



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__     =	getLogger(__name__)


lowerCamelCase__     =	"""cuda""" if torch.cuda.is_available() else """cpu"""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[str]			,       _SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     int = 8			,       _SCREAMING_SNAKE_CASE    :     str = DEFAULT_DEVICE			,       _SCREAMING_SNAKE_CASE    :     List[str]=False			,       _SCREAMING_SNAKE_CASE    :     Any="summarization"			,       _SCREAMING_SNAKE_CASE    :     int=None			,       **_SCREAMING_SNAKE_CASE    :     int			,       ):
				"""simple docstring"""







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

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

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

def 					lowerCAmelCase__  (							):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Any=True      ):
				"""simple docstring"""







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

				if args.reference_path is None:
								return {}

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

				if args.dump_args:
								scores.update(_SCREAMING_SNAKE_CASE      )
				if args.info:
								__a					       =						args.info

				if verbose:
								print(_SCREAMING_SNAKE_CASE      )

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





# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase__     =	"""Update an existing config file with the latest defaults while maintaining the old configuration."""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







				__a					       =						args.config_file
				if config_file is None and Path(_SCREAMING_SNAKE_CASE      ).exists():
								__a					       =						default_config_file
				elif not Path(_SCREAMING_SNAKE_CASE      ).exists():
								raise ValueError(f"The passed config file located at {config_file} doesn't exist."      )
				__a					       =						load_config_from_file(_SCREAMING_SNAKE_CASE      )

				if config_file.endswith(""".json"""      ):
								config.to_json_file(_SCREAMING_SNAKE_CASE      )
				else:
								config.to_yaml_file(_SCREAMING_SNAKE_CASE      )
				return config_file

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     int      ):
				"""simple docstring"""







				__a					       =						parser.add_parser("""update"""			,       parents=_SCREAMING_SNAKE_CASE			,       help=_SCREAMING_SNAKE_CASE			,       formatter_class=_SCREAMING_SNAKE_CASE      )
				parser.add_argument(
				    """--config_file"""			,       default=_SCREAMING_SNAKE_CASE			,       help=(
				        """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
				        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
				        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
				        """with 'huggingface'."""
				    )			,       )

				parser.set_defaults(func=_SCREAMING_SNAKE_CASE      )
				return parser

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				__a					       =						update_config(_SCREAMING_SNAKE_CASE      )
				print(f"Sucessfully updated the configuration file at {config_file}."      )
