"""simple docstring"""






from scipy.stats import spearmanr

import datasets


__lowerCAmelCase					: Optional[Any]        ="""
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.
"""

__lowerCAmelCase					: Optional[Any]        ="""
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
"""

__lowerCAmelCase					: Optional[int]        =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 				_A		( datasets.Metric			):

      def        A__ (		self		):







            """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.spearmanr.html"""]	, )







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=False		):







            """simple docstring"""



            lowercase														=			spearmanr(__lowerCAmelCase	, __lowerCAmelCase		)
            if return_pvalue:
                  return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
            else:
                  return {"spearmanr": results[0]}




"""simple docstring"""






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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__lowerCAmelCase					: Tuple        =logging.get_logger(__name__)

__lowerCAmelCase					: Tuple        ={
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     int													=			'blenderbot-small'
      snake_case__    :     Optional[Any]													=			['past_key_values']
      snake_case__    :     Optional[Any]													=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

      def __init__(		self	, __lowerCAmelCase=5_0265	, __lowerCAmelCase=512	, __lowerCAmelCase=8	, __lowerCAmelCase=2048	, __lowerCAmelCase=16	, __lowerCAmelCase=8	, __lowerCAmelCase=2048	, __lowerCAmelCase=16	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=512	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=1	, __lowerCAmelCase=False	, __lowerCAmelCase=0	, __lowerCAmelCase=1	, __lowerCAmelCase=2	, __lowerCAmelCase=2	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			vocab_size
            lowercase														=			max_position_embeddings
            lowercase														=			d_model
            lowercase														=			encoder_ffn_dim
            lowercase														=			encoder_layers
            lowercase														=			encoder_attention_heads
            lowercase														=			decoder_ffn_dim
            lowercase														=			decoder_layers
            lowercase														=			decoder_attention_heads
            lowercase														=			dropout
            lowercase														=			attention_dropout
            lowercase														=			activation_dropout
            lowercase														=			activation_function
            lowercase														=			init_std
            lowercase														=			encoder_layerdrop
            lowercase														=			decoder_layerdrop
            lowercase														=			use_cache
            lowercase														=			encoder_layers
            lowercase														=			scale_embedding  # scale factor will be sqrt(d_model) if True

            super().__init__(
                pad_token_id=__lowerCAmelCase	, bos_token_id=__lowerCAmelCase	, eos_token_id=__lowerCAmelCase	, is_encoder_decoder=__lowerCAmelCase	, decoder_start_token_id=__lowerCAmelCase	, forced_eos_token_id=__lowerCAmelCase	, **__lowerCAmelCase	, )






class 				_A		( lowerCAmelCase			):

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]		)

                  if self.use_past:
                        lowercase														=			{0: """batch"""}
                        lowercase														=			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
                  else:
                        lowercase														=			{0: """batch""", 1: """decoder_sequence"""}
                        lowercase														=			{0: """batch""", 1: """decoder_sequence"""}

                  if self.use_past:
                        self.fill_with_past_key_values_(__lowerCAmelCase	, direction="""inputs"""		)
            elif self.task == "causal-lm":
                  # TODO: figure this case out.
                  lowercase														=			OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]		)
                  if self.use_past:
                        lowercase							,							lowercase														=			self.num_layers
                        for i in range(__lowerCAmelCase		):
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
            else:
                  lowercase														=			OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                          ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                      ]		)

            return common_inputs

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			super().outputs
            else:
                  lowercase														=			super(__lowerCAmelCase	, self		).outputs
                  if self.use_past:
                        lowercase							,							lowercase														=			self.num_layers
                        for i in range(__lowerCAmelCase		):
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
            return common_outputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

            # Generate decoder inputs
            lowercase														=			seq_length if not self.use_past else 1
            lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
            lowercase														=			{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
            lowercase														=			dict(**__lowerCAmelCase	, **__lowerCAmelCase		)

