




"""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							:			str     							=							getLogger(__name__)


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



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__ = 8       , snake_case__ = DEFAULT_DEVICE       , snake_case__=False       , snake_case__="summarization"       , snake_case__=None       , **snake_case__       , )						->					Dict:
			lowerCamelCase			      =					Path(snake_case__				).open("""w"""       , encoding="""utf-8"""				)
			lowerCamelCase			      =					str(snake_case__				)
			lowerCamelCase			      =					AutoModelForSeqaSeqLM.from_pretrained(snake_case__				).to(snake_case__				)
			if fpaa:
						lowerCamelCase			      =					model.half()

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

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



def     a__ (				)						->					List[str]:
			return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""				)



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

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

			if args.reference_path is None:
						return {}

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

			if args.dump_args:
						scores.update(snake_case__				)
			if args.info:
						lowerCamelCase			      =					args.info

			if verbose:
						print(snake_case__				)

			if args.score_path is not None:
						json.dump(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)









"""simple docstring"""




from sklearn.metrics import recall_score

import datasets


lowerCAmelCase							:			Any     							=							"""
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


lowerCAmelCase							:			Any     							=							"""
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


lowerCAmelCase							:			Any     							=							"""
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION				)
class     __magic_name__							(       datasets.Metric				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return datasets.MetricInfo(
							    description=_DESCRIPTION		, citation=_CITATION		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
							        {
							            """predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
							            """references""": datasets.Sequence(datasets.Value("""int32""" ) ),
							        }
							        if self.config_name == """multilabel"""
							        else {
							            """predictions""": datasets.Value("""int32""" ),
							            """references""": datasets.Value("""int32""" ),
							        } )		, reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]		, )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a=None		, _a=1		, _a="binary"		, _a=None		, _a="warn"		, ):





							"""simple docstring"""
							lowerCamelCase			      =					recall_score(
							    _a		, _a		, labels=_a		, pos_label=_a		, average=_a		, sample_weight=_a		, zero_division=_a		, )
							return {"recall": float(_a ) if score.size == 1 else score}



