names[task]						)				,							reverse=UpperCAmelCase_						)
	print(' | '.join([F'''{col:{col_widths[col]}}''' for col in col_names]						)						)
	print(' | '.join([F'''{"-"*col_widths[col]}''' for col in col_names]						)						)
	for row in results_sorted:
		print(' | '.join([F'''{row[col]:{col_widths[col]}}''' for col in col_names]						)						)

	_UpperCamelCase :		Optional[int]								=    results_sorted[0]
	for score in task_score_names[task]:
		del best[score]
	_UpperCamelCase :		str								=    [F'''--{k} {v}''' for k, v in best.items()]
	_UpperCamelCase :		Optional[Any]								=    ['--bs', str(args.bs						)]
	if args.info:
		print(F'''\nInfo: {args.info}'''						)
	print('\nBest score args:'						)
	print(' '.join(args_main + best_args + dyn_args						)						)

	return results_sorted


if __name__ == "__main__":
							# Usage:
							# [normal-run_eval_search.py cmd plus] \
							# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
							#
							# Example:
							# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
							# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
							# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
							# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
							run_search()




