




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					Union[str, Any]          =  {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       """nat"""

						UpperCamelCase      =       {
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}

						def __init__(		self :List[Any]     ,			__snake_case :Tuple=4     ,			__snake_case :int=3     ,			__snake_case :Union[str, Any]=64     ,			__snake_case :Optional[Any]=[3, 4, 6, 5]     ,			__snake_case :Tuple=[2, 4, 8, 16]     ,			__snake_case :Optional[int]=7     ,			__snake_case :Optional[int]=3.0     ,			__snake_case :int=True     ,			__snake_case :Dict=0.0     ,			__snake_case :Tuple=0.0     ,			__snake_case :List[Any]=0.1     ,			__snake_case :Optional[int]="gelu"     ,			__snake_case :Optional[Any]=0.02     ,			__snake_case :Optional[int]=1E-5     ,			__snake_case :List[str]=0.0     ,			__snake_case :List[str]=None     ,			__snake_case :Optional[int]=None     ,			**__snake_case :List[Any]     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

										__magic_name__						:						Any										=patch_size
										__magic_name__						:						Optional[int]										=num_channels
										__magic_name__						:						Tuple										=embed_dim
										__magic_name__						:						List[Any]										=depths
										__magic_name__						:						Union[str, Any]										=len(__snake_case			)
										__magic_name__						:						List[Any]										=num_heads
										__magic_name__						:						int										=kernel_size
										__magic_name__						:						Tuple										=mlp_ratio
										__magic_name__						:						Tuple										=qkv_bias
										__magic_name__						:						Dict										=hidden_dropout_prob
										__magic_name__						:						Dict										=attention_probs_dropout_prob
										__magic_name__						:						Union[str, Any]										=drop_path_rate
										__magic_name__						:						Union[str, Any]										=hidden_act
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Union[str, Any]										=initializer_range
										# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										__magic_name__						:						List[str]										=int(embed_dim * 2 ** (len(__snake_case			) - 1)			)
										__magic_name__						:						List[str]										=layer_scale_init_value
										__magic_name__						:						List[Any]										=["""stem"""] + [f"stage{idx}" for idx in range(1     ,			len(__snake_case			) + 1			)]
										__magic_name__					,			__magic_name__						:						Dict										=get_aligned_output_features_output_indices(
										    out_features=__snake_case     ,			out_indices=__snake_case     ,			stage_names=self.stage_names			)










from sklearn.metrics import matthews_corrcoef

import datasets


UpperCAmelCase_		:					Dict          =  "\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_		:					Any          =  "\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_		:					Dict          =  "\\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      A__    (		self :List[str]			):
										'''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      A__    (		self :Tuple     ,			__snake_case :str     ,			__snake_case :Tuple     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

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




