



import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase :				Tuple							=16
UpperCAmelCase :				str							=32



def  _lowerCAmelCase     (_lowerCAmelCase):
					return int(x / 2**20)


class    _lowercase :
				'''simple docstring'''







				def __enter__(			self     ):

									'''simple docstring'''


									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									UpperCamelCase_			= torch.cuda.memory_allocated()
									UpperCamelCase_			= psutil.Process()

									UpperCamelCase_			= self.cpu_mem_used()
									UpperCamelCase_			= True
									UpperCamelCase_			= threading.Thread(target=self.peak_monitor_func     )
									UpperCamelCase_			= True
									peak_monitor_thread.start()
									return self

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self.process.memory_info().rss

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= -1

									while True:
														UpperCamelCase_			= max(self.cpu_mem_used()  ,			self.cpu_peak     )

														# can't sleep or will not catch the peak right (this comment is here on purpose)
														# time.sleep(0.001) # 1msec

														if not self.peak_monitoring:
																			break

				def __exit__(			self  ,			*snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= False

									gc.collect()
									torch.cuda.empty_cache()
									UpperCamelCase_			= torch.cuda.memory_allocated()
									UpperCamelCase_			= torch.cuda.max_memory_allocated()
									UpperCamelCase_			= bamb(self.end - self.begin     )
									UpperCamelCase_			= bamb(self.peak - self.begin     )

									UpperCamelCase_			= self.cpu_mem_used()
									UpperCamelCase_			= bamb(self.cpu_end - self.cpu_begin     )
									UpperCamelCase_			= bamb(self.cpu_peak - self.cpu_begin     )
									# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	UpperCAmelCase :				Union[str, Any]							=mocked_dataloaders  # noqa: F811



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS" ,						_lowerCAmelCase) == "1":
										UpperCamelCase_			= 2

					# New Code #
					# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
					UpperCamelCase_			= FullyShardedDataParallelPlugin(
					    state_dict_config=FullStateDictConfig(offload_to_cpu=_lowerCAmelCase ,						ranka_only=_lowerCAmelCase) ,						optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_lowerCAmelCase ,						ranka_only=_lowerCAmelCase) ,						)

					# Initialize accelerator
					if args.with_tracking:
										UpperCamelCase_			= Accelerator(
										    cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						log_with="wandb" ,						project_dir=args.logging_dir ,						fsdp_plugin=_lowerCAmelCase ,						)
					else:
										UpperCamelCase_			= Accelerator(fsdp_plugin=_lowerCAmelCase)
					accelerator.print(accelerator.distributed_type)

					if hasattr(args.checkpointing_steps ,						"isdigit"):
										if args.checkpointing_steps == "epoch":
															UpperCamelCase_			= args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															UpperCamelCase_			= int(args.checkpointing_steps)
										else:
															raise ValueError(
															    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""")
					else:
										UpperCamelCase_			= None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCamelCase_			= config["lr"]
					UpperCamelCase_			= int(config["num_epochs"])
					UpperCamelCase_			= int(config["seed"])
					UpperCamelCase_			= int(config["batch_size"])

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										UpperCamelCase_			= vars(_lowerCAmelCase)
										accelerator.init_trackers("fsdp_glue_no_trainer" ,						_lowerCAmelCase)

					UpperCamelCase_			= AutoTokenizer.from_pretrained(args.model_name_or_path)
					UpperCamelCase_			= load_dataset("glue" ,						"mrpc")
					UpperCamelCase_			= evaluate.load("glue" ,						"mrpc")

					def tokenize_function(_lowerCAmelCase):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase_			= tokenizer(examples["sentence1"] ,						examples["sentence2"] ,						truncation=_lowerCAmelCase ,						max_length=_lowerCAmelCase)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										UpperCamelCase_			= datasets.map(
										    _lowerCAmelCase ,						batched=_lowerCAmelCase ,						remove_columns=["idx", "sentence1", "sentence2"] ,						)

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					UpperCamelCase_			= tokenized_datasets.rename_column("label" ,						"labels")

					# If the batch size is too big we use gradient accumulation
					UpperCamelCase_			= 1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										UpperCamelCase_			= batch_size // MAX_GPU_BATCH_SIZE
										UpperCamelCase_			= MAX_GPU_BATCH_SIZE

					def collate_fn(_lowerCAmelCase):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCamelCase_			= 1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															UpperCamelCase_			= 16
										elif accelerator.mixed_precision != "no":
															UpperCamelCase_			= 8
										else:
															UpperCamelCase_			= None

										return tokenizer.pad(
										    _lowerCAmelCase ,						padding="longest" ,						max_length=_lowerCAmelCase ,						pad_to_multiple_of=_lowerCAmelCase ,						return_tensors="pt" ,						)

