





from scipy.stats import spearmanr

import datasets


_lowercase		:  Optional[Any]			       ="""
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

_lowercase		:  List[str]			       ="""
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {'spearmanr': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

_lowercase		:  Optional[int]			       =r"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@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, {\.I}lhan 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, Ant{\^o}nio 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 						UpperCamelCase_  (  datasets.Metric							):



					def   __a     (		self     :     Optional[Any]						):
							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.spearmanr.html']       ,      )





					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     Optional[Any]       ,      lowerCamelCase     :     Union[str, Any]=False						):
							lowerCamelCase_	:     Union[str, Any] 						=      spearmanr(a__       ,      a__						)
							if return_pvalue:
									return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
							else:
									return {"spearmanr": results[0]}








from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowerCamelCase__       		=  logging.get_logger(__name__)

# General docstring
lowerCamelCase__       		=  """ResNetConfig"""

# Base docstring
lowerCamelCase__       		=  """microsoft/resnet-50"""
lowerCamelCase__       		=  [1, 2048, 7, 7]

# Image classification docstring
lowerCamelCase__       		=  """microsoft/resnet-50"""
lowerCamelCase__       		=  """tiger cat"""

lowerCamelCase__       		=  [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__ = 3      ,    a__ = 1      ,    a__ = "relu"     )			->   Optional[Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Dict 							=    nn.Convad(
                a__      ,    a__      ,    kernel_size=a__      ,    stride=a__      ,    padding=kernel_size // 2      ,    bias=a__     )
            __snake_case      :str 							=    nn.BatchNormad(a__     )
            __snake_case      :List[Any] 							=    ACTaFN[activation] if activation is not None else nn.Identity()



       def        __lowercase    (				self      ,    a__     )			->   Tensor:






            '''simple docstring'''





            __snake_case      :int 							=    self.convolution(a__     )
            __snake_case      :Any 							=    self.normalization(a__     )
            __snake_case      :Optional[int] 							=    self.activation(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Dict 							=    ResNetConvLayer(
                config.num_channels      ,    config.embedding_size      ,    kernel_size=7      ,    stride=2      ,    activation=config.hidden_act     )
            __snake_case      :List[Any] 							=    nn.MaxPoolad(kernel_size=3      ,    stride=2      ,    padding=1     )
            __snake_case      :Tuple 							=    config.num_channels



       def        __lowercase    (				self      ,    a__     )			->   Tensor:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    pixel_values.shape[1]
            if num_channels != self.num_channels:
                 raise ValueError(
                     """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""     )
            __snake_case      :Optional[int] 							=    self.embedder(a__     )
            __snake_case      :int 							=    self.pooler(a__     )
            return embedding




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__ = 2     )			->   Optional[Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Optional[Any] 							=    nn.Convad(a__      ,    a__      ,    kernel_size=1      ,    stride=a__      ,    bias=a__     )
            __snake_case      :Tuple 							=    nn.BatchNormad(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Tensor:






            '''simple docstring'''





            __snake_case      :Any 							=    self.convolution(a__     )
            __snake_case      :str 							=    self.normalization(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = "relu"     )			->   List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :int 							=    in_channels != out_channels or stride != 1
            __snake_case      :Tuple 							=    (
                ResNetShortCut(a__      ,    a__      ,    stride=a__     ) if should_apply_shortcut else nn.Identity()
            )
            __snake_case      :Optional[int] 							=    nn.Sequential(
                ResNetConvLayer(a__      ,    a__      ,    stride=a__     )      ,    ResNetConvLayer(a__      ,    a__      ,    activation=a__     )      ,    )
            __snake_case      :Union[str, Any] 							=    ACTaFN[activation]



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :int 							=    hidden_state
            __snake_case      :Dict 							=    self.layer(a__     )
            __snake_case      :Any 							=    self.shortcut(a__     )
            hidden_state += residual
            __snake_case      :List[Any] 							=    self.activation(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = "relu"      ,    a__ = 4     )			->   List[Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Optional[int] 							=    in_channels != out_channels or stride != 1
            __snake_case      :List[Any] 							=    out_channels // reduction
            __snake_case      :List[str] 							=    (
                ResNetShortCut(a__      ,    a__      ,    stride=a__     ) if should_apply_shortcut else nn.Identity()
            )
            __snake_case      :int 							=    nn.Sequential(
                ResNetConvLayer(a__      ,    a__      ,    kernel_size=1     )      ,    ResNetConvLayer(a__      ,    a__      ,    stride=a__     )      ,    ResNetConvLayer(a__      ,    a__      ,    kernel_size=1      ,    activation=a__     )      ,    )
            __snake_case      :Dict 							=    ACTaFN[activation]



