





'''simple docstring'''






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


snake_case_     =	logging.get_logger(__name__)

# General docstring
snake_case_     =	"""ResNetConfig"""

# Base docstring
snake_case_     =	"""microsoft/resnet-50"""
snake_case_     =	[1, 20_48, 7, 7]

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

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


class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = 3    ,   _UpperCAmelCase = 1    ,   _UpperCAmelCase = "relu"					):
       super().__init__()
       A       :       Dict							=						nn.Convad(
           _UpperCAmelCase    ,   _UpperCAmelCase    ,   kernel_size=_UpperCAmelCase    ,   stride=_UpperCAmelCase    ,   padding=kernel_size // 2    ,   bias=_UpperCAmelCase					)
       A       :       Dict							=						nn.BatchNormad(_UpperCAmelCase					)
       A       :       Any							=						ACTaFN[activation] if activation is not None else nn.Identity()


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       List[str]							=						self.convolution(_UpperCAmelCase					)
       A       :       List[str]							=						self.normalization(_UpperCAmelCase					)
       A       :       Tuple							=						self.activation(_UpperCAmelCase					)
       return hidden_state







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase					):
       super().__init__()
       A       :       int							=						ResNetConvLayer(
           config.num_channels    ,   config.embedding_size    ,   kernel_size=7    ,   stride=2    ,   activation=config.hidden_act					)
       A       :       List[Any]							=						nn.MaxPoolad(kernel_size=3    ,   stride=2    ,   padding=1					)
       A       :       Optional[int]							=						config.num_channels


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       int							=						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.'''					)
       A       :       Any							=						self.embedder(_UpperCAmelCase					)
       A       :       Dict							=						self.pooler(_UpperCAmelCase					)
       return embedding







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = 2					):
       super().__init__()
       A       :       List[Any]							=						nn.Convad(_UpperCAmelCase    ,   _UpperCAmelCase    ,   kernel_size=1    ,   stride=_UpperCAmelCase    ,   bias=_UpperCAmelCase					)
       A       :       List[str]							=						nn.BatchNormad(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       int							=						self.convolution(_UpperCAmelCase					)
       A       :       Any							=						self.normalization(_UpperCAmelCase					)
       return hidden_state







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = 1    ,   _UpperCAmelCase = "relu"					):
       super().__init__()
       A       :       int							=						in_channels != out_channels or stride != 1
       A       :       Optional[int]							=						(
           ResNetShortCut(_UpperCAmelCase    ,   _UpperCAmelCase    ,   stride=_UpperCAmelCase					) if should_apply_shortcut else nn.Identity()
       )
       A       :       Any							=						nn.Sequential(
           ResNetConvLayer(_UpperCAmelCase    ,   _UpperCAmelCase    ,   stride=_UpperCAmelCase					)    ,   ResNetConvLayer(_UpperCAmelCase    ,   _UpperCAmelCase    ,   activation=_UpperCAmelCase					)    ,   )
       A       :       List[str]							=						ACTaFN[activation]


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Optional[int]							=						hidden_state
       A       :       Optional[Any]							=						self.layer(_UpperCAmelCase					)
       A       :       Union[str, Any]							=						self.shortcut(_UpperCAmelCase					)
       hidden_state += residual
       A       :       List[str]							=						self.activation(_UpperCAmelCase					)
       return hidden_state







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = 1    ,   _UpperCAmelCase = "relu"    ,   _UpperCAmelCase = 4					):
       super().__init__()
       A       :       Tuple							=						in_channels != out_channels or stride != 1
       A       :       int							=						out_channels // reduction
       A       :       Optional[Any]							=						(
           ResNetShortCut(_UpperCAmelCase    ,   _UpperCAmelCase    ,   stride=_UpperCAmelCase					) if should_apply_shortcut else nn.Identity()
       )
       A       :       Union[str, Any]							=						nn.Sequential(
           ResNetConvLayer(_UpperCAmelCase    ,   _UpperCAmelCase    ,   kernel_size=1					)    ,   ResNetConvLayer(_UpperCAmelCase    ,   _UpperCAmelCase    ,   stride=_UpperCAmelCase					)    ,   ResNetConvLayer(_UpperCAmelCase    ,   _UpperCAmelCase    ,   kernel_size=1    ,   activation=_UpperCAmelCase					)    ,   )
       A       :       List[str]							=						ACTaFN[activation]


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       str							=						hidden_state
       A       :       Tuple							=						self.layer(_UpperCAmelCase					)
       A       :       str							=						self.shortcut(_UpperCAmelCase					)
       hidden_state += residual
       A       :       Any							=						self.activation(_UpperCAmelCase					)
       return hidden_state







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = 2    ,   _UpperCAmelCase = 2    ,   ):
       super().__init__()

       A       :       str							=						ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

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


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       int							=						input
       for layer in self.layers:
           A       :       Tuple							=						layer(_UpperCAmelCase					)
       return hidden_state