					# Instantiate dataloaders.
					UpperCamelCase_			= DataLoader(
					    tokenized_datasets["train"] ,						shuffle=_lowerCAmelCase ,						collate_fn=_lowerCAmelCase ,						batch_size=_lowerCAmelCase)
					UpperCamelCase_			= DataLoader(
					    tokenized_datasets["validation"] ,						shuffle=_lowerCAmelCase ,						collate_fn=_lowerCAmelCase ,						batch_size=_lowerCAmelCase)

					set_seed(_lowerCAmelCase)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase_			= AutoModelForSequenceClassification.from_pretrained(
					    args.model_name_or_path ,						return_dict=_lowerCAmelCase ,						low_cpu_mem_usage=_lowerCAmelCase)

					# New Code #
					# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
					UpperCamelCase_			= accelerator.prepare(_lowerCAmelCase)
					accelerator.print(_lowerCAmelCase)

					# Instantiate optimizer
					# New Code #
					# For FSDP feature, at present it doesn't support multiple parameter groups,
					# so we need to create a single parameter group for the whole model
					UpperCamelCase_			= torch.optim.AdamW(params=model.parameters() ,						lr=_lowerCAmelCase ,						weight_decay=2e-4)

					# Instantiate scheduler
					UpperCamelCase_			= get_linear_schedule_with_warmup(
					    optimizer=_lowerCAmelCase ,						num_warmup_steps=10 ,						num_training_steps=(len(_lowerCAmelCase) * num_epochs) // gradient_accumulation_steps ,						)

					# New Code #
					# For FSDP feature, prepare everything except the model as we have already prepared the model
					# before creating the optimizer
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= accelerator.prepare(
					    _lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)

					UpperCamelCase_			= 0

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}""")
															accelerator.load_state(args.resume_from_checkpoint)
															UpperCamelCase_			= os.path.basename(args.resume_from_checkpoint)
										else:
															# Get the most recent checkpoint
															UpperCamelCase_			= [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
															dirs.sort(key=os.path.getctime)
															UpperCamelCase_			= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										UpperCamelCase_			= os.path.splitext(_lowerCAmelCase)[0]

										if "epoch" in training_difference:
															num_epochs -= int(training_difference.replace("epoch_" ,						""))
															UpperCamelCase_			= None
										else:
															UpperCamelCase_			= int(training_difference.replace("step_" ,						""))
															num_epochs -= resume_step // len(_lowerCAmelCase)
															# If resuming by step, we also need to know exactly how far into the DataLoader we went
															UpperCamelCase_			= (num_epochs * len(_lowerCAmelCase)) - resume_step

    # Now we train the model
					for epoch in range(_lowerCAmelCase):
										# New Code #
										# context manager to track the peak memory usage during the training epoch
										with TorchTracemalloc() as tracemalloc:
															model.train()
															if args.with_tracking:
																				UpperCamelCase_			= 0
															for step, batch in enumerate(_lowerCAmelCase):
																				# We need to skip steps until we reach the resumed step
																				if args.resume_from_checkpoint and epoch == 0:
																									if resume_step is not None and step < resume_step:
																														pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device)
																				UpperCamelCase_			= model(**_lowerCAmelCase)
																				UpperCamelCase_			= outputs.loss
																				UpperCamelCase_			= loss / gradient_accumulation_steps
																				# We keep track of the loss at each epoch
																				if args.with_tracking:
																									total_loss += loss.detach().float()
																				accelerator.backward(_lowerCAmelCase)
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# accelerator.print(lr_scheduler.get_lr())

																				overall_step += 1

																				if isinstance(_lowerCAmelCase ,						_lowerCAmelCase):
																									UpperCamelCase_			= f"""step_{overall_step}"""
																									if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																			UpperCamelCase_			= os.path.join(args.output_dir ,						_lowerCAmelCase)
																														accelerator.save_state(_lowerCAmelCase)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
										accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin)))
										accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
										accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
										accelerator.print(
										    "Total Peak Memory consumed during the train (max): {}".format(
										        tracemalloc.peaked + bamb(tracemalloc.begin)))
										# Logging the peak memory usage of the GPU to the tracker
										if args.with_tracking:
															accelerator.log(
															    {
															        "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin),
															    } ,						step=_lowerCAmelCase ,						)

