






"""simple docstring"""
from scipy.stats import pearsonr

import datasets


lowerCamelCase_							      = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


lowerCamelCase_							      = '''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


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







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





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     str:
				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.pearsonr.html"]      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str=False		)		->     str:
				if return_pvalue:
							UpperCAmelCase_	:					List[Any]     	=   pearsonr(lowerCAmelCase_      ,      lowerCAmelCase_		)
							return {"pearsonr": results[0], "p-value": results[1]}
				else:
							return {"pearsonr": float(pearsonr(lowerCAmelCase_      ,      lowerCAmelCase_		)[0]		)}











"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
			from ... import PreTrainedTokenizerBase, TensorType

lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''vision-encoder-decoder'''
	__magic_name__       =							True





	def __init__( self   :					Union[str, Any]      ,      **lowerCAmelCase_   :					List[Any]		)		->     Union[str, Any]:
				super().__init__(**lowerCAmelCase_		)
				if "encoder" not in kwargs or "decoder" not in kwargs:
							raise ValueError(
							    f"""A configuraton of type {self.model_type} cannot be instantiated because """
							    f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""		)

				UpperCAmelCase_	:					str     	=   kwargs.pop("encoder"		)
				UpperCAmelCase_	:					Dict     	=   encoder_config.pop("model_type"		)
				UpperCAmelCase_	:					List[Any]     	=   kwargs.pop("decoder"		)
				UpperCAmelCase_	:					Dict     	=   decoder_config.pop("model_type"		)

				UpperCAmelCase_	:					Union[str, Any]     	=   AutoConfig.for_model(lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   AutoConfig.for_model(lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   True





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					List[str]      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					PretrainedConfig      ,      **lowerCAmelCase_   :					Dict		)		->     PretrainedConfig:
				logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
				UpperCAmelCase_	:					Tuple     	=   True
				UpperCAmelCase_	:					str     	=   True

				return cls(encoder=encoder_config.to_dict()      ,      decoder=decoder_config.to_dict()      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   copy.deepcopy(self.__dict__		)
				UpperCAmelCase_	:					List[str]     	=   self.encoder.to_dict()
				UpperCAmelCase_	:					List[Any]     	=   self.decoder.to_dict()
				UpperCAmelCase_	:					Any     	=   self.__class__.model_type
				return output







class      UpperCamelCase_			(__A	):
	__magic_name__       =							version.parse('''1.11'''	)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     float:
				return 1e-4





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Mapping[str, Mapping[int, str]]:
				return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}		)







class      UpperCamelCase_			(__A	):





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_	:					List[Any]     	=   OrderedDict()
				UpperCAmelCase_	:					int     	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
				UpperCAmelCase_	:					Dict     	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
				UpperCAmelCase_	:					Union[str, Any]     	=   {0: "batch", 1: "encoder_sequence"}

				return common_inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					"PreTrainedTokenizerBase"      ,      lowerCAmelCase_   :					int = -1      ,      lowerCAmelCase_   :					int = -1      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					Optional["TensorType"] = None      ,      )		->     Mapping[str, Any]:
				import torch

				UpperCAmelCase_	:					Optional[int]     	=   OrderedDict()

				UpperCAmelCase_	:					Any     	=   super().generate_dummy_inputs(
				    lowerCAmelCase_      ,      batch_size=lowerCAmelCase_      ,      seq_length=lowerCAmelCase_      ,      is_pair=lowerCAmelCase_      ,      framework=lowerCAmelCase_		)

				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   dummy_input["input_ids"].shape
				UpperCAmelCase_	:					List[str]     	=   (batch, encoder_sequence, self._config.encoder_hidden_size)
				UpperCAmelCase_	:					List[str]     	=   dummy_input.pop("input_ids"		)
				UpperCAmelCase_	:					List[str]     	=   dummy_input.pop("attention_mask"		)
				UpperCAmelCase_	:					List[Any]     	=   torch.zeros(lowerCAmelCase_		)

				return common_inputs







class      UpperCamelCase_			(__A	):





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     None:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					PretrainedConfig		)		->     OnnxConfig:
				return VisionEncoderDecoderEncoderOnnxConfig(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					str = "default"		)		->     OnnxConfig:
				UpperCAmelCase_	:					Optional[int]     	=   encoder_config.hidden_size
				return VisionEncoderDecoderDecoderOnnxConfig(lowerCAmelCase_      ,      lowerCAmelCase_		)



