




from scipy.stats import pearsonr

import datasets


UpperCamelCase__         =      """
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.
"""


UpperCamelCase__         =      """
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
"""


UpperCamelCase__         =      """
@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     __SCREAMING_SNAKE_CASE(       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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A=False      ):




											"""simple docstring"""



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










from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class       a__   ( nn.Module				):






						def __init__(       self		,      _A		,      _A		,      _A		,      _A=0.0		,      _A = None		,      _A = "geglu"		,      _A = None		,      _A = False		,      _A = False		,      _A = False		,      _A = False		,      _A = True		,      _A = "layer_norm"		,      _A = False		,      ):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase           =	only_cross_attention

											__lowerCAmelCase           =	(num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
											__lowerCAmelCase           =	(num_embeds_ada_norm is not None) and norm_type == "ada_norm"

											if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
																raise ValueError(
																    f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
																    f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""      )

											# Define 3 blocks. Each block has its own normalization layer.
											# 1. Self-Attn
											if self.use_ada_layer_norm:
																__lowerCAmelCase           =	AdaLayerNorm(_A		,      _A      )
											elif self.use_ada_layer_norm_zero:
																__lowerCAmelCase           =	AdaLayerNormZero(_A		,      _A      )
											else:
																__lowerCAmelCase           =	nn.LayerNorm(_A		,      elementwise_affine=_A      )
											__lowerCAmelCase           =	Attention(
											    query_dim=_A		,      heads=_A		,      dim_head=_A		,      dropout=_A		,      bias=_A		,      cross_attention_dim=cross_attention_dim if only_cross_attention else None		,      upcast_attention=_A		,      )

											# 2. Cross-Attn
											if cross_attention_dim is not None or double_self_attention:
																# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
																# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
																# the second cross attention block.
																__lowerCAmelCase           =	(
																    AdaLayerNorm(_A		,      _A      )
																    if self.use_ada_layer_norm
																    else nn.LayerNorm(_A		,      elementwise_affine=_A      )
																)
																__lowerCAmelCase           =	Attention(
																    query_dim=_A		,      cross_attention_dim=cross_attention_dim if not double_self_attention else None		,      heads=_A		,      dim_head=_A		,      dropout=_A		,      bias=_A		,      upcast_attention=_A		,      )  # is self-attn if encoder_hidden_states is none
											else:
																__lowerCAmelCase           =	None
																__lowerCAmelCase           =	None

											# 3. Feed-forward
											__lowerCAmelCase           =	nn.LayerNorm(_A		,      elementwise_affine=_A      )
											__lowerCAmelCase           =	FeedForward(_A		,      dropout=_A		,      activation_fn=_A		,      final_dropout=_A      )

											# let chunk size default to None
											__lowerCAmelCase           =	None
											__lowerCAmelCase           =	0






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	chunk_size
											__lowerCAmelCase           =	dim






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      ):




											"""simple docstring"""



											if self.use_ada_layer_norm:
																__lowerCAmelCase           =	self.norma(_A		,      _A      )
											elif self.use_ada_layer_norm_zero:
																__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	self.norma(
																    _A		,      _A		,      _A		,      hidden_dtype=hidden_states.dtype      )
											else:
																__lowerCAmelCase           =	self.norma(_A      )

											__lowerCAmelCase           =	cross_attention_kwargs if cross_attention_kwargs is not None else {}

											__lowerCAmelCase           =	self.attna(
											    _A		,      encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None		,      attention_mask=_A		,      **_A		,      )
											if self.use_ada_layer_norm_zero:
																__lowerCAmelCase           =	gate_msa.unsqueeze(1      ) * attn_output
											__lowerCAmelCase           =	attn_output + hidden_states

											# 2. Cross-Attention
											if self.attna is not None:
																__lowerCAmelCase           =	(
																    self.norma(_A		,      _A      ) if self.use_ada_layer_norm else self.norma(_A      )
																)

																__lowerCAmelCase           =	self.attna(
																    _A		,      encoder_hidden_states=_A		,      attention_mask=_A		,      **_A		,      )
																__lowerCAmelCase           =	attn_output + hidden_states

											# 3. Feed-forward
											__lowerCAmelCase           =	self.norma(_A      )

											if self.use_ada_layer_norm_zero:
																__lowerCAmelCase           =	norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

											if self._chunk_size is not None:
																# "feed_forward_chunk_size" can be used to save memory
																if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																					raise ValueError(
																					    f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""      )

																__lowerCAmelCase           =	norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
																__lowerCAmelCase           =	torch.cat(
																    [self.ff(_A      ) for hid_slice in norm_hidden_states.chunk(_A		,      dim=self._chunk_dim      )]		,      dim=self._chunk_dim		,      )
											else:
																__lowerCAmelCase           =	self.ff(_A      )

											if self.use_ada_layer_norm_zero:
																__lowerCAmelCase           =	gate_mlp.unsqueeze(1      ) * ff_output

