
'''simple docstring'''



import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_lowercase          =						getLogger(__name__)


def A (__lowerCamelCase :Optional[int]					,      __lowerCamelCase :str					,      __lowerCamelCase :str					,      __lowerCamelCase :int = 8					,      __lowerCamelCase :int = 1024					,      __lowerCamelCase :str="val"					,      __lowerCamelCase :Optional[Any]=None					,      __lowerCamelCase :Optional[int]=False					,      __lowerCamelCase :str="summarization"					,      __lowerCamelCase :Tuple=None					,      __lowerCamelCase :List[Any]=1					,      __lowerCamelCase :Dict = None					,      __lowerCamelCase :Optional[int]=""					,      **__lowerCamelCase :str					,      ):
						_lowerCAmelCase    		=					str(__lowerCamelCase						)
						assert local_rank is not None
						torch.distributed.init_process_group(backend="""nccl"""					,      rank=__lowerCamelCase						)

						_lowerCAmelCase    		=					Path(__lowerCamelCase						)
						_lowerCAmelCase    		=					save_dir.joinpath(f'rank_{local_rank}_output.json'						)
						torch.cuda.set_device(__lowerCamelCase						)
						_lowerCAmelCase    		=					AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase						).cuda()
						if fpaa:
												_lowerCAmelCase    		=					model.half()
						# determine if we need to increase num_beams
						use_task_specific_params(__lowerCamelCase					,      __lowerCamelCase						)  # update config with task specific params
						_lowerCAmelCase    		=					generate_kwargs.pop("""num_beams"""					,      model.config.num_beams						)  # AttributeError risk?
						if num_return_sequences > num_beams:
												_lowerCAmelCase    		=					num_return_sequences

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

						if max_source_length is None:
												_lowerCAmelCase    		=					tokenizer.model_max_length
						if prefix is None:
												_lowerCAmelCase    		=					prefix or getattr(model.config					,      """prefix"""					,      """"""						) or """"""
						_lowerCAmelCase    		=					SeqaSeqDataset(
						    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      max_target_length=1024					,      type_path=__lowerCamelCase					,      n_obs=__lowerCamelCase					,      prefix=__lowerCamelCase					,      **__lowerCamelCase					,      )
						# I set shuffle=True for a more accurate progress bar.
						# If all the longest samples are first, the prog bar estimate is too high at the beginning.
						_lowerCAmelCase    		=					ds.make_sortish_sampler(__lowerCamelCase					,      distributed=__lowerCamelCase					,      add_extra_examples=__lowerCamelCase					,      shuffle=__lowerCamelCase						)
						_lowerCAmelCase    		=					DataLoader(__lowerCamelCase					,      sampler=__lowerCamelCase					,      batch_size=__lowerCamelCase					,      collate_fn=ds.collate_fn						)
						_lowerCAmelCase    		=					[]
						for batch in tqdm(__lowerCamelCase						):
												_lowerCAmelCase    		=					model.generate(
												    input_ids=batch["""input_ids"""].to(model.device						)					,      attention_mask=batch["""attention_mask"""].to(model.device						)					,      num_return_sequences=__lowerCamelCase					,      num_beams=__lowerCamelCase					,      **__lowerCamelCase					,      )
												_lowerCAmelCase    		=					tokenizer.batch_decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase					,      clean_up_tokenization_spaces=__lowerCamelCase						)
												_lowerCAmelCase    		=					batch["""ids"""]
												if num_return_sequences > 1:
																		_lowerCAmelCase    		=					chunks(__lowerCamelCase					,      __lowerCamelCase						)  # batch size chunks, each of size num_return_seq
												for i, pred in enumerate(__lowerCamelCase						):
																		results.append({"""pred""": pred, """id""": ids[i].item()}						)
						save_json(__lowerCamelCase					,      __lowerCamelCase						)
						return results, sampler.num_replicas


