


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[Any]     						=     getLogger(__name__)


A							:					Dict     						=     '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:


						'''simple docstring'''






						__snake_case				=		Path(__A ).open("w"      ,	encoding="utf-8" )
						__snake_case				=		str(__A )
						__snake_case				=		AutoModelForSeqaSeqLM.from_pretrained(__A ).to(__A )
						if fpaa:
												__snake_case				=		model.half()

						__snake_case				=		AutoTokenizer.from_pretrained(__A )
						logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}''' )  # if this is wrong, check config.model_type.

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





def 	_lowerCAmelCase       (    )						->	Optional[Any]:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase=True )						->	List[Any]:


						'''simple docstring'''






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

						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" )

						__snake_case				=		generate_summaries_or_translations(
						    __A      ,	args.save_path      ,	args.model_name      ,	batch_size=args.bs      ,	device=args.device      ,	fpaa=args.fpaa      ,	task=args.task      ,	prefix=args.prefix      ,	**__A      ,	)

						if args.reference_path is None:
												return {}

						# Compute scores
						__snake_case				=		calculate_bleu if '''translation''' in args.task else calculate_rouge
						__snake_case				=		[x.rstrip() for x in open(args.save_path ).readlines()]
						__snake_case				=		[x.rstrip() for x in open(args.reference_path ).readlines()][: len(__A )]
						__snake_case				=		score_fn(__A      ,	__A )
						scores.update(__A )

						if args.dump_args:
												scores.update(__A )
						if args.info:
												__snake_case				=		args.info

						if verbose:
												print(__A )

						if args.score_path is not None:
												json.dump(__A      ,	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 __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase = 10**-10 )						->	float:


						'''simple docstring'''






						__snake_case				=		a
						while True:
												__snake_case				=		Decimal(_lowerCAmelCase ) - (
												    Decimal(eval(_lowerCAmelCase ) ) / Decimal(eval(str(diff(_lowerCAmelCase ) ) ) )  # noqa: S307
												)
												# This number dictates the accuracy of the answer
												if abs(eval(_lowerCAmelCase ) ) < precision:  # noqa: S307
																		return float(_lowerCAmelCase )


# Let's Execute
if __name__ == "__main__":
	# Find root of trigonometric function
	# Find value of pi
	print(f'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
	# Find root of polynomial
	print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
	# Find Square Root of 5
	print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
	# Exponential Roots
	print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')





