
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


snake_case_      :					List[Any]				      =     getLogger(__name__)


snake_case_      :					Any				      =     'cuda' if torch.cuda.is_available() else 'cpu'



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		int				,					snake_case_							:		Tuple				,					snake_case_							:		Any = 8				,					snake_case_							:		Dict = DEFAULT_DEVICE				,					snake_case_							:		Dict=False				,					snake_case_							:		Dict="summarization"				,					snake_case_							:		int=None				,					**snake_case_							:		int				,					):



	'''simple docstring'''



	UpperCAmelCase:					List[str] 						=     Path(__lowerCAmelCase				).open("w"				,					encoding="utf-8"				)
	UpperCAmelCase:					Union[str, Any] 						=     str(__lowerCAmelCase				)
	UpperCAmelCase:					Optional[Any] 						=     AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase				).to(__lowerCAmelCase				)
	if fpaa:
		UpperCAmelCase:					List[str] 						=     model.half()

	UpperCAmelCase:					List[str] 						=     AutoTokenizer.from_pretrained(__lowerCAmelCase				)
	logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'				)  # if this is wrong, check config.model_type.

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



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



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



def      __UpperCAmelCase    (  snake_case_							:		Dict=True				):



	'''simple docstring'''



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

	if args.reference_path is None:
		return {}

	# Compute scores
	UpperCAmelCase:					Dict 						=     calculate_bleu if "translation" in args.task else calculate_rouge
	UpperCAmelCase:					Tuple 						=     [x.rstrip() for x in open(args.save_path				).readlines()]
	UpperCAmelCase:					Any 						=     [x.rstrip() for x in open(args.reference_path				).readlines()][: len(__lowerCAmelCase				)]
	UpperCAmelCase:					Optional[int] 						=     score_fn(__lowerCAmelCase				,					__lowerCAmelCase				)
	scores.update(__lowerCAmelCase				)

	if args.dump_args:
		scores.update(__lowerCAmelCase				)
	if args.info:
		UpperCAmelCase:					Optional[int] 						=     args.info

	if verbose:
		print(__lowerCAmelCase				)

	if args.score_path is not None:
		json.dump(__lowerCAmelCase				,					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 numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCAmelCase							   =     int(n_draws - floored.sum()      )
						if need_to_add > 0:
												_UpperCAmelCase							   =     continuous - floored
												_UpperCAmelCase							   =     np.sort(np.unique(__lowerCAmelCase      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCAmelCase)   ,					)							   =     np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCAmelCase							   =     min(len(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 2:
												raise ValueError('Minimum class count error'      )
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
						_UpperCAmelCase							   =     np.split(np.argsort(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						mode='clip'      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test