										# New Code #
										# context manager to track the peak memory usage during the evaluation
										with TorchTracemalloc() as tracemalloc:
															model.eval()
															for step, batch in enumerate(_lowerCAmelCase):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device)
																				with torch.no_grad():
																									UpperCamelCase_			= model(**_lowerCAmelCase)
																				UpperCamelCase_			= outputs.logits.argmax(dim=-1)
																				UpperCamelCase_	,     UpperCamelCase_			= accelerator.gather_for_metrics((predictions, batch["labels"]))
																				metric.add_batch(
																				    predictions=_lowerCAmelCase ,						references=_lowerCAmelCase ,						)

															UpperCamelCase_			= metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(f"""epoch {epoch}:""" ,						_lowerCAmelCase)
															if args.with_tracking:
																				accelerator.log(
																				    {
																				        "accuracy": eval_metric["accuracy"],
																				        "f1": eval_metric["f1"],
																				        "train_loss": total_loss.item() / len(_lowerCAmelCase),
																				    } ,						step=_lowerCAmelCase ,						)

															if checkpointing_steps == "epoch":
																				UpperCamelCase_			= f"""epoch_{epoch}"""
																				if args.output_dir is not None:
																									UpperCamelCase_			= os.path.join(args.output_dir ,						_lowerCAmelCase)
																				accelerator.save_state(_lowerCAmelCase)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
										accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin)))
										accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used))
										accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked))
										accelerator.print(
										    "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin)))
										# Logging the peak memory usage of the GPU to the tracker
										if args.with_tracking:
															accelerator.log(
															    {
															        "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin),
															    } ,						step=_lowerCAmelCase ,						)

					if args.with_tracking:
										accelerator.end_training()



def  _lowerCAmelCase     ():
					UpperCamelCase_			= argparse.ArgumentParser(description="Simple example of training script.")
					parser.add_argument(
					    "--mixed_precision" ,						type=_lowerCAmelCase ,						default=_lowerCAmelCase ,						choices=["no", "fp16", "bf16", "fp8"] ,						help="Whether to use mixed precision. Choose"
					    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
					    "and an Nvidia Ampere GPU." ,						)
					parser.add_argument("--cpu" ,						action="store_true" ,						help="If passed, will train on the CPU.")
					parser.add_argument(
					    "--checkpointing_steps" ,						type=_lowerCAmelCase ,						default=_lowerCAmelCase ,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." ,						)
					parser.add_argument(
					    "--resume_from_checkpoint" ,						type=_lowerCAmelCase ,						default=_lowerCAmelCase ,						help="If the training should continue from a checkpoint folder." ,						)
					parser.add_argument(
					    "--with_tracking" ,						action="store_true" ,						help="Whether to load in all available experiment trackers from the environment and use them for logging." ,						)
					parser.add_argument(
					    "--output_dir" ,						type=_lowerCAmelCase ,						default="." ,						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,						)
					parser.add_argument(
					    "--logging_dir" ,						type=_lowerCAmelCase ,						default="logs" ,						help="Location on where to store experiment tracking logs`" ,						)
					parser.add_argument(
					    "--model_name_or_path" ,						type=_lowerCAmelCase ,						help="Path to pretrained model or model identifier from huggingface.co/models." ,						required=_lowerCAmelCase ,						)
					UpperCamelCase_			= parser.parse_args()
					UpperCamelCase_			= {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(_lowerCAmelCase ,						_lowerCAmelCase)


if __name__ == "__main__":
	main()






import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowercase_      :							int    							=					getLogger(__name__)


lowercase_      :							str    							=					'cuda' if torch.cuda.is_available() else 'cpu'


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_ = 8      ,       snake_case_ = DEFAULT_DEVICE      ,       snake_case_=False      ,       snake_case_="summarization"      ,       snake_case_=None      ,       **snake_case_      ,       ):

						'''simple docstring'''


						_UpperCAmelCase						=	Path(snake_case_		).open("w"      ,       encoding="utf-8"		)
						_UpperCAmelCase						=	str(snake_case_		)
						_UpperCAmelCase						=	AutoModelForSeqaSeqLM.from_pretrained(snake_case_		).to(snake_case_		)
						if fpaa:
												_UpperCAmelCase						=	model.half()

						_UpperCAmelCase						=	AutoTokenizer.from_pretrained(snake_case_		)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""		)  # if this is wrong, check config.model_type.

