






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__     :      int             =			getLogger(__name__)


A__     :      str             =			'cuda' if torch.cuda.is_available() else 'cpu'






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 8		,       lowerCamelCase_ = DEFAULT_DEVICE		,       lowerCamelCase_=False		,       lowerCamelCase_="summarization"		,       lowerCamelCase_=None		,       **lowerCamelCase_		,       ):






				'''simple docstring'''
				lowercase__       			=      Path(lowerCamelCase_    ).open('''w'''		,       encoding='''utf-8'''    )
				lowercase__       			=      str(lowerCamelCase_    )
				lowercase__       			=      AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_    ).to(lowerCamelCase_    )
				if fpaa:
								lowercase__       			=      model.half()

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






def   a      (						):






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






def   a      (						lowerCamelCase_=True    ):






				'''simple docstring'''
				lowercase__       			=      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
				lowercase__			,						lowercase__       			=      parser.parse_known_args()
				lowercase__       			=      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:
								lowercase__       			=      [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
				if args.n_obs > 0:
								lowercase__       			=      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."""    )
				lowercase__       			=      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
				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(lowerCamelCase_    )]
				lowercase__       			=      score_fn(lowerCamelCase_		,       lowerCamelCase_    )
				scores.update(lowerCamelCase_    )

				if args.dump_args:
								scores.update(lowerCamelCase_    )
				if args.info:
								lowercase__       			=      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
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   Union[str, Any],  lowerCamelCase						:   int=5,  lowerCamelCase						:   Optional[Any]=5  ):

								'''simple docstring'''
								lowercase__       			=      depth
								lowercase__       			=      0
								lowercase__       			=      None
								lowercase__       			=      None
								lowercase__       			=      min_leaf_size
								lowercase__       			=      None




				def     lowercase__       (					self						:   Union[str, Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								if labels.ndim != 1:
												print('''Error: Input labels must be one dimensional'''  )

								return np.mean((labels - prediction) ** 2  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								if x.ndim != 1:
												print('''Error: Input data set must be one dimensional'''  )
												return
								if len(lowerCamelCase  ) != len(lowerCamelCase  ):
												print('''Error: X and y have different lengths'''  )
												return
								if y.ndim != 1:
												print('''Error: Data set labels must be one dimensional'''  )
												return

								if len(lowerCamelCase  ) < 2 * self.min_leaf_size:
												lowercase__       			=      np.mean(lowerCamelCase  )
												return

								if self.depth == 1:
												lowercase__       			=      np.mean(lowerCamelCase  )
												return

								lowercase__       			=      0
								lowercase__       			=      self.mean_squared_error(lowerCamelCase,  np.mean(lowerCamelCase  )  ) * 2
								for i in range(len(lowerCamelCase  )  ):
												if len(x[:i]  ) < self.min_leaf_size:
																continue
												elif len(x[i:]  ) < self.min_leaf_size:
																continue
												else:
																lowercase__       			=      self.mean_squared_error(x[:i],  np.mean(y[:i]  )  )
																lowercase__       			=      self.mean_squared_error(x[i:],  np.mean(y[i:]  )  )
																lowercase__       			=      error_left + error_right
																if error < min_error:
																				lowercase__       			=      i
																				lowercase__       			=      error

								if best_split != 0:
												lowercase__       			=      x[:best_split]
												lowercase__       			=      y[:best_split]
												lowercase__       			=      x[best_split:]
												lowercase__       			=      y[best_split:]

												lowercase__       			=      x[best_split]
												lowercase__       			=      DecisionTree(
												    depth=self.depth - 1,  min_leaf_size=self.min_leaf_size  )
												lowercase__       			=      DecisionTree(
												    depth=self.depth - 1,  min_leaf_size=self.min_leaf_size  )
												self.left.train(lowerCamelCase,  lowerCamelCase  )
												self.right.train(lowerCamelCase,  lowerCamelCase  )
								else:
												lowercase__       			=      np.mean(lowerCamelCase  )

								return




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								if self.prediction is not None:
												return self.prediction
								elif self.left or self.right is not None:
												if x >= self.decision_boundary:
																return self.right.predict(lowerCamelCase  )
												else:
																return self.left.predict(lowerCamelCase  )
								else:
												print('''Error: Decision tree not yet trained'''  )
												return None



class 				_UpperCAmelCase :

				"""simple docstring"""




				@staticmethod
				def     lowercase__       (					lowerCamelCase						:   int,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      float(0  )
								for label in labels:
												squared_error_sum += (label - prediction) ** 2

								return float(squared_error_sum / labels.size  )






def   a      (						):






				'''simple docstring'''
				lowercase__       			=      np.arange(-1.0		,       1.0		,       0.0_05    )
				lowercase__       			=      np.sin(lowerCamelCase_    )

				lowercase__       			=      DecisionTree(depth=10		,       min_leaf_size=10    )
				tree.train(lowerCamelCase_		,       lowerCamelCase_    )

				lowercase__       			=      (np.random.rand(10    ) * 2) - 1
				lowercase__       			=      np.array([tree.predict(lowerCamelCase_    ) for x in test_cases]    )
				lowercase__       			=      np.mean((predictions - test_cases) ** 2    )

				print('''Test values: ''' + str(lowerCamelCase_    )    )
				print('''Predictions: ''' + str(lowerCamelCase_    )    )
				print('''Average error: ''' + str(lowerCamelCase_    )    )


if __name__ == "__main__":
							main()
							import doctest

							doctest.testmod(name='mean_squarred_error', verbose=True)




