




import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm
						import torch_xla.debug.metrics as met







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,snake_case__=None      ,snake_case__=None      ,**snake_case__ ):
							super().__init__(*snake_case__      ,**snake_case__ )
							SCREAMING_SNAKE_CASE_   : int					  =      eval_examples
							SCREAMING_SNAKE_CASE_   : Dict					  =      post_process_function






				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__ = "eval" ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      self.eval_dataset if eval_dataset is None else eval_dataset
							SCREAMING_SNAKE_CASE_   : str					  =      self.get_eval_dataloader(snake_case__ )
							SCREAMING_SNAKE_CASE_   : int					  =      self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.compute_metrics
							SCREAMING_SNAKE_CASE_   : Dict					  =      None
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							SCREAMING_SNAKE_CASE_   : List[str]					  =      time.time()
							try:
										SCREAMING_SNAKE_CASE_   : Dict					  =      eval_loop(
										    snake_case__      ,description='Evaluation'      ,prediction_loss_only=True if compute_metrics is None else None      ,ignore_keys=snake_case__      ,metric_key_prefix=snake_case__      ,)
							finally:
										SCREAMING_SNAKE_CASE_   : Any					  =      compute_metrics
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.args.eval_batch_size * self.args.world_size
							if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
										start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
							output.metrics.update(
							    speed_metrics(
							        snake_case__      ,snake_case__      ,num_samples=output.num_samples      ,num_steps=math.ceil(output.num_samples / total_batch_size )      ,) )
							if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
										# Only the main node write the results by default
										SCREAMING_SNAKE_CASE_   : int					  =      self.post_process_function(snake_case__      ,snake_case__      ,output.predictions )
										SCREAMING_SNAKE_CASE_   : Tuple					  =      self.compute_metrics(snake_case__ )

										# Prefix all keys with metric_key_prefix + '_'
										for key in list(metrics.keys() ):
													if not key.startswith(F'{metric_key_prefix}_' ):
																SCREAMING_SNAKE_CASE_   : List[Any]					  =      metrics.pop(snake_case__ )
										metrics.update(output.metrics )
							else:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      output.metrics

							if self.args.should_log:
										# Only the main node log the results by default
										self.log(snake_case__ )

							if self.args.tpu_metrics_debug or self.args.debug:
										# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
										xm.master_print(met.metrics_report() )

							SCREAMING_SNAKE_CASE_   : Dict					  =      self.callback_handler.on_evaluate(self.args      ,self.state      ,self.control      ,snake_case__ )
							return metrics






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__=None      ,snake_case__ = "test" ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.get_test_dataloader(snake_case__ )

							# Temporarily disable metric computation, we will do it in the loop here.
							SCREAMING_SNAKE_CASE_   : int					  =      self.compute_metrics
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      None
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							SCREAMING_SNAKE_CASE_   : Tuple					  =      time.time()
							try:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      eval_loop(
										    snake_case__      ,description='Prediction'      ,prediction_loss_only=True if compute_metrics is None else None      ,ignore_keys=snake_case__      ,metric_key_prefix=snake_case__      ,)
							finally:
										SCREAMING_SNAKE_CASE_   : Dict					  =      compute_metrics
							SCREAMING_SNAKE_CASE_   : int					  =      self.args.eval_batch_size * self.args.world_size
							if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
										start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
							output.metrics.update(
							    speed_metrics(
							        snake_case__      ,snake_case__      ,num_samples=output.num_samples      ,num_steps=math.ceil(output.num_samples / total_batch_size )      ,) )

							if self.post_process_function is None or self.compute_metrics is None:
										return output

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.post_process_function(snake_case__      ,snake_case__      ,output.predictions      ,'predict' )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.compute_metrics(snake_case__ )

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys() ):
										if not key.startswith(F'{metric_key_prefix}_' ):
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      metrics.pop(snake_case__ )
							metrics.update(output.metrics )
							return PredictionOutput(predictions=predictions.predictions      ,label_ids=predictions.label_ids      ,metrics=snake_case__ )









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


UpperCamelCase__ :   Tuple						=							getLogger(__name__)


UpperCamelCase__ :   Dict						=							'''cuda''' if torch.cuda.is_available() else '''cpu'''





