




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 :    Union[str, Any]      						=      getLogger(__name__)


__lowerCamelCase :    int      						=      '''cuda''' if torch.cuda.is_available() else '''cpu'''







def 		lowercase__ (		__A:						List[str]				,__A:						str				,__A:						str				,__A:						int = 8				,__A:						str = DEFAULT_DEVICE				,__A:						Tuple=False				,__A:						Dict="summarization"				,__A:						Any=None				,**__A:						int				,):





					'''simple docstring'''






					__magic_name__				:						Union[str, Any]     				=				Path(__A					).open('''w'''				,encoding='''utf-8'''					)
					__magic_name__				:						Dict     				=				str(__A					)
					__magic_name__				:						Any     				=				AutoModelForSeqaSeqLM.from_pretrained(__A					).to(__A					)
					if fpaa:
										__magic_name__				:						int     				=				model.half()

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

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







def 		lowercase__ (		):





					'''simple docstring'''






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







def 		lowercase__ (		__A:						Any=True					):





					'''simple docstring'''






					__magic_name__				:						Optional[int]     				=				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
					__magic_name__				,     __magic_name__				:						Optional[int]     				=				parser.parse_known_args()
					__magic_name__				:						int     				=				parse_numeric_n_bool_cl_kwargs(__A					)
					if parsed_args and verbose:
										print(F'''parsed the following generate kwargs: {parsed_args}'''					)
					with open(args.input_path					) as f:
										__magic_name__				:						str     				=				[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										__magic_name__				:						Any     				=				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.'''					)
					__magic_name__				:						List[Any]     				=				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
					__magic_name__				:						str     				=				calculate_bleu if '''translation''' in args.task else calculate_rouge
					__magic_name__				:						Union[str, Any]     				=				[x.rstrip() for x in open(args.save_path					).readlines()]
					__magic_name__				:						Union[str, Any]     				=				[x.rstrip() for x in open(args.reference_path					).readlines()][: len(__A					)]
					__magic_name__				:						dict     				=				score_fn(__A				,__A					)
					scores.update(__A					)

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









def 		lowercase__ (		__A:						list[list[int]]					):





					'''simple docstring'''






					if len(__A					) == 0 or not all(len(__A					) == 2 for pair in coordinates					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					if len({tuple(__A					) for pair in coordinates}					) != len(__A					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					__magic_name__				:						Union[str, Any]     				=				{x for x, _ in coordinates}
					if len(__A					) == 1:
										return F'''x={coordinates[0][0]}'''

					if len(__A					) != len(__A					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					__magic_name__				:						List[Any]     				=				len(__A					)

					__magic_name__				:						int     				=				0
					__magic_name__				:						list[list[float]]     				=				[]
					# put the x and x to the power values in a matrix
					while count_of_line < x:
										__magic_name__				:						List[Any]     				=				0
										__magic_name__				:						List[str]     				=				coordinates[count_of_line][0]
										__magic_name__				:						list[float]     				=				[]
										while count_in_line < x:
															count_line.append(a ** (x - (count_in_line + 1))					)
															count_in_line += 1
										matrix.append(__A					)
										count_of_line += 1

					__magic_name__				:						Union[str, Any]     				=				0
					# put the y values into a vector
					__magic_name__				:						list[float]     				=				[]
					while count_of_line < x:
										vector.append(coordinates[count_of_line][1]					)
										count_of_line += 1

					__magic_name__				:						int     				=				0

					while count < x:
										__magic_name__				:						int     				=				0
										while zahlen < x:
															if count == zahlen:
																				zahlen += 1
															if zahlen == x:
																				break
															__magic_name__				:						Tuple     				=				matrix[zahlen][count] / matrix[count][count]
															for counting_columns, item in enumerate(matrix[count]					):
																				# manipulating all the values in the matrix
																				matrix[zahlen][counting_columns] -= item * bruch
															# manipulating the values in the vector
															vector[zahlen] -= vector[count] * bruch
															zahlen += 1
										count += 1

					__magic_name__				:						str     				=				0
					# make solutions
					__magic_name__				:						list[str]     				=				[]
					while count < x:
										solution.append(str(vector[count] / matrix[count][count]					)					)
										count += 1

					__magic_name__				:						List[Any]     				=				0
					__magic_name__				:						Union[str, Any]     				=				'''f(x)='''

					while count < x:
										__magic_name__				:						list[str]     				=				solution[count].split('''E'''					)
										if len(__A					) > 1:
															__magic_name__				:						Union[str, Any]     				=				F'''{remove_e[0]}*10^{remove_e[1]}'''
										solved += F'''x^{x - (count + 1)}*{solution[count]}'''
										if count + 1 != x:
															solved += "+"
										count += 1

					return solved


if __name__ == "__main__":
	print(points_to_polynomial([]))
	print(points_to_polynomial([[]]))
	print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
	print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
	print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
	print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
	print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
	print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))



