




'''simple docstring'''

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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


a  :				Tuple									=  logging.get_logger(__name__)

a  :				List[str]									=  {
    'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"perceiver"


				def __init__(		self    :					Dict       ,				lowercase_    :					str=256       ,				lowercase_    :					List[Any]=1280       ,				lowercase_    :					Dict=768       ,				lowercase_    :					str=1       ,				lowercase_    :					Optional[int]=26       ,				lowercase_    :					Any=8       ,				lowercase_    :					Tuple=8       ,				lowercase_    :					int=None       ,				lowercase_    :					List[str]=None       ,				lowercase_    :					Any="kv"       ,				lowercase_    :					str=1       ,				lowercase_    :					int=1       ,				lowercase_    :					List[str]="gelu"       ,				lowercase_    :					List[str]=0.1       ,				lowercase_    :					Union[str, Any]=0.02       ,				lowercase_    :					Optional[Any]=1e-12       ,				lowercase_    :					Any=True       ,				lowercase_    :					List[Any]=262       ,				lowercase_    :					List[str]=2048       ,				lowercase_    :					str=56       ,				lowercase_    :					int=[368, 496]       ,				lowercase_    :					Any=16       ,				lowercase_    :					Optional[int]=1920       ,				lowercase_    :					Optional[int]=16       ,				lowercase_    :					Union[str, Any]=[1, 16, 224, 224]       ,				**lowercase_    :					List[str]       ,				):
									super().__init__(**lowercase_					)

									snake_case_									=				num_latents
									snake_case_									=				d_latents
									snake_case_									=				d_model
									snake_case_									=				num_blocks
									snake_case_									=				num_self_attends_per_block
									snake_case_									=				num_self_attention_heads
									snake_case_									=				num_cross_attention_heads
									snake_case_									=				qk_channels
									snake_case_									=				v_channels
									snake_case_									=				cross_attention_shape_for_attention
									snake_case_									=				self_attention_widening_factor
									snake_case_									=				cross_attention_widening_factor
									snake_case_									=				hidden_act
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				initializer_range
									snake_case_									=				layer_norm_eps
									snake_case_									=				use_query_residual
									# masked language modeling attributes
									snake_case_									=				vocab_size
									snake_case_									=				max_position_embeddings
									# image classification attributes
									snake_case_									=				image_size
									# flow attributes
									snake_case_									=				train_size
									# multimodal autoencoding attributes
									snake_case_									=				num_frames
									snake_case_									=				audio_samples_per_frame
									snake_case_									=				samples_per_patch
									snake_case_									=				output_shape







class 						a				(   _lowerCamelCase     ):


				@property
				def   A_ (		self    :					Any					):
									if self.task == "multiple-choice":
														snake_case_									=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
														snake_case_									=				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''inputs''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ]					)


				@property
				def   A_ (		self    :					Dict					):
									return 1e-4


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional[TensorType] = None       ,				lowercase_    :					int = 3       ,				lowercase_    :					int = 40       ,				lowercase_    :					int = 40       ,				):
									# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

									if isinstance(lowercase_       ,				lowercase_					):
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														snake_case_									=				compute_effective_axis_dimension(
														    lowercase_       ,				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
														snake_case_									=				preprocessor.num_special_tokens_to_add(lowercase_					)
														snake_case_									=				compute_effective_axis_dimension(
														    lowercase_       ,				fixed_dimension=OnnxConfig.default_fixed_sequence       ,				num_token_to_add=lowercase_					)
														# Generate dummy inputs according to compute batch and sequence
														snake_case_									=				[''' '''.join(['''a''']					) * seq_length] * batch_size
														snake_case_									=				dict(preprocessor(lowercase_       ,				return_tensors=lowercase_					)					)
														snake_case_									=				inputs.pop('''input_ids'''					)
														return inputs
									elif isinstance(lowercase_       ,				lowercase_					) and preprocessor.model_input_names[0] == "pixel_values":
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														snake_case_									=				compute_effective_axis_dimension(lowercase_       ,				fixed_dimension=OnnxConfig.default_fixed_batch					)
														snake_case_									=				self._generate_dummy_images(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)
														snake_case_									=				dict(preprocessor(images=lowercase_       ,				return_tensors=lowercase_					)					)
														snake_case_									=				inputs.pop('''pixel_values'''					)
														return inputs
									else:
														raise ValueError(
														    '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'''					)


"""simple docstring"""






from scipy.stats import pearsonr

import datasets


__lowerCAmelCase    :       List[Any]					     ="""
Pearson correlation coefficient and p-value for testing non-correlation.
The 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.
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.
"""


__lowerCAmelCase    :       Optional[int]					     ="""
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    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.
    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.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


__lowerCAmelCase    :       str					     ="""
@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, Ilhan 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, Antonio 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.pearsonr.html"""]		,       )







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







            """simple docstring"""



            if return_pvalue:
                  lowercase														=	pearsonr(__lowerCAmelCase		,       __lowerCAmelCase						)
                  return {"pearsonr": results[0], "p-value": results[1]}
            else:
                  return {"pearsonr": float(pearsonr(__lowerCAmelCase		,       __lowerCAmelCase						)[0]						)}



