



from scipy.stats import pearsonr

import datasets


lowercase_						=       "\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_						=       "\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_						=       "\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 			_snake_case     (   datasets.Metric):




				def 							A__ (  self   :  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.pearsonr.html"],   )




				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  Tuple,   __lowercase   :  Optional[int],   __lowercase   :  Tuple=False  ):







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









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


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """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 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Any       						="""perceiver"""




				def __init__(  self   :  Any,   __lowercase   :  Optional[Any]=256,   __lowercase   :  List[str]=1280,   __lowercase   :  Dict=768,   __lowercase   :  int=1,   __lowercase   :  Dict=26,   __lowercase   :  Any=8,   __lowercase   :  List[Any]=8,   __lowercase   :  Dict=None,   __lowercase   :  List[Any]=None,   __lowercase   :  str="kv",   __lowercase   :  str=1,   __lowercase   :  Optional[Any]=1,   __lowercase   :  str="gelu",   __lowercase   :  List[str]=0.1,   __lowercase   :  int=0.02,   __lowercase   :  Union[str, Any]=1e-1_2,   __lowercase   :  Optional[Any]=True,   __lowercase   :  Optional[Any]=262,   __lowercase   :  str=2048,   __lowercase   :  Optional[Any]=56,   __lowercase   :  str=[368, 496],   __lowercase   :  str=16,   __lowercase   :  int=1920,   __lowercase   :  Dict=16,   __lowercase   :  List[Any]=[1, 16, 224, 224],   **__lowercase   :  str,   ):
							super().__init__(**__lowercase  )

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




				@property
				def 							A__ (  self   :  Optional[int]  ):
							if self.task == "multiple-choice":
										lowercase__   					=			{0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowercase__   					=			{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("inputs", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]  )




				@property
				def 							A__ (  self   :  Optional[Any]  ):
							return 1e-4




				def 							A__ (  self   :  Tuple,   __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
										lowercase__   					=			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
										lowercase__   					=			preprocessor.num_special_tokens_to_add(__lowercase  )
										lowercase__   					=			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
										lowercase__   					=			[" ".join(["a"]  ) * seq_length] * batch_size
										lowercase__   					=			dict(preprocessor(__lowercase,   return_tensors=__lowercase  )  )
										lowercase__   					=			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
										lowercase__   					=			compute_effective_axis_dimension(__lowercase,   fixed_dimension=OnnxConfig.default_fixed_batch  )
										lowercase__   					=			self._generate_dummy_images(__lowercase,   __lowercase,   __lowercase,   __lowercase  )
										lowercase__   					=			dict(preprocessor(images=__lowercase,   return_tensors=__lowercase  )  )
										lowercase__   					=			inputs.pop("pixel_values"  )
										return inputs
							else:
										raise ValueError(
										    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."  )




