





from scipy.stats import pearsonr

import datasets


_SCREAMING_SNAKE_CASE      	=		"""
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.
"""


_SCREAMING_SNAKE_CASE      	=		"""
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
"""


_SCREAMING_SNAKE_CASE      	=		"""
@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 SCREAMING_SNAKE_CASE_						(					datasets.Metric					):






						def UpperCAmelCase_				(      self      :      int	)							->    Dict:







							"""simple docstring"""


							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      :      Union[str, Any]		,   _A      :      Optional[int]		,   _A      :      Tuple		,   _A      :      int=False	)							->    Optional[int]:







							"""simple docstring"""


							if return_pvalue:
								snake_case_		:			int											=	pearsonr(_A		,   _A	)
								return {"pearsonr": results[0], "p-value": results[1]}
							else:
								return {"pearsonr": float(pearsonr(_A		,   _A	)[0]	)}








# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


_SCREAMING_SNAKE_CASE      	=		Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

_SCREAMING_SNAKE_CASE      	=		{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

_SCREAMING_SNAKE_CASE      	=		"""zero2"""
_SCREAMING_SNAKE_CASE      	=		"""zero3"""
_SCREAMING_SNAKE_CASE      	=		[ZEROa, ZEROa]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	# customize the test name generator function as we want both params to appear in the sub-test
	# name, as by default it shows only the first param
	snake_case_		:			Tuple											=	parameterized.to_safe_name('_'.join(str(__a   ) for x in param.args   )   )
	return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
_SCREAMING_SNAKE_CASE      	=		list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Any		,   _A      :      int	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[str]		,   _A      :      str	)							->    Tuple:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      int		,   _A      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Union[str, Any]	)							->    Any:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      bool = True		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	models[model]

							snake_case_		:			str											=	self.run_trainer(
							    stage=_A		,   model_name=_A		,   eval_steps=_A		,   num_train_epochs=1		,   distributed=_A		,   fpaa=_A		,   )

							self.do_checks(_A	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      int = 1		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			str											=	self.get_auto_remove_tmp_dir('./xxx'		,   after=_A	)
							snake_case_		:			Tuple											=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_A	)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

							if fpaa:
								args.extend(['--fp16']	)

							# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
							# hence the separate config files
							snake_case_		:			Any											=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
							snake_case_		:			str											=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
							snake_case_		:			int											=	self.get_launcher(_A	)

							snake_case_		:			List[str]											=	launcher + script + args + ds_args
							# keep for quick debug
							# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
							execute_subprocess_async(_A		,   env=self.get_env()	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Optional[Any]=False	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	min(2		,   get_gpu_count()	) if distributed else 1
							return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