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
                  else:
                        import torch
                  lowercase							,							lowercase														=			common_inputs["""input_ids"""].shape
                  lowercase														=			common_inputs["""decoder_input_ids"""].shape[1]
                  lowercase							,							lowercase														=			self.num_attention_heads
                  lowercase														=			(
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowercase														=			decoder_seq_length + 3
                  lowercase														=			(
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

                  lowercase														=			torch.cat(
                      [common_inputs["""decoder_attention_mask"""], torch.ones(__lowerCAmelCase	, __lowerCAmelCase		)]	, dim=1		)

                  lowercase														=			[]
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  lowercase							,							lowercase														=			self.num_layers
                  lowercase														=			min(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			max(__lowerCAmelCase	, __lowerCAmelCase		) - min_num_layers
                  lowercase														=			"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

                  for _ in range(__lowerCAmelCase		):
                        common_inputs["past_key_values"].append(
                            (
                                torch.zeros(__lowerCAmelCase		),
                                torch.zeros(__lowerCAmelCase		),
                                torch.zeros(__lowerCAmelCase		),
                                torch.zeros(__lowerCAmelCase		),
                            )		)
                  # TODO: test this.
                  lowercase														=			encoder_shape if remaining_side_name == """encoder""" else decoder_shape
                  for _ in range(__lowerCAmelCase	, __lowerCAmelCase		):
                        common_inputs["past_key_values"].append((torch.zeros(__lowerCAmelCase		), torch.zeros(__lowerCAmelCase		))		)
            return common_inputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
                  else:
                        import torch
                  lowercase							,							lowercase														=			common_inputs["""input_ids"""].shape
                  # Not using the same length for past_key_values
                  lowercase														=			seqlen + 2
                  lowercase							,							lowercase														=			self.num_layers
                  lowercase							,							lowercase														=			self.num_attention_heads
                  lowercase														=			(
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

                  lowercase														=			common_inputs["""attention_mask"""].dtype
                  lowercase														=			torch.cat(
                      [common_inputs["""attention_mask"""], torch.ones(__lowerCAmelCase	, __lowerCAmelCase	, dtype=__lowerCAmelCase		)]	, dim=1		)
                  lowercase														=			[
                      (torch.zeros(__lowerCAmelCase		), torch.zeros(__lowerCAmelCase		)) for _ in range(__lowerCAmelCase		)
                  ]
            return common_inputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			compute_effective_axis_dimension(
                __lowerCAmelCase	, fixed_dimension=OnnxConfig.default_fixed_batch	, num_token_to_add=0		)

            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            lowercase														=			tokenizer.num_special_tokens_to_add(__lowerCAmelCase		)
            lowercase														=			compute_effective_axis_dimension(
                __lowerCAmelCase	, fixed_dimension=OnnxConfig.default_fixed_sequence	, num_token_to_add=__lowerCAmelCase		)

            # Generate dummy inputs according to compute batch and sequence
            lowercase														=			[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
            lowercase														=			dict(tokenizer(__lowerCAmelCase	, return_tensors=__lowerCAmelCase		)		)
            return common_inputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                      __lowerCAmelCase	, batch_size=__lowerCAmelCase	, seq_length=__lowerCAmelCase	, is_pair=__lowerCAmelCase	, framework=__lowerCAmelCase		)

            elif self.task == "causal-lm":
                  lowercase														=			self._generate_dummy_inputs_for_causal_lm(
                      __lowerCAmelCase	, batch_size=__lowerCAmelCase	, seq_length=__lowerCAmelCase	, is_pair=__lowerCAmelCase	, framework=__lowerCAmelCase		)
            else:
                  lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                      __lowerCAmelCase	, batch_size=__lowerCAmelCase	, seq_length=__lowerCAmelCase	, is_pair=__lowerCAmelCase	, framework=__lowerCAmelCase		)

            return common_inputs







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			super()._flatten_past_key_values_(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
            else:
                  lowercase														=			super(__lowerCAmelCase	, self		)._flatten_past_key_values_(
                      __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)