def A ():
						_lowerCAmelCase    		=					argparse.ArgumentParser(
						    epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"""						)
						parser.add_argument("""--data_dir"""					,      type=__lowerCamelCase					,      help="""like cnn_dm/test.source"""						)
						parser.add_argument(
						    """--model_name"""					,      type=__lowerCamelCase					,      help="""like facebook/bart-large-cnn,t5-base, etc."""					,      default="""sshleifer/distilbart-xsum-12-3"""					,      )
						parser.add_argument("""--save_dir"""					,      type=__lowerCamelCase					,      help="""where to save"""					,      default="""tmp_gen"""						)
						parser.add_argument("""--max_source_length"""					,      type=__lowerCamelCase					,      default=__lowerCamelCase						)
						parser.add_argument(
						    """--type_path"""					,      type=__lowerCamelCase					,      default="""test"""					,      help="""which subset to evaluate typically train/val/test"""						)
						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(
						    """--local_rank"""					,      type=__lowerCamelCase					,      default=-1					,      required=__lowerCamelCase					,      help="""should be passed by distributed.launch"""						)

						parser.add_argument(
						    """--n_obs"""					,      type=__lowerCamelCase					,      default=__lowerCamelCase					,      required=__lowerCamelCase					,      help="""How many observations. Defaults to all."""						)
						parser.add_argument(
						    """--num_return_sequences"""					,      type=__lowerCamelCase					,      default=1					,      required=__lowerCamelCase					,      help="""How many sequences to return"""						)
						parser.add_argument(
						    """--sync_timeout"""					,      type=__lowerCamelCase					,      default=600					,      required=__lowerCamelCase					,      help="""How long should master process wait for other processes to finish."""					,      )
						parser.add_argument("""--src_lang"""					,      type=__lowerCamelCase					,      default=__lowerCamelCase					,      required=__lowerCamelCase						)
						parser.add_argument("""--tgt_lang"""					,      type=__lowerCamelCase					,      default=__lowerCamelCase					,      required=__lowerCamelCase						)
						parser.add_argument(
						    """--prefix"""					,      type=__lowerCamelCase					,      required=__lowerCamelCase					,      default=__lowerCamelCase					,      help="""will be added to the begininng of src examples"""						)
						parser.add_argument("""--fp16"""					,      action="""store_true"""						)
						parser.add_argument("""--debug"""					,      action="""store_true"""						)
						_lowerCAmelCase    		=					time.time()
						_lowerCAmelCase		,       _lowerCAmelCase    		=					parser.parse_known_args()
						_lowerCAmelCase    		=					parse_numeric_n_bool_cl_kwargs(__lowerCamelCase						)
						if generate_kwargs and args.local_rank <= 0:
												print(f'parsed the following generate kwargs: {generate_kwargs}'						)
						_lowerCAmelCase    		=					Path(args.save_dir + """_tmp"""						)
						Path(__lowerCamelCase						).mkdir(exist_ok=__lowerCamelCase						)  # this handles locking.
						_lowerCAmelCase    		=					list(json_save_dir.glob("""rank_*.json"""						)						)
						if intermediate_files:
												raise ValueError(f'Found files at {json_save_dir} please move or remove them.'						)
												# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
						_lowerCAmelCase    		=					{}
						if args.src_lang is not None:
												_lowerCAmelCase    		=					args.src_lang
						if args.tgt_lang is not None:
												_lowerCAmelCase    		=					args.tgt_lang

						Path(args.save_dir						).mkdir(exist_ok=__lowerCamelCase						)
						_lowerCAmelCase		,       _lowerCAmelCase    		=					eval_data_dir(
						    args.data_dir					,      __lowerCamelCase					,      args.model_name					,      type_path=args.type_path					,      bs=args.bs					,      fpaa=args.fpaa					,      task=args.task					,      local_rank=args.local_rank					,      n_obs=args.n_obs					,      max_source_length=args.max_source_length					,      num_return_sequences=args.num_return_sequences					,      prefix=args.prefix					,      dataset_kwargs=__lowerCamelCase					,      **__lowerCamelCase					,      )

						if args.local_rank <= 0:
												_lowerCAmelCase    		=					Path(args.save_dir						)
												save_dir.mkdir(exist_ok=__lowerCamelCase						)
												_lowerCAmelCase    		=					gather_results_from_each_node(__lowerCamelCase					,      __lowerCamelCase					,      args.sync_timeout						)
												_lowerCAmelCase    		=					combine_partial_results(__lowerCamelCase						)
												if args.num_return_sequences > 1:
																		_lowerCAmelCase    		=					save_dir.joinpath("""pseudolabel_results.json"""						)
																		print(f'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'						)
																		save_json(__lowerCamelCase					,      __lowerCamelCase						)
																		return
												_lowerCAmelCase    		=					Path(args.data_dir						).joinpath(args.type_path + """.target"""						)
												with open(__lowerCamelCase						) as f:
																		_lowerCAmelCase    		=					[x.rstrip() for x in f.readlines()][: len(__lowerCamelCase						)]

