



"""simple docstring"""
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class       snake_case_    (    nn.Module					):






							def __init__( self      ,  a_ = 1_6      ,  a_ = 8_8      ,  a_ = None      ,  a_ = 1      ,  a_ = 0.0      ,  a_ = 3_2      ,  a_ = None      ,  a_ = False      ,  a_ = None      ,  a_ = None      ,  a_ = "geglu"      ,  a_ = None      ,  ):
									super().__init__()
									a_  :   List[Any]			    =  nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=a_      ,  attention_head_dim=a_      ,  in_channels=a_      ,  num_layers=a_      ,  dropout=a_      ,  norm_num_groups=a_      ,  cross_attention_dim=a_      ,  attention_bias=a_      ,  sample_size=a_      ,  num_vector_embeds=a_      ,  activation_fn=a_      ,  num_embeds_ada_norm=a_      ,  )
									        for _ in range(2   )
									    ]   )

									# Variables that can be set by a pipeline:

									# The ratio of transformer1 to transformer2's output states to be combined during inference
									a_  :   List[Any]			    =  0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									a_  :   Any			    =  [7_7, 2_5_7]

									# Which transformer to use to encode which condition.
									# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
									a_  :   int			    =  [1, 0]






							def snake_case_	( self      ,  a_      ,  a_      ,  a_=None      ,  a_=None      ,  a_=None      ,  a_ = True      ,  ):
									a_  :   Optional[int]			    =  hidden_states

									a_  :   int			    =  []
									a_  :   int			    =  0
									# attention_mask is not used yet
									for i in range(2   ):
											# for each of the two transformers, pass the corresponding condition tokens
											a_  :   Tuple			    =  encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
											a_  :   Tuple			    =  self.transformer_index_for_condition[i]
											a_  :   Optional[Any]			    =  self.transformers[transformer_index](
											    a_      ,  encoder_hidden_states=a_      ,  timestep=a_      ,  cross_attention_kwargs=a_      ,  return_dict=a_      ,  )[0]
											encoded_states.append(encoded_state - input_states   )
											tokens_start += self.condition_lengths[i]

									a_  :   List[Any]			    =  encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									a_  :   List[str]			    =  output_states + input_states

									if not return_dict:
											return (output_states,)

									return TransformeraDModelOutput(sample=a_   )






'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCamelCase					    =	"\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

__UpperCamelCase					    =	"\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

__UpperCamelCase					    =	"\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION		)
class  _A (					datasets.Metric		):

		def 	lowercase__     (   self					:   Tuple			)					->    Dict:


					"""simple docstring"""

					return datasets.MetricInfo(
					    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
					        {
					            """predictions""": datasets.Value("""int32"""			),
					            """references""": datasets.Value("""int32"""			),
					        }			)  ,      reference_urls=[
					        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
					    ]  ,      )

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Union[str, Any]=None			)					->    Optional[int]:


					"""simple docstring"""

					return {
					    "matthews_correlation": float(matthews_corrcoef(__magic_name__  ,      __magic_name__  ,      sample_weight=__magic_name__			)			),
					}


