



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


lowerCAmelCase__	:  List[str] 		=torch.cuda.is_available()
lowerCAmelCase__	:  List[str] 		=[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCAmelCase__	:  str 		=["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCAmelCase__	:  str 		={
    """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 			__lowercase     (				a__							,     a__     ):
							__SCREAMING_SNAKE_CASE								=      '\n'.join(lowerCAmelCase__     )
							Path(lowerCAmelCase__     ).open('w'     ).writelines(lowerCAmelCase__     )






def 			__lowercase     (				a__     ):
							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      UpperCAmelCase_						(     snake_case__ ):

						'''simple docstring'''




						@classmethod
						def  _A			(					cls	):







													'''simple docstring'''

													return cls






						@require_torch_multi_gpu
						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      {
													    'no_teacher': True,
													    'freeze_encoder': True,
													    'gpus': 2,
													    'overwrite_output_dir': True,
													    'sortish_sampler': True,
													}
													self._test_distiller_cli_fork(_A   ,							check_contents=_A	)






						def  _A			(					self   ,							_A   ,							_A=True	):







													'''simple docstring'''

													__SCREAMING_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(_A	)
													__SCREAMING_SNAKE_CASE								=      CHEAP_ARGS.copy()
													__SCREAMING_SNAKE_CASE								=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
													__SCREAMING_SNAKE_CASE								=      self.get_auto_remove_tmp_dir()
													args_d.update(data_dir=_A   ,							output_dir=_A   ,							**_A	)

													def convert(_A   ,							_A	):
																				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}"""

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

													__SCREAMING_SNAKE_CASE								=      os.listdir(_A	)
													__SCREAMING_SNAKE_CASE								=      {os.path.basename(_A	) for p in contents}
													__SCREAMING_SNAKE_CASE								=      [p for p in contents if p.endswith('ckpt'	)]
													assert len(_A	) > 0

													self.assertIn('test_generations.txt'   ,							_A	)
													self.assertIn('test_results.txt'   ,							_A	)

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

													__SCREAMING_SNAKE_CASE								=      load_json(_A	)
													# {'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(_A	)
													__SCREAMING_SNAKE_CASE								=      metrics['val'][-1]
													self.assertGreaterEqual(last_step_stats['val_avg_gen_time']   ,							0.0_1	)
													self.assertIsInstance(last_step_stats[f"""val_avg_{val_metric}"""]   ,							_A	)
													self.assertEqual(len(metrics['test']	)   ,							1	)
													__SCREAMING_SNAKE_CASE								=      int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1	)
													self.assertEqual(len(metrics['val']	)   ,							_A	)











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


lowerCAmelCase__	:  Union[str, Any] 		=getLogger(__name__)


lowerCAmelCase__	:  Optional[int] 		='''cuda''' if torch.cuda.is_available() else '''cpu'''






def 			__lowercase     (				a__							,     a__							,     a__							,     a__ = 8							,     a__ = DEFAULT_DEVICE							,     a__=False							,     a__="summarization"							,     a__=None							,     **a__							,     )       ->				Dict:
							__SCREAMING_SNAKE_CASE								=      Path(a__     ).open('w'							,     encoding='utf-8'     )
							__SCREAMING_SNAKE_CASE								=      str(a__     )
							__SCREAMING_SNAKE_CASE								=      AutoModelForSeqaSeqLM.from_pretrained(a__     ).to(a__     )
							if fpaa:
														__SCREAMING_SNAKE_CASE								=      model.half()

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

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






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






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

							if args.reference_path is None:
														return {}

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

							if args.dump_args:
														scores.update(a__     )
							if args.info:
														__SCREAMING_SNAKE_CASE								=      args.info

							if verbose:
														print(a__     )

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






