



from scipy.stats import spearmanr

import datasets


__UpperCAmelCase          =       '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

__UpperCAmelCase          =       '''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

__UpperCAmelCase          =       r'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@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, {\.I}lhan 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, Ant{\^o}nio 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 				lowerCAmelCase_		(  datasets.Metric							):
       def 					snake_case_       (			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 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=False       )     ->		Any:
          UpperCamelCase    :  int							=	spearmanr(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          if return_pvalue:
             return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
          else:
             return {"spearmanr": results[0]}








from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)


__UpperCAmelCase          =       {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					List[str]        =  "codegen"
       UpperCAmelCase__				:					str        =  {
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=5_0400,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4096,			SCREAMING_SNAKE_CASE_=28,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_="gelu_new",			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=False,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  Tuple							=	vocab_size
          UpperCamelCase    :  Optional[int]							=	n_ctx
          UpperCamelCase    :  Optional[int]							=	n_positions
          UpperCamelCase    :  List[str]							=	n_embd
          UpperCamelCase    :  Dict							=	n_layer
          UpperCamelCase    :  int							=	n_head
          UpperCamelCase    :  Union[str, Any]							=	n_inner
          UpperCamelCase    :  int							=	rotary_dim
          UpperCamelCase    :  Optional[Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	resid_pdrop
          UpperCamelCase    :  Union[str, Any]							=	embd_pdrop
          UpperCamelCase    :  Optional[Any]							=	attn_pdrop
          UpperCamelCase    :  List[str]							=	layer_norm_epsilon
          UpperCamelCase    :  Union[str, Any]							=	initializer_range
          UpperCamelCase    :  str							=	use_cache

          UpperCamelCase    :  Dict							=	bos_token_id
          UpperCamelCase    :  Union[str, Any]							=	eos_token_id

          super().__init__(
              bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			tie_word_embeddings=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )






class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = "default",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			)     ->		List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_,			task=SCREAMING_SNAKE_CASE_,			patching_specs=SCREAMING_SNAKE_CASE_,			use_past=SCREAMING_SNAKE_CASE_       )
          if not getattr(self._config,			'pad_token_id',			SCREAMING_SNAKE_CASE_       ):
             # TODO: how to do that better?
             UpperCamelCase    :  str							=	0
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  Tuple							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs'       )
             UpperCamelCase    :  List[Any]							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  Optional[int]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_layer
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_head
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Mapping[str, Any]:
          UpperCamelCase    :  Tuple							=	super(SCREAMING_SNAKE_CASE_,			self       ).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,			batch_size=SCREAMING_SNAKE_CASE_,			seq_length=SCREAMING_SNAKE_CASE_,			is_pair=SCREAMING_SNAKE_CASE_,			framework=SCREAMING_SNAKE_CASE_       )

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase    :  Optional[int]							=	OrderedDict({'input_ids': common_inputs['input_ids']}       )

          # Need to add the past_keys
          if self.use_past:
             if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'       )
             else:
                import torch

                UpperCamelCase	,     UpperCamelCase    :  List[str]							=	common_inputs['input_ids'].shape
                # Not using the same length for past_key_values
                UpperCamelCase    :  List[Any]							=	seqlen + 2
                UpperCamelCase    :  List[str]							=	(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                UpperCamelCase    :  str							=	[
                    (torch.zeros(SCREAMING_SNAKE_CASE_       ), torch.zeros(SCREAMING_SNAKE_CASE_       )) for _ in range(self.num_layers       )
                ]

          UpperCamelCase    :  List[Any]							=	common_inputs['attention_mask']
          if self.use_past:
             UpperCamelCase    :  Optional[Any]							=	ordered_inputs['attention_mask'].dtype
             UpperCamelCase    :  List[Any]							=	torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )],			dim=1       )

          return ordered_inputs






       @property
       def 					snake_case_       (			self       )     ->		int:
          return 13