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            __snake_case      :List[str] 							=    hidden_state
            __snake_case      :List[Any] 							=    self.layer(a__     )
            __snake_case      :List[Any] 							=    self.shortcut(a__     )
            hidden_state += residual
            __snake_case      :Optional[Any] 							=    self.activation(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__      ,    a__ = 2      ,    a__ = 2      ,    )			->   Any:






            '''simple docstring'''





            super().__init__()

            __snake_case      :Optional[int] 							=    ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

            __snake_case      :Tuple 							=    nn.Sequential(
                # downsampling is done in the first layer with stride of 2
                layer(a__      ,    a__      ,    stride=a__      ,    activation=config.hidden_act     )      ,    *[layer(a__      ,    a__      ,    activation=config.hidden_act     ) for _ in range(depth - 1     )]      ,    )



       def        __lowercase    (				self      ,    a__     )			->   Tensor:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    input
            for layer in self.layers:
                 __snake_case      :str 							=    layer(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Optional[int] 							=    nn.ModuleList([]     )
            # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
            self.stages.append(
                ResNetStage(
                    a__      ,    config.embedding_size      ,    config.hidden_sizes[0]      ,    stride=2 if config.downsample_in_first_stage else 1      ,    depth=config.depths[0]      ,    )     )
            __snake_case      :Tuple 							=    zip(config.hidden_sizes      ,    config.hidden_sizes[1:]     )
            for (in_channels, out_channels), depth in zip(a__      ,    config.depths[1:]     ):
                 self.stages.append(ResNetStage(a__      ,    a__      ,    a__      ,    depth=a__     )     )



       def        __lowercase    (				self      ,    a__      ,    a__ = False      ,    a__ = True     )			->   BaseModelOutputWithNoAttention:






            '''simple docstring'''





            __snake_case      :Dict 							=    () if output_hidden_states else None

            for stage_module in self.stages:
                 if output_hidden_states:
                      __snake_case      :Optional[int] 							=    hidden_states + (hidden_state,)

                 __snake_case      :Tuple 							=    stage_module(a__     )

            if output_hidden_states:
                 __snake_case      :Optional[Any] 							=    hidden_states + (hidden_state,)

            if not return_dict:
                 return tuple(v for v in [hidden_state, hidden_states] if v is not None     )

            return BaseModelOutputWithNoAttention(
                last_hidden_state=a__      ,    hidden_states=a__      ,    )




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			ResNetConfig
       lowerCamelCase				:					Optional[Any]								=			"resnet"
       lowerCamelCase				:					str								=			"pixel_values"
       lowerCamelCase				:					Optional[int]								=			True



       def        __lowercase    (				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            if isinstance(a__      ,    nn.Convad     ):
                 nn.init.kaiming_normal_(module.weight      ,    mode="""fan_out"""      ,    nonlinearity="""relu"""     )
            elif isinstance(a__      ,    (nn.BatchNormad, nn.GroupNorm)     ):
                 nn.init.constant_(module.weight      ,    1     )
                 nn.init.constant_(module.bias      ,    0     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Optional[int]:






            '''simple docstring'''





            if isinstance(a__      ,    a__     ):
                 __snake_case      :Union[str, Any] 							=    value
lowerCamelCase__       		=  R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCamelCase__       		=  R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""




@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."						,     lowercase_						,     )
class       snake_case__ (				lowercase_):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   Tuple:






            '''simple docstring'''





            super().__init__(a__     )
            __snake_case      :int 							=    config
            __snake_case      :Any 							=    ResNetEmbeddings(a__     )
            __snake_case      :Dict 							=    ResNetEncoder(a__     )
            __snake_case      :Dict 							=    nn.AdaptiveAvgPoolad((1, 1)     )
            # Initialize weights and apply final processing
            self.post_init()



       @add_start_docstrings_to_model_forward(a__     )
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC      ,    output_type=a__      ,    config_class=_CONFIG_FOR_DOC      ,    modality="""vision"""      ,    expected_output=_EXPECTED_OUTPUT_SHAPE      ,    )
       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   BaseModelOutputWithPoolingAndNoAttention:






            '''simple docstring'''





            __snake_case      :List[str] 							=    (
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            __snake_case      :List[str] 							=    return_dict if return_dict is not None else self.config.use_return_dict

            __snake_case      :int 							=    self.embedder(a__     )

            __snake_case      :Any 							=    self.encoder(
                a__      ,    output_hidden_states=a__      ,    return_dict=a__     )

