





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   params

       UpperCamelCase	:					Union[str, Any]       	=   np.array(A_   )
       UpperCamelCase	:					Optional[int]       	=   np.array([len(A_   ) for t in data]   )

       self.check()
       self.remove_long_sequences()
       self.remove_empty_sequences()
       self.remove_unknown_sequences()
       self.check()
       self.print_statistics()





      def __getitem__( self       ,							A_   ):
       '''simple docstring'''




       return (self.token_ids[index], self.lengths[index])





      def __len__( self   ):
       '''simple docstring'''




       return len(self.lengths   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       assert len(self.token_ids   ) == len(self.lengths   )
       assert all(self.lengths[i] == len(self.token_ids[i]   ) for i in range(len(self.lengths   )   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   self.params.max_model_input_size
       UpperCamelCase	:					Dict       	=   self.lengths > max_len
       logger.info(F"""Splitting {sum(A_   )} too long sequences."""   )

       def divide_chunks(A_       ,							A_   ):
        return [l[i : i + n] for i in range(0       ,							len(A_   )       ,							A_   )]

       UpperCamelCase	:					List[str]       	=   []
       UpperCamelCase	:					Tuple       	=   []
       if self.params.mlm:
        UpperCamelCase	,    UpperCamelCase	:					Dict       	=   self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
       else:
        UpperCamelCase	,    UpperCamelCase	:					Dict       	=   self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

       for seq_, len_ in zip(self.token_ids       ,							self.lengths   ):
        assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
        if len_ <= max_len:
         new_tok_ids.append(seq_   )
         new_lengths.append(len_   )
        else:
         UpperCamelCase	:					Dict       	=   []
         for sub_s in divide_chunks(seq_       ,							max_len - 2   ):
          if sub_s[0] != cls_id:
           UpperCamelCase	:					Union[str, Any]       	=   np.insert(A_       ,							0       ,							A_   )
          if sub_s[-1] != sep_id:
           UpperCamelCase	:					Optional[int]       	=   np.insert(A_       ,							len(A_   )       ,							A_   )
          assert len(A_   ) <= max_len
          assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
          sub_seqs.append(A_   )

         new_tok_ids.extend(A_   )
         new_lengths.extend([len(A_   ) for l in sub_seqs]   )

       UpperCamelCase	:					Union[str, Any]       	=   np.array(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   np.array(A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   len(self   )
       UpperCamelCase	:					Dict       	=   self.lengths > 11
       UpperCamelCase	:					List[str]       	=   self.token_ids[indices]
       UpperCamelCase	:					Tuple       	=   self.lengths[indices]
       UpperCamelCase	:					Optional[Any]       	=   len(self   )
       logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if "unk_token" not in self.params.special_tok_ids:
        return
       else:
        UpperCamelCase	:					List[str]       	=   self.params.special_tok_ids["unk_token"]
       UpperCamelCase	:					int       	=   len(self   )
       UpperCamelCase	:					str       	=   np.array([np.count_nonzero(a == unk_token_id   ) for a in self.token_ids]   )
       UpperCamelCase	:					List[Any]       	=   (unk_occs / self.lengths) < 0.5
       UpperCamelCase	:					List[Any]       	=   self.token_ids[indices]
       UpperCamelCase	:					str       	=   self.lengths[indices]
       UpperCamelCase	:					Dict       	=   len(self   )
       logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if not self.params.is_master:
        return
       logger.info(F"""{len(self   )} sequences"""   )
       # data_len = sum(self.lengths)
       # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
       # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

       # unk_idx = self.params.special_tok_ids['unk_token']
       # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
       # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   [t[0] for t in batch]
       UpperCamelCase	:					str       	=   [t[1] for t in batch]
       assert len(A_   ) == len(A_   )

       # Max for paddings
       UpperCamelCase	:					Union[str, Any]       	=   max(A_   )

       # Pad token ids
       if self.params.mlm:
        UpperCamelCase	:					List[Any]       	=   self.params.special_tok_ids["pad_token"]
       else:
        UpperCamelCase	:					Dict       	=   self.params.special_tok_ids["unk_token"]
       UpperCamelCase	:					Optional[int]       	=   [list(t.astype(A_   )   ) + [pad_idx] * (max_seq_len_ - len(A_   )) for t in token_ids]
       assert len(tk_   ) == len(A_   )
       assert all(len(A_   ) == max_seq_len_ for t in tk_   )

       UpperCamelCase	:					Any       	=   torch.tensor(tk_   )  # (bs, max_seq_len_)
       UpperCamelCase	:					Any       	=   torch.tensor(A_   )  # (bs)
       return tk_t, lg_t


'''simple docstring'''




from scipy.stats import pearsonr

import datasets


lowercase			:				Optional[int]    		=			'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe 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.\nThe 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.\n'


lowercase			:				Optional[Any]    		=			'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    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.\n    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.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


lowercase			:				str    		=			'\n@article{2020SciPy-NMeth,\nauthor  = {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, Ilhan 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, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'





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





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False	) ->				Union[str, Any]:






         """simple docstring"""






         if return_pvalue:
             A       :   Union[str, Any]					=						pearsonr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
             return {"pearsonr": results[0], "p-value": results[1]}
         else:
             return {"pearsonr": float(pearsonr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)[0]	)}




