




import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						FunnelTokenizer
     _a							 =						FunnelTokenizerFast
     _a							 =						True
     _a							 =						True

     def 					a__	(  self				)						->							Optional[int]:
           super().setUp()

           _A							:     Tuple			=						[
               """<unk>""",
               """<cls>""",
               """<sep>""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           _A							:     List[Any]			=						os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]				)
           with open(self.vocab_file		,     """w"""		,     encoding="""utf-8"""				) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

     def 					a__	(  self		,     **_a				)						->							Union[str, Any]:
           return FunnelTokenizer.from_pretrained(self.tmpdirname		,     **UpperCAmelCase__				)

     def 					a__	(  self		,     **_a				)						->							int:
           return FunnelTokenizerFast.from_pretrained(self.tmpdirname		,     **UpperCAmelCase__				)

     def 					a__	(  self		,     _a				)						->							int:
           _A							:     Tuple			=						"""UNwant\u00E9d,running"""
           _A							:     Optional[Any]			=						"""unwanted, running"""
           return input_text, output_text

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     Tuple			=						self.tokenizer_class(self.vocab_file				)

           _A							:     Union[str, Any]			=						tokenizer.tokenize("""UNwant\u00E9d,running"""				)
           self.assertListEqual(UpperCAmelCase__		,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]				)
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__				)		,     [7, 4, 5, 10, 8, 9]				)



     def 					a__	(  self				)						->							int:
           _A							:     Union[str, Any]			=						self.get_tokenizers(do_lower_case=UpperCAmelCase__				)
           for tokenizer in tokenizers:
                 _A							:     Union[str, Any]			=						tokenizer("""UNwant\u00E9d,running"""				)
                 _A							:     int			=						len(inputs["""input_ids"""]				) - 1
                 self.assertListEqual(inputs["""token_type_ids"""]		,     [2] + [0] * sentence_len				)

                 _A							:     int			=						tokenizer("""UNwant\u00E9d,running"""		,     """UNwant\u00E9d,running"""				)
                 self.assertListEqual(inputs["""token_type_ids"""]		,     [2] + [0] * sentence_len + [1] * sentence_len				)




"""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]}




