




import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
  import torch_xla.debug.metrics as met







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     *_a		,     _a=None		,     _a=None		,     **_a				)						->							Union[str, Any]:
           super().__init__(*UpperCAmelCase__		,     **UpperCAmelCase__				)
           _A							:     Union[str, Any]			=						eval_examples
           _A							:     List[str]			=						post_process_function

     def 					a__	(  self		,     _a = None		,     _a=None		,     _a = None		,     _a = "eval"		,     **_a		,     )						->							str:
           _A							:     Any			=						gen_kwargs.copy()
           _A							:     Tuple			=						(
               gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""				) is not None else self.args.generation_max_length
           )
           _A							:     int			=						(
               gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""				) is not None else self.args.generation_num_beams
           )
           _A							:     List[str]			=						gen_kwargs

           _A							:     List[str]			=						self.eval_dataset if eval_dataset is None else eval_dataset
           _A							:     List[str]			=						self.get_eval_dataloader(UpperCAmelCase__				)
           _A							:     List[Any]			=						self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           _A							:     Dict			=						self.compute_metrics
           _A							:     List[str]			=						None
           _A							:     List[Any]			=						time.time()
           _A							:     Any			=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
                 _A							:     Any			=						eval_loop(
                     UpperCAmelCase__		,     description="""Evaluation"""		,     prediction_loss_only=True if compute_metrics is None else None		,     ignore_keys=UpperCAmelCase__		,     metric_key_prefix=UpperCAmelCase__		,     )
           finally:
                 _A							:     Optional[int]			=						compute_metrics
           _A							:     Any			=						self.args.eval_batch_size * self.args.world_size
           if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
                 start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
           output.metrics.update(
               speed_metrics(
                   UpperCAmelCase__		,     UpperCAmelCase__		,     num_samples=output.num_samples		,     num_steps=math.ceil(output.num_samples / total_batch_size				)		,     )				)

           if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
                 # Only the main node write the results by default
                 _A							:     Tuple			=						self.post_process_function(UpperCAmelCase__		,     UpperCAmelCase__		,     UpperCAmelCase__				)
                 _A							:     Tuple			=						self.compute_metrics(UpperCAmelCase__				)

                 # Prefix all keys with metric_key_prefix + '_'
                 for key in list(metrics.keys()				):
                       if not key.startswith(F'''{metric_key_prefix}_'''				):
                             _A							:     Any			=						metrics.pop(UpperCAmelCase__				)

                 metrics.update(output.metrics				)
           else:
                 _A							:     str			=						output.metrics

           if self.args.should_log:
                 # Only the main node log the results by default
                 self.log(UpperCAmelCase__				)

           if self.args.tpu_metrics_debug or self.args.debug:
                 # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                 xm.master_print(met.metrics_report()				)

           _A							:     List[Any]			=						self.callback_handler.on_evaluate(self.args		,     self.state		,     self.control		,     UpperCAmelCase__				)
           return metrics



     def 					a__	(  self		,     _a		,     _a		,     _a=None		,     _a = "test"		,     **_a				)						->							Optional[Any]:
           _A							:     int			=						gen_kwargs.copy()

           _A							:     int			=						self.get_test_dataloader(UpperCAmelCase__				)

           # Temporarily disable metric computation, we will do it in the loop here.
           _A							:     Optional[Any]			=						self.compute_metrics
           _A							:     Optional[Any]			=						None
           _A							:     List[str]			=						time.time()
           _A							:     str			=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
                 _A							:     Optional[int]			=						eval_loop(
                     UpperCAmelCase__		,     description="""Prediction"""		,     prediction_loss_only=True if compute_metrics is None else None		,     ignore_keys=UpperCAmelCase__		,     metric_key_prefix=UpperCAmelCase__		,     )
           finally:
                 _A							:     Union[str, Any]			=						compute_metrics

           _A							:     Optional[int]			=						self.args.eval_batch_size * self.args.world_size
           if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
                 start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
           output.metrics.update(
               speed_metrics(
                   UpperCAmelCase__		,     UpperCAmelCase__		,     num_samples=output.num_samples		,     num_steps=math.ceil(output.num_samples / total_batch_size				)		,     )				)
           if self.post_process_function is None or self.compute_metrics is None:
                 return output

           _A							:     Optional[int]			=						self.post_process_function(UpperCAmelCase__		,     UpperCAmelCase__		,     UpperCAmelCase__		,     """predict"""				)
           _A							:     Union[str, Any]			=						self.compute_metrics(UpperCAmelCase__				)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()				):
                 if not key.startswith(F'''{metric_key_prefix}_'''				):
                       _A							:     Optional[int]			=						metrics.pop(UpperCAmelCase__				)
           metrics.update(output.metrics				)
           return PredictionOutput(predictions=predictions.predictions		,     label_ids=predictions.label_ids		,     metrics=UpperCAmelCase__				)




"""simple docstring"""





from scipy.stats import spearmanr

import datasets


_a       =							'\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

_a       =							'\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

_a       =							r'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'




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







 """simple docstring"""







 def 					_lowercase (			self							:			Dict	):
    return datasets.MetricInfo(
        description=_DESCRIPTION,					citation=_CITATION,					inputs_description=_KWARGS_DESCRIPTION,					features=datasets.Features(
            {
                "predictions": datasets.Value("float"	),
                "references": datasets.Value("float"	),
            }	),					reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"],					)



 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[int]=False	):
    __lowercase								=						spearmanr(UpperCAmelCase__,					UpperCAmelCase__	)
    if return_pvalue:
       return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
    else:
       return {"spearmanr": results[0]}