            __snake_case      :Any 							=    encoder_outputs[0]

            __snake_case      :int 							=    self.pooler(a__     )

            if not return_dict:
                 return (last_hidden_state, pooled_output) + encoder_outputs[1:]

            return BaseModelOutputWithPoolingAndNoAttention(
                last_hidden_state=a__      ,    pooler_output=a__      ,    hidden_states=encoder_outputs.hidden_states      ,    )




@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "						,     lowercase_						,     )
class       snake_case__ (				lowercase_):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   List[Any]:






            '''simple docstring'''





            super().__init__(a__     )
            __snake_case      :Union[str, Any] 							=    config.num_labels
            __snake_case      :Optional[int] 							=    ResNetModel(a__     )
            # classification head
            __snake_case      :List[str] 							=    nn.Sequential(
                nn.Flatten()      ,    nn.Linear(config.hidden_sizes[-1]      ,    config.num_labels     ) if config.num_labels > 0 else nn.Identity()      ,    )
            # initialize weights and apply final processing
            self.post_init()



       @add_start_docstrings_to_model_forward(a__     )
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT      ,    output_type=a__      ,    config_class=_CONFIG_FOR_DOC      ,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,    )
       def        __lowercase    (				self      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    )			->   ImageClassifierOutputWithNoAttention:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    return_dict if return_dict is not None else self.config.use_return_dict

            __snake_case      :Tuple 							=    self.resnet(a__      ,    output_hidden_states=a__      ,    return_dict=a__     )

            __snake_case      :Optional[Any] 							=    outputs.pooler_output if return_dict else outputs[1]

            __snake_case      :Optional[Any] 							=    self.classifier(a__     )

            __snake_case      :Any 							=    None

            if labels is not None:
                 if self.config.problem_type is None:
                      if self.num_labels == 1:
                           __snake_case      :List[Any] 							=    """regression"""
                      elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                           __snake_case      :List[Any] 							=    """single_label_classification"""
                      else:
                           __snake_case      :Union[str, Any] 							=    """multi_label_classification"""
                 if self.config.problem_type == "regression":
                      __snake_case      :Any 							=    MSELoss()
                      if self.num_labels == 1:
                           __snake_case      :Optional[Any] 							=    loss_fct(logits.squeeze()      ,    labels.squeeze()     )
                      else:
                           __snake_case      :Any 							=    loss_fct(a__      ,    a__     )
                 elif self.config.problem_type == "single_label_classification":
                      __snake_case      :int 							=    CrossEntropyLoss()
                      __snake_case      :List[Any] 							=    loss_fct(logits.view(-1      ,    self.num_labels     )      ,    labels.view(-1     )     )
                 elif self.config.problem_type == "multi_label_classification":
                      __snake_case      :List[str] 							=    BCEWithLogitsLoss()
                      __snake_case      :int 							=    loss_fct(a__      ,    a__     )

            if not return_dict:
                 __snake_case      :int 							=    (logits,) + outputs[2:]
                 return (loss,) + output if loss is not None else output

            return ImageClassifierOutputWithNoAttention(loss=a__      ,    logits=a__      ,    hidden_states=outputs.hidden_states     )




@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "						,     lowercase_						,     )
class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   int:






            '''simple docstring'''





            super().__init__(a__     )
            super()._init_backbone(a__     )

            __snake_case      :Optional[int] 							=    [config.embedding_size] + config.hidden_sizes
            __snake_case      :str 							=    ResNetEmbeddings(a__     )
            __snake_case      :Any 							=    ResNetEncoder(a__     )

            # initialize weights and apply final processing
            self.post_init()



       @add_start_docstrings_to_model_forward(a__     )
       @replace_return_docstrings(output_type=a__      ,    config_class=_CONFIG_FOR_DOC     )
       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   BackboneOutput:






            '''simple docstring'''





            __snake_case      :int 							=    return_dict if return_dict is not None else self.config.use_return_dict
            __snake_case      :Union[str, Any] 							=    (
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )

            __snake_case      :List[str] 							=    self.embedder(a__     )

            __snake_case      :List[Any] 							=    self.encoder(a__      ,    output_hidden_states=a__      ,    return_dict=a__     )

            __snake_case      :Optional[int] 							=    outputs.hidden_states

            __snake_case      :Tuple 							=    ()
            for idx, stage in enumerate(self.stage_names     ):
                 if stage in self.out_features:
                      feature_maps += (hidden_states[idx],)

            if not return_dict:
                 __snake_case      :Union[str, Any] 							=    (feature_maps,)
                 if output_hidden_states:
                      output += (outputs.hidden_states,)
                 return output

            return BackboneOutput(
                feature_maps=a__      ,    hidden_states=outputs.hidden_states if output_hidden_states else None      ,    attentions=a__      ,    )