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase					):
       super().__init__()
       A       :       Any							=						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(
               _UpperCAmelCase    ,   config.embedding_size    ,   config.hidden_sizes[0]    ,   stride=2 if config.downsample_in_first_stage else 1    ,   depth=config.depths[0]    ,   )					)
       A       :       List[Any]							=						zip(config.hidden_sizes    ,   config.hidden_sizes[1:]					)
       for (in_channels, out_channels), depth in zip(_UpperCAmelCase    ,   config.depths[1:]					):
           self.stages.append(ResNetStage(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   depth=_UpperCAmelCase					)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = True					):
       A       :       Optional[Any]							=						() if output_hidden_states else None

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

           A       :       str							=						stage_module(_UpperCAmelCase					)

       if output_hidden_states:
           A       :       Union[str, 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=_UpperCAmelCase    ,   hidden_states=_UpperCAmelCase    ,   )







class   _lowercase						(							a				):
   _UpperCamelCase       		=   ResNetConfig
   _UpperCamelCase       		=   """resnet"""
   _UpperCamelCase       		=   """pixel_values"""
   _UpperCamelCase       		=   True


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       if isinstance(_UpperCAmelCase    ,   nn.Convad					):
           nn.init.kaiming_normal_(module.weight    ,   mode='''fan_out'''    ,   nonlinearity='''relu'''					)
       elif isinstance(_UpperCAmelCase    ,   (nn.BatchNormad, nn.GroupNorm)					):
           nn.init.constant_(module.weight    ,   1					)
           nn.init.constant_(module.bias    ,   0					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=False					):
       if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       Tuple							=						value







snake_case_     =	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.
"""

snake_case_     =	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."""		,					a		,					)
class   _lowercase						(							a				):


   def __init__( self    ,   _UpperCAmelCase					):
       super().__init__(_UpperCAmelCase					)
       A       :       Union[str, Any]							=						config
       A       :       Tuple							=						ResNetEmbeddings(_UpperCAmelCase					)
       A       :       Tuple							=						ResNetEncoder(_UpperCAmelCase					)
       A       :       List[Any]							=						nn.AdaptiveAvgPoolad((1, 1)					)
       # Initialize weights and apply final processing
       self.post_init()


   @add_start_docstrings_to_model_forward(_UpperCAmelCase					)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC    ,   output_type=_UpperCAmelCase    ,   config_class=_CONFIG_FOR_DOC    ,   modality='''vision'''    ,   expected_output=_EXPECTED_OUTPUT_SHAPE    ,   )
   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None					):
       A       :       List[str]							=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       A       :       str							=						return_dict if return_dict is not None else self.config.use_return_dict

       A       :       List[Any]							=						self.embedder(_UpperCAmelCase					)

       A       :       List[Any]							=						self.encoder(
           _UpperCAmelCase    ,   output_hidden_states=_UpperCAmelCase    ,   return_dict=_UpperCAmelCase					)

       A       :       Dict							=						encoder_outputs[0]

       A       :       Optional[int]							=						self.pooler(_UpperCAmelCase					)

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

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







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


   def __init__( self    ,   _UpperCAmelCase					):
       super().__init__(_UpperCAmelCase					)
       A       :       Optional[int]							=						config.num_labels
       A       :       Optional[int]							=						ResNetModel(_UpperCAmelCase					)
       # classification head
       A       :       List[Any]							=						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(_UpperCAmelCase					)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT    ,   output_type=_UpperCAmelCase    ,   config_class=_CONFIG_FOR_DOC    ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,   )
   def 					snake_case				( self    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   ):
       A       :       List[Any]							=						return_dict if return_dict is not None else self.config.use_return_dict

       A       :       str							=						self.resnet(_UpperCAmelCase    ,   output_hidden_states=_UpperCAmelCase    ,   return_dict=_UpperCAmelCase					)

       A       :       int							=						outputs.pooler_output if return_dict else outputs[1]

       A       :       Optional[int]							=						self.classifier(_UpperCAmelCase					)

       A       :       Dict							=						None

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

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

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







@add_start_docstrings(
    """
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    """		,					a		,					)
class   _lowercase						(							a		,					a				):


   def __init__( self    ,   _UpperCAmelCase					):
       super().__init__(_UpperCAmelCase					)
       super()._init_backbone(_UpperCAmelCase					)

       A       :       Optional[Any]							=						[config.embedding_size] + config.hidden_sizes
       A       :       Tuple							=						ResNetEmbeddings(_UpperCAmelCase					)
       A       :       int							=						ResNetEncoder(_UpperCAmelCase					)

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


   @add_start_docstrings_to_model_forward(_UpperCAmelCase					)
   @replace_return_docstrings(output_type=_UpperCAmelCase    ,   config_class=_CONFIG_FOR_DOC					)
   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None					):
       A       :       int							=						return_dict if return_dict is not None else self.config.use_return_dict
       A       :       int							=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )

       A       :       Any							=						self.embedder(_UpperCAmelCase					)

       A       :       Any							=						self.encoder(_UpperCAmelCase    ,   output_hidden_states=_UpperCAmelCase    ,   return_dict=_UpperCAmelCase					)

       A       :       Tuple							=						outputs.hidden_states

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

       if not return_dict:
           A       :       List[Any]							=						(feature_maps,)
           if output_hidden_states:
               output += (outputs.hidden_states,)
           return output

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







'''simple docstring'''






from math import pi
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : int		)		->  float:
    return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(90, 10))
