



'''simple docstring'''

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					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = 8						,      lowerCAmelCase_ = DEFAULT_DEVICE						,      lowerCAmelCase_=False						,      lowerCAmelCase_="summarization"						,      lowerCAmelCase_=None						,      **lowerCAmelCase_						,      )      ->				Dict:
		_a					:				Union[str, Any]				=							Path(lowerCAmelCase_  ).open('w'						,      encoding='utf-8'  )
		_a					:				Union[str, Any]				=							str(lowerCAmelCase_  )
		_a					:				Union[str, Any]				=							AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_  ).to(lowerCAmelCase_  )
		if fpaa:
				_a					:				Tuple				=							model.half()

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

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



def 					__lowerCamelCase					(				)      ->				Tuple:
		return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'  )



def 					__lowerCamelCase					(				lowerCAmelCase_=True  )      ->				str:
		_a					:				Union[str, 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
		_a	, _a					:				Dict				=							parser.parse_known_args()
		_a					:				str				=							parse_numeric_n_bool_cl_kwargs(lowerCAmelCase_  )
		if parsed_args and verbose:
				print(f"""parsed the following generate kwargs: {parsed_args}"""  )
		_a					:				Any				=							[' ' + 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					:				List[Any]				=							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."""  )

		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(
		    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
		_a					:				Tuple				=							calculate_bleu if 'translation' in args.task else calculate_rouge
		_a					:				List[str]				=							[x.rstrip() for x in open(args.save_path  ).readlines()]
		_a					:				str				=							[x.rstrip() for x in open(args.reference_path  ).readlines()][: len(lowerCAmelCase_  )]
		_a					:				dict				=							score_fn(lowerCAmelCase_						,      lowerCAmelCase_  )
		scores.update(lowerCAmelCase_  )

		if args.dump_args:
				scores.update(lowerCAmelCase_  )
		if args.info:
				_a					:				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)









'''simple docstring'''

__lowerCAmelCase						      =       range(2, 20 + 1)
__lowerCAmelCase						      =       [10**k for k in range(ks[-1] + 1)]
__lowerCAmelCase						      =       {}



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Optional[Any]:
		_a					:				Optional[int]				=							sum(a_i[j] for j in range(lowerCAmelCase_						,      len(lowerCAmelCase_  )  )  )
		_a					:				List[str]				=							sum(a_i[j] * base[j] for j in range(min(len(lowerCAmelCase_  )						,      lowerCAmelCase_  )  )  )

		_a	, _a					:				Any				=							0, 0
		_a					:				Any				=							n - i

		_a					:				List[Any]				=							memo.get(lowerCAmelCase_  )

		if sub_memo is not None:
				_a					:				Tuple				=							sub_memo.get(lowerCAmelCase_  )

				if jumps is not None and len(lowerCAmelCase_  ) > 0:
						# find and make the largest jump without going over
						_a					:				Any				=							-1
						for _k in range(len(lowerCAmelCase_  ) - 1						,      -1						,      -1  ):
								if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
										_a					:				Any				=							_k
										break

						if max_jump >= 0:
								_a	, _a	, _a					:				Tuple				=							jumps[max_jump]
								# since the difference between jumps is cached, add c
								_a					:				Union[str, Any]				=							diff + c
								for j in range(min(lowerCAmelCase_						,      len(lowerCAmelCase_  )  )  ):
										_a	, _a					:				Dict				=							divmod(lowerCAmelCase_						,      10  )
								if new_c > 0:
										add(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

				else:
						_a					:				Tuple				=							[]
		else:
				_a					:				Any				=							{c: []}
				_a					:				Optional[Any]				=							sub_memo

		if dn >= max_dn or c + diff >= base[k]:
				return diff, dn

		if k > ks[0]:
				while True:
						# keep doing smaller jumps
						_a	, _a					:				Dict				=							next_term(lowerCAmelCase_						,      k - 1						,      i + dn						,      lowerCAmelCase_  )
						diff += _diff
						dn += terms_jumped

						if dn >= max_dn or c + diff >= base[k]:
								break
		else:
				# would be too small a jump, just compute sequential terms instead
				_a	, _a					:				Any				=							compute(lowerCAmelCase_						,      lowerCAmelCase_						,      i + dn						,      lowerCAmelCase_  )
				diff += _diff
				dn += terms_jumped

		_a					:				Tuple				=							sub_memo[c]

		# keep jumps sorted by # of terms skipped
		_a					:				Any				=							0
		while j < len(lowerCAmelCase_  ):
				if jumps[j][1] > dn:
						break
				j += 1

		# cache the jump for this value digitsum(b) and c
		sub_memo[c].insert(lowerCAmelCase_						,      (diff, dn, k)  )
		return (diff, dn)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		if i >= n:
				return 0, i
		if k > len(lowerCAmelCase_  ):
				a_i.extend([0 for _ in range(k - len(lowerCAmelCase_  )  )]  )

		# note: a_i -> b * 10^k + c
		# ds_b -> digitsum(b)
		# ds_c -> digitsum(c)
		_a					:				Any				=							i
		_a	, _a	, _a					:				Optional[int]				=							0, 0, 0
		for j in range(len(lowerCAmelCase_  )  ):
				if j >= k:
						ds_b += a_i[j]
				else:
						ds_c += a_i[j]

		while i < n:
				i += 1
				_a					:				Any				=							ds_c + ds_b
				diff += addend
				_a					:				int				=							0
				for j in range(lowerCAmelCase_  ):
						_a					:				Optional[Any]				=							a_i[j] + addend
						_a	, _a					:				Tuple				=							divmod(lowerCAmelCase_						,      10  )

						ds_c += a_i[j]

				if addend > 0:
						break

		if addend > 0:
				add(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		return diff, i - start_i



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		for j in range(lowerCAmelCase_						,      len(lowerCAmelCase_  )  ):
				_a					:				Optional[Any]				=							digits[j] + addend
				if s >= 10:
						_a	, _a					:				List[str]				=							divmod(lowerCAmelCase_						,      10  )
						_a					:				List[str]				=							addend // 10 + quotient
				else:
						_a					:				Optional[Any]				=							s
						_a					:				Optional[Any]				=							addend // 10

				if addend == 0:
						break

		while addend > 0:
				_a	, _a					:				List[str]				=							divmod(lowerCAmelCase_						,      10  )
				digits.append(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_ = 10**15  )      ->				int:
		_a					:				Dict				=							[1]
		_a					:				int				=							1
		_a					:				Tuple				=							0
		while True:
				_a	, _a					:				str				=							next_term(lowerCAmelCase_						,      20						,      i + dn						,      lowerCAmelCase_  )
				dn += terms_jumped
				if dn == n - i:
						break

		_a					:				Union[str, Any]				=							0
		for j in range(len(lowerCAmelCase_  )  ):
				a_n += digits[j] * 10**j
		return a_n


if __name__ == "__main__":
							print(f"""{solution() = }""")




