






from scipy.stats import pearsonr

import datasets


lowerCamelCase			:					Optional[Any]         =	'''
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			:					Optional[Any]         =	'''
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			:					int         =	'''
@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  __UpperCAmelCase		(	self	)->	List[Any]:
           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  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase=False	)->	Tuple:
           if return_pvalue:
                __lowerCAmelCase								=					pearsonr(__UpperCamelCase					,						__UpperCamelCase	)
                return {"pearsonr": results[0], "p-value": results[1]}
           else:
                return {"pearsonr": float(pearsonr(__UpperCamelCase					,						__UpperCamelCase	)[0]	)}














# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase			:					List[str]         =	'''Create a default config file for Accelerate with only a few flags set.'''





def       __lowerCAmelCase      (						__snake_case="no"					, __snake_case = default_json_config_file					, __snake_case = False       ):
     __lowerCAmelCase								=					Path(__snake_case       )
     path.parent.mkdir(parents=__snake_case					, exist_ok=__snake_case       )
     if path.exists():
          print(
              F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""       )
          return False
     __lowerCAmelCase								=					mixed_precision.lower()
     if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
          raise ValueError(
              F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""       )
     __lowerCAmelCase								=					{
         "compute_environment": "LOCAL_MACHINE",
         "mixed_precision": mixed_precision,
     }
     if torch.cuda.is_available():
          __lowerCAmelCase								=					torch.cuda.device_count()
          __lowerCAmelCase								=					num_gpus
          __lowerCAmelCase								=					False
          if num_gpus > 1:
               __lowerCAmelCase								=					"MULTI_GPU"
          else:
               __lowerCAmelCase								=					"NO"
     elif is_xpu_available() and use_xpu:
          __lowerCAmelCase								=					torch.xpu.device_count()
          __lowerCAmelCase								=					num_xpus
          __lowerCAmelCase								=					False
          if num_xpus > 1:
               __lowerCAmelCase								=					"MULTI_XPU"
          else:
               __lowerCAmelCase								=					"NO"
     elif is_npu_available():
          __lowerCAmelCase								=					torch.npu.device_count()
          __lowerCAmelCase								=					num_npus
          __lowerCAmelCase								=					False
          if num_npus > 1:
               __lowerCAmelCase								=					"MULTI_NPU"
          else:
               __lowerCAmelCase								=					"NO"
     else:
          __lowerCAmelCase								=					0
          __lowerCAmelCase								=					True
          __lowerCAmelCase								=					1
          __lowerCAmelCase								=					"NO"
     __lowerCAmelCase								=					ClusterConfig(**__snake_case       )
     config.to_json_file(__snake_case       )
     return path





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     __lowerCAmelCase								=					parser.add_parser("default"					, parents=__snake_case					, help=__snake_case					, formatter_class=__snake_case       )
     parser.add_argument(
         "--config_file"					, default=__snake_case					, help=(
             "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
             "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
             "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
             "with 'huggingface'."
         )					, dest="save_location"					, )

     parser.add_argument(
         "--mixed_precision"					, choices=["no", "fp16", "bf16"]					, type=__snake_case					, help="Whether or not to use mixed precision training. "
         "Choose between FP16 and BF16 (bfloat16) training. "
         "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."					, default="no"					, )
     parser.set_defaults(func=__snake_case       )
     return parser





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					write_basic_config(args.mixed_precision					, args.save_location       )
     if config_file:
          print(F"""accelerate configuration saved at {config_file}"""       )






