
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_						=						getLogger(__name__)


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





def 				_lowerCamelCase      (       lowerCamelCase_:	List[str]					,						lowerCamelCase_:	str					,						lowerCamelCase_:	str					,						lowerCamelCase_:	int = 8					,						lowerCamelCase_:	str = DEFAULT_DEVICE					,						lowerCamelCase_:	Optional[int]=False					,						lowerCamelCase_:	Optional[int]="summarization"					,						lowerCamelCase_:	Dict=None					,						**lowerCamelCase_:	int					,						):



				'''simple docstring'''







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

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

				A  :	Union[str, Any]				=					time.time()
				# update config with task specific params
				use_task_specific_params(_SCREAMING_SNAKE_CASE					,						_SCREAMING_SNAKE_CASE		)
				if prefix is None:
								A  :	Any				=					prefix or getattr(model.config					,						'''prefix'''					,						''''''		) or """"""
				for examples_chunk in tqdm(list(chunks(_SCREAMING_SNAKE_CASE					,						_SCREAMING_SNAKE_CASE		)		)		):
								A  :	List[str]				=					[prefix + text for text in examples_chunk]
								A  :	Any				=					tokenizer(_SCREAMING_SNAKE_CASE					,						return_tensors='''pt'''					,						truncation=_SCREAMING_SNAKE_CASE					,						padding='''longest'''		).to(_SCREAMING_SNAKE_CASE		)
								A  :	str				=					model.generate(
								    input_ids=batch.input_ids					,						attention_mask=batch.attention_mask					,						**_SCREAMING_SNAKE_CASE					,						)
								A  :	List[Any]				=					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  :	Optional[int]				=					int(time.time() - start_time		)  # seconds
				A  :	int				=					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      (       lowerCamelCase_:	Union[str, Any]=True		):



				'''simple docstring'''







				A  :	int				=					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  :	int				=					parser.parse_known_args()
				A  :	List[str]				=					parse_numeric_n_bool_cl_kwargs(_SCREAMING_SNAKE_CASE		)
				if parsed_args and verbose:
								print(f"""parsed the following generate kwargs: {parsed_args}"""		)
				A  :	List[str]				=					[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path		).readlines()]
				if args.n_obs > 0:
								A  :	Optional[int]				=					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."""		)

				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'''		)

				A  :	Any				=					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  :	Optional[int]				=					calculate_bleu if """translation""" in args.task else calculate_rouge
				A  :	Optional[Any]				=					[x.rstrip() for x in open(args.save_path		).readlines()]
				A  :	Dict				=					[x.rstrip() for x in open(args.reference_path		).readlines()][: len(_SCREAMING_SNAKE_CASE		)]
				A  :	dict				=					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  :	Optional[Any]				=					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)
import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






