


"""simple docstring"""


from scipy.stats import spearmanr

import datasets


_snake_case          =							"\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"

_snake_case          =							"\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"

_snake_case          =							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   _a      (      datasets.Metric ):
    def      _UpperCamelCase       ( self							:					List[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.spearmanr.html']							,					)


    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Any=False				):
          lowerCamelCase__           =		spearmanr(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          if return_pvalue:
                return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
          else:
                return {"spearmanr": results[0]}







"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_snake_case          =							[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

_snake_case          =							{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
_snake_case          =							{f"""funnel-transformer/{name}""": 512 for name in _model_names}
_snake_case          =							{f"""funnel-transformer/{name}""": {"do_lower_case": True} for name in _model_names}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				List[str]             =    VOCAB_FILES_NAMES
    a_    :				List[str]             =    PRETRAINED_VOCAB_FILES_MAP
    a_    :				Optional[int]             =    PRETRAINED_INIT_CONFIGURATION
    a_    :				List[str]             =    FunnelTokenizer
    a_    :				Dict             =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a_    :				int             =    2
    def __init__( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=None							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					SCREAMING_SNAKE_CASE__							:					int=True							,					SCREAMING_SNAKE_CASE__							:					Any="<unk>"							,					SCREAMING_SNAKE_CASE__							:					List[Any]="<sep>"							,					SCREAMING_SNAKE_CASE__							:					int="<pad>"							,					SCREAMING_SNAKE_CASE__							:					Tuple="<cls>"							,					SCREAMING_SNAKE_CASE__							:					Tuple="<mask>"							,					SCREAMING_SNAKE_CASE__							:					Any="<s>"							,					SCREAMING_SNAKE_CASE__							:					Tuple="</s>"							,					SCREAMING_SNAKE_CASE__							:					str=True							,					SCREAMING_SNAKE_CASE__							:					Any=True							,					SCREAMING_SNAKE_CASE__							:					str=None							,					SCREAMING_SNAKE_CASE__							:					int="##"							,					**SCREAMING_SNAKE_CASE__							:					Any							,					):
          super().__init__(
              SCREAMING_SNAKE_CASE__							,					tokenizer_file=SCREAMING_SNAKE_CASE__							,					do_lower_case=SCREAMING_SNAKE_CASE__							,					unk_token=SCREAMING_SNAKE_CASE__							,					sep_token=SCREAMING_SNAKE_CASE__							,					pad_token=SCREAMING_SNAKE_CASE__							,					cls_token=SCREAMING_SNAKE_CASE__							,					mask_token=SCREAMING_SNAKE_CASE__							,					bos_token=SCREAMING_SNAKE_CASE__							,					eos_token=SCREAMING_SNAKE_CASE__							,					clean_text=SCREAMING_SNAKE_CASE__							,					tokenize_chinese_chars=SCREAMING_SNAKE_CASE__							,					strip_accents=SCREAMING_SNAKE_CASE__							,					wordpieces_prefix=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__							,					)

          lowerCamelCase__           =		json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
          if (
              normalizer_state.get('lowercase'							,					SCREAMING_SNAKE_CASE__				) != do_lower_case
              or normalizer_state.get('strip_accents'							,					SCREAMING_SNAKE_CASE__				) != strip_accents
              or normalizer_state.get('handle_chinese_chars'							,					SCREAMING_SNAKE_CASE__				) != tokenize_chinese_chars
          ):
                lowerCamelCase__           =		getattr(SCREAMING_SNAKE_CASE__							,					normalizer_state.pop('type'				)				)
                lowerCamelCase__           =		do_lower_case
                lowerCamelCase__           =		strip_accents
                lowerCamelCase__           =		tokenize_chinese_chars
                lowerCamelCase__           =		normalizer_class(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		do_lower_case
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=None				):
          lowerCamelCase__           =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

          return output
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					List[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[List[int]] = None				):
          lowerCamelCase__           =		[self.sep_token_id]
          lowerCamelCase__           =		[self.cls_token_id]
          if token_ids_a is None:
                return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0]
          return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = None				):
          lowerCamelCase__           =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE__							,					name=SCREAMING_SNAKE_CASE__				)
          return tuple(SCREAMING_SNAKE_CASE__				)