						_UpperCAmelCase						=	time.time()
						# update config with task specific params
						use_task_specific_params(snake_case_      ,       snake_case_		)
						if prefix is None:
												_UpperCAmelCase						=	prefix or getattr(model.config      ,       "prefix"      ,       ""		) or ""
						for examples_chunk in tqdm(list(chunks(snake_case_      ,       snake_case_		)		)		):
												_UpperCAmelCase						=	[prefix + text for text in examples_chunk]
												_UpperCAmelCase						=	tokenizer(snake_case_      ,       return_tensors="pt"      ,       truncation=snake_case_      ,       padding="longest"		).to(snake_case_		)
												_UpperCAmelCase						=	model.generate(
												    input_ids=batch.input_ids      ,       attention_mask=batch.attention_mask      ,       **snake_case_      ,       )
												_UpperCAmelCase						=	tokenizer.batch_decode(snake_case_      ,       skip_special_tokens=snake_case_      ,       clean_up_tokenization_spaces=snake_case_		)
												for hypothesis in dec:
																		fout.write(hypothesis + "\n"		)
																		fout.flush()
						fout.close()
						_UpperCAmelCase						=	int(time.time() - start_time		)  # seconds
						_UpperCAmelCase						=	len(snake_case_		)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs      ,       4		)}


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_=True		):

						'''simple docstring'''


						_UpperCAmelCase						=	argparse.ArgumentParser()
						parser.add_argument("model_name"      ,       type=snake_case_      ,       help="like facebook/bart-large-cnn,t5-base, etc."		)
						parser.add_argument("input_path"      ,       type=snake_case_      ,       help="like cnn_dm/test.source"		)
						parser.add_argument("save_path"      ,       type=snake_case_      ,       help="where to save summaries"		)
						parser.add_argument("--reference_path"      ,       type=snake_case_      ,       required=snake_case_      ,       help="like cnn_dm/test.target"		)
						parser.add_argument("--score_path"      ,       type=snake_case_      ,       required=snake_case_      ,       default="metrics.json"      ,       help="where to save metrics"		)
						parser.add_argument("--device"      ,       type=snake_case_      ,       required=snake_case_      ,       default=snake_case_      ,       help="cuda, cuda:1, cpu etc."		)
						parser.add_argument(
						    "--prefix"      ,       type=snake_case_      ,       required=snake_case_      ,       default=snake_case_      ,       help="will be added to the begininng of src examples"		)
						parser.add_argument("--task"      ,       type=snake_case_      ,       default="summarization"      ,       help="used for task_specific_params + metrics"		)
						parser.add_argument("--bs"      ,       type=snake_case_      ,       default=8      ,       required=snake_case_      ,       help="batch size"		)
						parser.add_argument(
						    "--n_obs"      ,       type=snake_case_      ,       default=-1      ,       required=snake_case_      ,       help="How many observations. Defaults to all."		)
						parser.add_argument("--fp16"      ,       action="store_true"		)
						parser.add_argument("--dump-args"      ,       action="store_true"      ,       help="print the custom hparams with the results"		)
						parser.add_argument(
						    "--info"      ,       nargs="?"      ,       type=snake_case_      ,       const=datetime_now()      ,       help=(
						        "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
						        " lang=en-ru. If no value is passed, the current datetime string will be used."
						    )      ,       )
						# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
						_UpperCAmelCase				,					_UpperCAmelCase						=	parser.parse_known_args()
						_UpperCAmelCase						=	parse_numeric_n_bool_cl_kwargs(snake_case_		)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""		)
						with open(args.input_path		) as f:
												_UpperCAmelCase						=	[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
						if args.n_obs > 0:
												_UpperCAmelCase						=	examples[: args.n_obs]
						Path(args.save_path		).parent.mkdir(exist_ok=snake_case_		)
						if args.reference_path is None and Path(args.score_path		).exists():
												warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""		)
						_UpperCAmelCase						=	generate_summaries_or_translations(
						    snake_case_      ,       args.save_path      ,       args.model_name      ,       batch_size=args.bs      ,       device=args.device      ,       fpaa=args.fpaa      ,       task=args.task      ,       prefix=args.prefix      ,       **snake_case_      ,       )

						if args.reference_path is None:
												return {}

						# Compute scores
						_UpperCAmelCase						=	calculate_bleu if "translation" in args.task else calculate_rouge
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.save_path		).readlines()]
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.reference_path		).readlines()][: len(snake_case_		)]
						_UpperCAmelCase						=	score_fn(snake_case_      ,       snake_case_		)
						scores.update(snake_case_		)

						if args.dump_args:
												scores.update(snake_case_		)
						if args.info:
												_UpperCAmelCase						=	args.info

						if verbose:
												print(snake_case_		)

						if args.score_path is not None:
												json.dump(snake_case_      ,       open(args.score_path      ,       "w"		)		)

						return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)