											__lowerCAmelCase           =	ff_output + hidden_states

											return hidden_states






class       a__   ( nn.Module				):






						def __init__(       self		,      _A		,      _A = None		,      _A = 4		,      _A = 0.0		,      _A = "geglu"		,      _A = False		,      ):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase           =	int(dim * mult      )
											__lowerCAmelCase           =	dim_out if dim_out is not None else dim

											if activation_fn == "gelu":
																__lowerCAmelCase           =	GELU(_A		,      _A      )
											if activation_fn == "gelu-approximate":
																__lowerCAmelCase           =	GELU(_A		,      _A		,      approximate="tanh"      )
											elif activation_fn == "geglu":
																__lowerCAmelCase           =	GEGLU(_A		,      _A      )
											elif activation_fn == "geglu-approximate":
																__lowerCAmelCase           =	ApproximateGELU(_A		,      _A      )

											__lowerCAmelCase           =	nn.ModuleList([]      )
											# project in
											self.net.append(_A      )
											# project dropout
											self.net.append(nn.Dropout(_A      )      )
											# project out
											self.net.append(nn.Linear(_A		,      _A      )      )
											# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
											if final_dropout:
																self.net.append(nn.Dropout(_A      )      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											for module in self.net:
																__lowerCAmelCase           =	module(_A      )
											return hidden_states






class       a__   ( nn.Module				):






						def __init__(       self		,      _A		,      _A		,      _A = "none"      ):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase           =	nn.Linear(_A		,      _A      )
											__lowerCAmelCase           =	approximate






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											if gate.device.type != "mps":
																return F.gelu(_A		,      approximate=self.approximate      )
											# mps: gelu is not implemented for float16
											return F.gelu(gate.to(dtype=torch.floataa      )		,      approximate=self.approximate      ).to(dtype=gate.dtype      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.proj(_A      )
											__lowerCAmelCase           =	self.gelu(_A      )
											return hidden_states






class       a__   ( nn.Module				):






						def __init__(       self		,      _A		,      _A      ):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase           =	nn.Linear(_A		,      dim_out * 2      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											if gate.device.type != "mps":
																return F.gelu(_A      )
											# mps: gelu is not implemented for float16
											return F.gelu(gate.to(dtype=torch.floataa      )      ).to(dtype=gate.dtype      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.proj(_A      ).chunk(2		,      dim=-1      )
											return hidden_states * self.gelu(_A      )






class       a__   ( nn.Module				):






						def __init__(       self		,      _A		,      _A      ):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase           =	nn.Linear(_A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.proj(_A      )
											return x * torch.sigmoid(1.7_02 * x      )






class       a__   ( nn.Module				):






						def __init__(       self		,      _A		,      _A      ):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase           =	nn.Embedding(_A		,      _A      )
											__lowerCAmelCase           =	nn.SiLU()
											__lowerCAmelCase           =	nn.Linear(_A		,      embedding_dim * 2      )
											__lowerCAmelCase           =	nn.LayerNorm(_A		,      elementwise_affine=_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.linear(self.silu(self.emb(_A      )      )      )
											__lowerCAmelCase       ,							__lowerCAmelCase           =	torch.chunk(_A		,      2      )
											__lowerCAmelCase           =	self.norm(_A      ) * (1 + scale) + shift
											return x






class       a__   ( nn.Module				):






						def __init__(       self		,      _A		,      _A      ):




											"""simple docstring"""



											super().__init__()

											__lowerCAmelCase           =	CombinedTimestepLabelEmbeddings(_A		,      _A      )

											__lowerCAmelCase           =	nn.SiLU()
											__lowerCAmelCase           =	nn.Linear(_A		,      6 * embedding_dim		,      bias=_A      )
											__lowerCAmelCase           =	nn.LayerNorm(_A		,      elementwise_affine=_A		,      eps=1E-6      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A=None      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.linear(self.silu(self.emb(_A		,      _A		,      hidden_dtype=_A      )      )      )
											__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	emb.chunk(6		,      dim=1      )
											__lowerCAmelCase           =	self.norm(_A      ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
											return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class       a__   ( nn.Module				):






						def __init__(       self		,      _A		,      _A		,      _A		,      _A = None		,      _A = 1E-5      ):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase           =	num_groups
											__lowerCAmelCase           =	eps

											if act_fn is None:
																__lowerCAmelCase           =	None
											else:
																__lowerCAmelCase           =	get_activation(_A      )

											__lowerCAmelCase           =	nn.Linear(_A		,      out_dim * 2      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											if self.act:
																__lowerCAmelCase           =	self.act(_A      )
											__lowerCAmelCase           =	self.linear(_A      )
											__lowerCAmelCase           =	emb[:, :, None, None]
											__lowerCAmelCase       ,							__lowerCAmelCase           =	emb.chunk(2		,      dim=1      )

											__lowerCAmelCase           =	F.group_norm(_A		,      self.num_groups		,      eps=self.eps      )
											__lowerCAmelCase           =	x * (1 + scale) + shift
											return x




