
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		: Optional[int]								=     getLogger(__name__)


A		: Union[str, Any]								=     '''cuda''' if torch.cuda.is_available() else '''cpu'''




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

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

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




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




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

						_lowercase					       =					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
						_lowercase					       =					calculate_bleu if """translation""" in args.task else calculate_rouge
						_lowercase					       =					[x.rstrip() for x in open(args.save_path       ).readlines()]
						_lowercase					       =					[x.rstrip() for x in open(args.reference_path       ).readlines()][: len(SCREAMING_SNAKE_CASE_       )]
						_lowercase					       =					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:
												_lowercase					       =					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)

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     list:

						# bit count represents no. of bits in the gray code
						if bit_count < 0:
												raise ValueError("""The given input must be positive"""       )

						# get the generated string sequence
						_lowercase					       =					gray_code_sequence_string(SCREAMING_SNAKE_CASE_       )
						#
						# convert them to integers
						for i in range(len(SCREAMING_SNAKE_CASE_       )       ):
												_lowercase					       =					int(sequence[i]		,   2       )

						return sequence




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     list:

						# The approach is a recursive one
						# Base case achieved when either n = 0 or n=1
						if bit_count == 0:
												return ["0"]

						if bit_count == 1:
												return ["0", "1"]

						_lowercase					       =					1 << bit_count  # defines the length of the sequence
						# 1<< n is equivalent to 2^n

						# recursive answer will generate answer for n-1 bits
						_lowercase					       =					gray_code_sequence_string(bit_count - 1       )

						_lowercase					       =					[]

						# append 0 to first half of the smaller sequence generated
						for i in range(seq_len // 2       ):
												_lowercase					       =					"""0""" + smaller_sequence[i]
												sequence.append(SCREAMING_SNAKE_CASE_       )

						# append 1 to second half ... start from the end of the list
						for i in reversed(range(seq_len // 2       )       ):
												_lowercase					       =					"""1""" + smaller_sequence[i]
												sequence.append(SCREAMING_SNAKE_CASE_       )

						return sequence


if __name__ == "__main__":
		import doctest

		doctest.testmod()