




'''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


UpperCAmelCase_							:	List[str]	     =		logging.get_logger(__name__)

# General docstring
UpperCAmelCase_							:	Union[str, Any]	     =		'''ResNetConfig'''

# Base docstring
UpperCAmelCase_							:	Tuple	     =		'''microsoft/resnet-50'''
UpperCAmelCase_							:	List[Any]	     =		[1, 2_0_4_8, 7, 7]

# Image classification docstring
UpperCAmelCase_							:	Optional[Any]	     =		'''microsoft/resnet-50'''
UpperCAmelCase_							:	Dict	     =		'''tiger cat'''

UpperCAmelCase_							:	Optional[Any]	     =		[
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]





class      lowerCAmelCase (			nn.Module):


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



            '''simple docstring'''




            super().__init__()
            __snake_case            =	nn.Convad(
                __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	kernel_size=__SCREAMING_SNAKE_CASE     ,	stride=__SCREAMING_SNAKE_CASE     ,	padding=kernel_size // 2     ,	bias=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	nn.BatchNormad(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ACTaFN[activation] if activation is not None else nn.Identity()




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Tensor:



            '''simple docstring'''




            __snake_case            =	self.convolution(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.normalization(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.activation(__SCREAMING_SNAKE_CASE	)
            return hidden_state





class      lowerCAmelCase (			nn.Module):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            super().__init__()
            __snake_case            =	ResNetConvLayer(
                config.num_channels     ,	config.embedding_size     ,	kernel_size=7     ,	stride=2     ,	activation=config.hidden_act	)
            __snake_case            =	nn.MaxPoolad(kernel_size=3     ,	stride=2     ,	padding=1	)
            __snake_case            =	config.num_channels




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Tensor:



            '''simple docstring'''




            __snake_case            =	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            =	self.embedder(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.pooler(__SCREAMING_SNAKE_CASE	)
            return embedding





class      lowerCAmelCase (			nn.Module):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 2	)   ->	Union[str, Any]:



            '''simple docstring'''




            super().__init__()
            __snake_case            =	nn.Convad(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	kernel_size=1     ,	stride=__SCREAMING_SNAKE_CASE     ,	bias=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	nn.BatchNormad(__SCREAMING_SNAKE_CASE	)




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Tensor:



            '''simple docstring'''




            __snake_case            =	self.convolution(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.normalization(__SCREAMING_SNAKE_CASE	)
            return hidden_state





class      lowerCAmelCase (			nn.Module):


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



            '''simple docstring'''




            super().__init__()
            __snake_case            =	in_channels != out_channels or stride != 1
            __snake_case            =	(
                ResNetShortCut(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	stride=__SCREAMING_SNAKE_CASE	) if should_apply_shortcut else nn.Identity()
            )
            __snake_case            =	nn.Sequential(
                ResNetConvLayer(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	stride=__SCREAMING_SNAKE_CASE	)     ,	ResNetConvLayer(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	activation=__SCREAMING_SNAKE_CASE	)     ,	)
            __snake_case            =	ACTaFN[activation]




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	hidden_state
            __snake_case            =	self.layer(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.shortcut(__SCREAMING_SNAKE_CASE	)
            hidden_state += residual
            __snake_case            =	self.activation(__SCREAMING_SNAKE_CASE	)
            return hidden_state





class      lowerCAmelCase (			nn.Module):


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



            '''simple docstring'''




            super().__init__()
            __snake_case            =	in_channels != out_channels or stride != 1
            __snake_case            =	out_channels // reduction
            __snake_case            =	(
                ResNetShortCut(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	stride=__SCREAMING_SNAKE_CASE	) if should_apply_shortcut else nn.Identity()
            )
            __snake_case            =	nn.Sequential(
                ResNetConvLayer(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	kernel_size=1	)     ,	ResNetConvLayer(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	stride=__SCREAMING_SNAKE_CASE	)     ,	ResNetConvLayer(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	kernel_size=1     ,	activation=__SCREAMING_SNAKE_CASE	)     ,	)
            __snake_case            =	ACTaFN[activation]




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	hidden_state
            __snake_case            =	self.layer(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.shortcut(__SCREAMING_SNAKE_CASE	)
            hidden_state += residual
            __snake_case            =	self.activation(__SCREAMING_SNAKE_CASE	)
            return hidden_state





class      lowerCAmelCase (			nn.Module):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 2     ,	__SCREAMING_SNAKE_CASE = 2     ,	)   ->	str:



            '''simple docstring'''




            super().__init__()

            __snake_case            =	ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

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




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Tensor:



            '''simple docstring'''




            __snake_case            =	input
            for layer in self.layers:
                 __snake_case            =	layer(__SCREAMING_SNAKE_CASE	)
            return hidden_state





class      lowerCAmelCase (			nn.Module):


