


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							:  Tuple            =						getLogger(__name__)


lowerCAmelCase							:  Optional[Any]            =						"""cuda""" if torch.cuda.is_available() else """cpu"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = 8    ,     _UpperCAmelCase = DEFAULT_DEVICE    ,     _UpperCAmelCase=False    ,     _UpperCAmelCase="summarization"    ,     _UpperCAmelCase=None    ,     **_UpperCAmelCase    ,     ):
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Path(_UpperCAmelCase   ).open("w"    ,     encoding="utf-8"   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	str(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase   ).to(_UpperCAmelCase   )
						if fpaa:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model.half()

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

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







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







def       A_				(   _UpperCAmelCase=True   ):
						SCREAMING_SNAKE_CASE_:			int  		=	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
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	parser.parse_known_args()
						SCREAMING_SNAKE_CASE_:			Any  		=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase   )
						if parsed_args and verbose:
												print(f"parsed the following generate kwargs: {parsed_args}"   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	[" " + 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_:			List[Any]  		=	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."   )

						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[Any]  		=	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
						SCREAMING_SNAKE_CASE_:			Dict  		=	calculate_bleu if "translation" in args.task else calculate_rouge
						SCREAMING_SNAKE_CASE_:			int  		=	[x.rstrip() for x in open(args.save_path   ).readlines()]
						SCREAMING_SNAKE_CASE_:			Any  		=	[x.rstrip() for x in open(args.reference_path   ).readlines()][: len(_UpperCAmelCase   )]
						SCREAMING_SNAKE_CASE_:			dict  		=	score_fn(_UpperCAmelCase    ,     _UpperCAmelCase   )
						scores.update(_UpperCAmelCase   )

						if args.dump_args:
												scores.update(_UpperCAmelCase   )
						if args.info:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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)









from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase							:  List[str]            =						logging.get_logger(__name__)


lowerCAmelCase							:  Tuple            =						{
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
lowerCAmelCase							:  Any            =						1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowerCAmelCase							:  Dict            =						0

lowerCAmelCase							:  Dict            =						0xe000
lowerCAmelCase							:  str            =						0xe001
lowerCAmelCase							:  str            =						0xe002
lowerCAmelCase							:  Optional[int]            =						0xe003
lowerCAmelCase							:  List[Any]            =						0xe004

# Maps special codepoints to human-readable names.
lowerCAmelCase							:  Dict[int, str]            =						{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
lowerCAmelCase							:  Dict[str, int]            =						{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					str=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					List[Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Optional[int]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=False       ,						lowerCAmelCase__  :					int=2048       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else bos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else eos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else sep_token
									SCREAMING_SNAKE_CASE_:			Any  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else cls_token
									SCREAMING_SNAKE_CASE_:			str  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE_:			Dict  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else mask_token

									super().__init__(
									    bos_token=lowerCAmelCase__       ,						eos_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						add_prefix_space=lowerCAmelCase__       ,						model_max_length=lowerCAmelCase__       ,						**lowerCAmelCase__       ,						)

									# Creates a mapping for looking up the IDs of special symbols.
									SCREAMING_SNAKE_CASE_:			Dict[str, int]  		=	{}
									for codepoint, name in SPECIAL_CODEPOINTS.items():
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	codepoint

									# Creates a mapping for looking up the string forms of special symbol IDs.
									SCREAMING_SNAKE_CASE_:			Dict[int, str]  		=	{
									    codepoint: name for name, codepoint in self._special_codepoints.items()
									}

									SCREAMING_SNAKE_CASE_:			List[str]  		=	UNICODE_VOCAB_SIZE
									SCREAMING_SNAKE_CASE_:			Tuple  		=	len(self._special_codepoints)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self._unicode_vocab_size



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					str):
									return list(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					str):
									try:
															return ord(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid token: '{token}'")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					int):
									try:
															if index in SPECIAL_CODEPOINTS:
																					return SPECIAL_CODEPOINTS[index]
															return chr(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid id: {index}")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					Dict):
									return "".join(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			int  		=	cls + token_ids_a + sep
									if token_ids_a is not None:
															result += token_ids_a + sep
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None       ,						lowerCAmelCase__  :					bool = False):
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase__       ,						token_ids_a=lowerCAmelCase__       ,						already_has_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	[1] + ([0] * len(lowerCAmelCase__)) + [1]
									if token_ids_a is not None:
															result += ([0] * len(lowerCAmelCase__)) + [1]
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Dict  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			int  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	len(cls + token_ids_a + sep) * [0]
									if token_ids_a is not None:
															result += len(token_ids_a + sep) * [1]
									return result




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									return ()