												# Calculate metrics, save metrics,  and save _generations.txt
												_lowerCAmelCase    		=					"""translation""" in args.task
												_lowerCAmelCase    		=					calculate_bleu if calc_bleu else calculate_rouge
												_lowerCAmelCase    		=					"""bleu""" if calc_bleu else """rouge"""
												_lowerCAmelCase    		=					score_fn(__lowerCamelCase					,      __lowerCamelCase						)
												_lowerCAmelCase    		=					len(__lowerCamelCase						)
												_lowerCAmelCase    		=					time.time() - start_time
												_lowerCAmelCase    		=					round(runtime / metrics["""n_obs"""]					,      4						)
												_lowerCAmelCase    		=					num_replicas
												# TODO(@stas00): add whatever metadata to metrics
												_lowerCAmelCase    		=					save_dir.joinpath(f'{args.type_path}_{metric_name}.json'						)
												save_json(__lowerCamelCase					,      __lowerCamelCase					,      indent=__lowerCamelCase						)
												print(__lowerCamelCase						)
												write_txt_file(__lowerCamelCase					,      save_dir.joinpath(f'{args.type_path}_generations.txt'						)						)
												if args.debug:
																		write_txt_file(__lowerCamelCase					,      save_dir.joinpath(f'{args.type_path}.target'						)						)
												else:
																		shutil.rmtree(__lowerCamelCase						)


def A (__lowerCamelCase :Optional[Any]						):
						_lowerCAmelCase    		=					[]
						for partial_result in partial_results:
												records.extend(__lowerCamelCase						)
						_lowerCAmelCase    		=					sorted(__lowerCamelCase					,      key=lambda __lowerCamelCase						: x["id"]						)
						_lowerCAmelCase    		=					[x["""pred"""] for x in records]
						return preds


def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :Dict					,      __lowerCamelCase :List[Any]						):
						# WAIT FOR lots of .json files
						_lowerCAmelCase    		=					time.time()
						logger.info("""waiting for all nodes to finish"""						)
						_lowerCAmelCase    		=					None
						while (time.time() - start_wait) < timeout:
												_lowerCAmelCase    		=					list(save_dir.glob("""rank_*.json"""						)						)
												if len(__lowerCamelCase						) < num_replicas:
																		continue
												try:
																		# make sure all json files are fully saved
																		_lowerCAmelCase    		=					lmap(__lowerCamelCase					,      __lowerCamelCase						)
																		return json_data
												except JSONDecodeError:
																		continue
						else:
												raise TimeoutError("""Rank 0 gave up on waiting for other processes"""						)
						# Unreachable


if __name__ == "__main__":
						# Usage for MT:
						run_generate()


'''simple docstring'''



from sklearn.metrics import mean_squared_error

import datasets


_lowercase          =						"""\
@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}
}
"""

_lowercase          =						"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_lowercase          =						"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""




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






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,  citation=_CITATION       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(self._get_feature_types() )       ,  reference_urls=[
												        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
												    ]       ,  )




						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.config_name == "multilist":
																		return {
																		    "predictions": datasets.Sequence(datasets.Value("""float""" ) ),
																		    "references": datasets.Sequence(datasets.Value("""float""" ) ),
																		}
												else:
																		return {
																		    "predictions": datasets.Value("""float""" ),
																		    "references": datasets.Value("""float""" ),
																		}




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="uniform_average"       ,  _lowercase=True ):

												"""simple docstring"""



												_lowerCAmelCase    		=					mean_squared_error(
												    _lowercase       ,  _lowercase       ,  sample_weight=_lowercase       ,  multioutput=_lowercase       ,  squared=_lowercase )

												return {"mse": mse}
