


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


_A							     =       getLogger(__name__)


_A							     =       'cuda' if torch.cuda.is_available() else 'cpu'

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[str]     ,					UpperCamelCase		:      str     ,					UpperCamelCase		:      str     ,					UpperCamelCase		:      int = 8     ,					UpperCamelCase		:      str = DEFAULT_DEVICE     ,					UpperCamelCase		:      Tuple=False     ,					UpperCamelCase		:      Optional[Any]="summarization"     ,					UpperCamelCase		:      Any=None     ,					**UpperCamelCase		:      str     ,					)     ->	Dict:



	"""simple docstring"""






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

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

def 			__SCREAMING_SNAKE_CASE		(		)     ->	Tuple:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Union[str, Any]=True	)     ->	List[str]:



	"""simple docstring"""






	a_     			=   argparse.ArgumentParser()
	parser.add_argument("""model_name"""     ,					type=UpperCamelCase     ,					help="""like facebook/bart-large-cnn,t5-base, etc."""	)
	parser.add_argument("""input_path"""     ,					type=UpperCamelCase     ,					help="""like cnn_dm/test.source"""	)
	parser.add_argument("""save_path"""     ,					type=UpperCamelCase     ,					help="""where to save summaries"""	)
	parser.add_argument("""--reference_path"""     ,					type=UpperCamelCase     ,					required=UpperCamelCase     ,					help="""like cnn_dm/test.target"""	)
	parser.add_argument("""--score_path"""     ,					type=UpperCamelCase     ,					required=UpperCamelCase     ,					default="""metrics.json"""     ,					help="""where to save metrics"""	)
	parser.add_argument("""--device"""     ,					type=UpperCamelCase     ,					required=UpperCamelCase     ,					default=UpperCamelCase     ,					help="""cuda, cuda:1, cpu etc."""	)
	parser.add_argument(
	    """--prefix"""     ,					type=UpperCamelCase     ,					required=UpperCamelCase     ,					default=UpperCamelCase     ,					help="""will be added to the begininng of src examples"""	)
	parser.add_argument("""--task"""     ,					type=UpperCamelCase     ,					default="""summarization"""     ,					help="""used for task_specific_params + metrics"""	)
	parser.add_argument("""--bs"""     ,					type=UpperCamelCase     ,					default=8     ,					required=UpperCamelCase     ,					help="""batch size"""	)
	parser.add_argument(
	    """--n_obs"""     ,					type=UpperCamelCase     ,					default=-1     ,					required=UpperCamelCase     ,					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=UpperCamelCase     ,					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(UpperCamelCase	)
	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=UpperCamelCase	)
	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(
	    UpperCamelCase     ,					args.save_path     ,					args.model_name     ,					batch_size=args.bs     ,					device=args.device     ,					fpaa=args.fpaa     ,					task=args.task     ,					prefix=args.prefix     ,					**UpperCamelCase     ,					)

	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(UpperCamelCase	)]
	a_     			=   score_fn(UpperCamelCase     ,					UpperCamelCase	)
	scores.update(UpperCamelCase	)

	if args.dump_args:
		scores.update(UpperCamelCase	)
	if args.info:
		a_     			=   args.info

	if verbose:
		print(UpperCamelCase	)

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



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_A							     =       set()
_A							     =       get_logger(__name__)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[str] = None	)     ->	Optional[int]:



	"""simple docstring"""






	def decorator(UpperCamelCase		:      Callable	):
		global _emitted_deprecation_warnings

		if inspect.isclass(UpperCamelCase	):
			a_     			=   deprecated_class_or_function.__init__
			a_     			=   deprecated_class_or_function.__name__
		else:
			a_     			=   deprecated_class_or_function
			a_     			=   deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			a_     			=   name if name != """__init__""" else deprecated_function.__qualname__.split("""."""	)[-2]

		a_     			=   (
		    F"""{name} is deprecated and will be removed in the next major version of datasets.""" + F""" {help_message}"""
		    if help_message
		    else """"""
		)

		@wraps(UpperCamelCase	)
		def wrapper(*UpperCamelCase		:      List[str]     ,					**UpperCamelCase		:      Union[str, Any]	):
			a_     			=   hash(UpperCamelCase	)
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(UpperCamelCase     ,					category=UpperCamelCase     ,					stacklevel=2	)
				_emitted_deprecation_warnings.add(UpperCamelCase	)
			return deprecated_function(*UpperCamelCase     ,					**UpperCamelCase	)

		a_     			=   """deprecated"""

		if inspect.isclass(UpperCamelCase	):
			a_     			=   wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class 			lowerCamelCase_					(    enum.EnumMeta    ):





							def __getattribute__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getattribute__(_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj





							def __getitem__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getitem__(_SCREAMING_SNAKE_CASE    )
								if member._on_access:
									member._on_access()
								return member





							def __call__(							cls			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       *, _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=1    ):
								a_     			=   super().__call__(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       module=_SCREAMING_SNAKE_CASE			,       qualname=_SCREAMING_SNAKE_CASE			,       type=_SCREAMING_SNAKE_CASE			,       start=_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj







class 			lowerCamelCase_					(    enum.Enum   ,		metaclass=_SCREAMING_SNAKE_CASE    ):





							def __new__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   object.__new__(cls    )
								a_     			=   value
								a_     			=   member.deprecate
								return member





							@property
							def   __magic_name__      (							self    ):
								return ""





							def   __magic_name__      (							self    ):
								a_     			=   f""" {self.help_message}""" if self.help_message else """"""
								warnings.warn(
								    f"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
								    + help_message			,       _SCREAMING_SNAKE_CASE			,       stacklevel=3			,       )