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     int = 8  ,							lowerCamelCase_ :     str = DEFAULT_DEVICE  ,							lowerCamelCase_ :     Union[str, Any]=False  ,							lowerCamelCase_ :     Any="summarization"  ,							lowerCamelCase_ :     Tuple=None  ,							**lowerCamelCase_ :     str  ,							)       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      Path(lowerCamelCase_  ).open('w'  ,							encoding='utf-8'  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      str(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_  ).to(lowerCamelCase_  )
			if fpaa:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model.half()

			SCREAMING_SNAKE_CASE_   : str					  =      AutoTokenizer.from_pretrained(lowerCamelCase_  )
			logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'  )  # if this is wrong, check config.model_type.

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





def        __UpperCAmelCase						(							)       ->  int:







			"""simple docstring"""





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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict=True  )       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      argparse.ArgumentParser()
			parser.add_argument('model_name'  ,							type=lowerCamelCase_  ,							help='like facebook/bart-large-cnn,t5-base, etc.'  )
			parser.add_argument('input_path'  ,							type=lowerCamelCase_  ,							help='like cnn_dm/test.source'  )
			parser.add_argument('save_path'  ,							type=lowerCamelCase_  ,							help='where to save summaries'  )
			parser.add_argument('--reference_path'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							help='like cnn_dm/test.target'  )
			parser.add_argument('--score_path'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							default='metrics.json'  ,							help='where to save metrics'  )
			parser.add_argument('--device'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							default=lowerCamelCase_  ,							help='cuda, cuda:1, cpu etc.'  )
			parser.add_argument(
			    '--prefix'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							default=lowerCamelCase_  ,							help='will be added to the begininng of src examples'  )
			parser.add_argument('--task'  ,							type=lowerCamelCase_  ,							default='summarization'  ,							help='used for task_specific_params + metrics'  )
			parser.add_argument('--bs'  ,							type=lowerCamelCase_  ,							default=8  ,							required=lowerCamelCase_  ,							help='batch size'  )
			parser.add_argument(
			    '--n_obs'  ,							type=lowerCamelCase_  ,							default=-1  ,							required=lowerCamelCase_  ,							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=lowerCamelCase_  ,							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
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      parser.parse_known_args()
			SCREAMING_SNAKE_CASE_   : str					  =      parse_numeric_n_bool_cl_kwargs(lowerCamelCase_  )
			if parsed_args and verbose:
						print(F'parsed the following generate kwargs: {parsed_args}'  )
			SCREAMING_SNAKE_CASE_   : int					  =      [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path  ).readlines()]
			if args.n_obs > 0:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      examples[: args.n_obs]
			Path(args.save_path  ).parent.mkdir(exist_ok=lowerCamelCase_  )

			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.'  )

			if args.device == "cpu" and args.fpaa:
						# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
						raise ValueError('Can\'t mix --fp16 and --device cpu'  )

			SCREAMING_SNAKE_CASE_   : List[str]					  =      generate_summaries_or_translations(
			    lowerCamelCase_  ,							args.save_path  ,							args.model_name  ,							batch_size=args.bs  ,							device=args.device  ,							fpaa=args.fpaa  ,							task=args.task  ,							prefix=args.prefix  ,							**lowerCamelCase_  ,							)

			if args.reference_path is None:
						return {}

			# Compute scores
			SCREAMING_SNAKE_CASE_   : str					  =      calculate_bleu if 'translation' in args.task else calculate_rouge
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [x.rstrip() for x in open(args.save_path  ).readlines()]
			SCREAMING_SNAKE_CASE_   : int					  =      [x.rstrip() for x in open(args.reference_path  ).readlines()][: len(lowerCamelCase_  )]
			SCREAMING_SNAKE_CASE_   : dict					  =      score_fn(lowerCamelCase_  ,							lowerCamelCase_  )
			scores.update(lowerCamelCase_  )

			if args.dump_args:
						scores.update(lowerCamelCase_  )
			if args.info:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      args.info

			if verbose:
						print(lowerCamelCase_  )

			if args.score_path is not None:
						json.dump(lowerCamelCase_  ,							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)