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



            '''simple docstring'''




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




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = False     ,	__SCREAMING_SNAKE_CASE = True	)   ->	BaseModelOutputWithNoAttention:



            '''simple docstring'''




            __snake_case            =	() if output_hidden_states else None

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

                 __snake_case            =	stage_module(__SCREAMING_SNAKE_CASE	)

            if output_hidden_states:
                 __snake_case            =	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=__SCREAMING_SNAKE_CASE     ,	hidden_states=__SCREAMING_SNAKE_CASE     ,	)





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Optional[Any]										=      ResNetConfig
       __lowercase	:   Optional[int]										=      '''resnet'''
       __lowercase	:   Dict										=      '''pixel_values'''
       __lowercase	:   List[Any]										=      True


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




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




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False	)   ->	Any:



            '''simple docstring'''




            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	value





UpperCAmelCase_							:	Any	     =		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.
'''

UpperCAmelCase_							:	str	     =		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.'''					, __lowerCAmelCase					, )
class      lowerCAmelCase (			__lowerCAmelCase):


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



            '''simple docstring'''




            super().__init__(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	config
            __snake_case            =	ResNetEmbeddings(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ResNetEncoder(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	nn.AdaptiveAvgPoolad((1, 1)	)
            # Initialize weights and apply final processing
            self.post_init()




       @add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE	)
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC     ,	output_type=__SCREAMING_SNAKE_CASE     ,	config_class=_CONFIG_FOR_DOC     ,	modality='''vision'''     ,	expected_output=_EXPECTED_OUTPUT_SHAPE     ,	)
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None	)   ->	BaseModelOutputWithPoolingAndNoAttention:



            '''simple docstring'''




            __snake_case            =	(
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            __snake_case            =	return_dict if return_dict is not None else self.config.use_return_dict

            __snake_case            =	self.embedder(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.encoder(
                __SCREAMING_SNAKE_CASE     ,	output_hidden_states=__SCREAMING_SNAKE_CASE     ,	return_dict=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	encoder_outputs[0]

            __snake_case            =	self.pooler(__SCREAMING_SNAKE_CASE	)

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

            return BaseModelOutputWithPoolingAndNoAttention(
                last_hidden_state=__SCREAMING_SNAKE_CASE     ,	pooler_output=__SCREAMING_SNAKE_CASE     ,	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.
    '''					, __lowerCAmelCase					, )
class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            super().__init__(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	config.num_labels
            __snake_case            =	ResNetModel(__SCREAMING_SNAKE_CASE	)
            # classification head
            __snake_case            =	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(__SCREAMING_SNAKE_CASE	)
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT     ,	output_type=__SCREAMING_SNAKE_CASE     ,	config_class=_CONFIG_FOR_DOC     ,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,	)
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	)   ->	ImageClassifierOutputWithNoAttention:



            '''simple docstring'''




            __snake_case            =	return_dict if return_dict is not None else self.config.use_return_dict

            __snake_case            =	self.resnet(__SCREAMING_SNAKE_CASE     ,	output_hidden_states=__SCREAMING_SNAKE_CASE     ,	return_dict=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	outputs.pooler_output if return_dict else outputs[1]

            __snake_case            =	self.classifier(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	None

            if labels is not None:
                 if self.config.problem_type is None:
                      if self.num_labels == 1:
                           __snake_case            =	'''regression'''
                      elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                           __snake_case            =	'''single_label_classification'''
                      else:
                           __snake_case            =	'''multi_label_classification'''
                 if self.config.problem_type == "regression":
                      __snake_case            =	MSELoss()
                      if self.num_labels == 1:
                           __snake_case            =	loss_fct(logits.squeeze()     ,	labels.squeeze()	)
                      else:
                           __snake_case            =	loss_fct(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 elif self.config.problem_type == "single_label_classification":
                      __snake_case            =	CrossEntropyLoss()
                      __snake_case            =	loss_fct(logits.view(-1     ,	self.num_labels	)     ,	labels.view(-1	)	)
                 elif self.config.problem_type == "multi_label_classification":
                      __snake_case            =	BCEWithLogitsLoss()
                      __snake_case            =	loss_fct(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

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

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





@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''					, __lowerCAmelCase					, )
class      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase):


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



            '''simple docstring'''




            super().__init__(__SCREAMING_SNAKE_CASE	)
            super()._init_backbone(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	[config.embedding_size] + config.hidden_sizes
            __snake_case            =	ResNetEmbeddings(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ResNetEncoder(__SCREAMING_SNAKE_CASE	)

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




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



            '''simple docstring'''




            __snake_case            =	return_dict if return_dict is not None else self.config.use_return_dict
            __snake_case            =	(
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )

            __snake_case            =	self.embedder(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.encoder(__SCREAMING_SNAKE_CASE     ,	output_hidden_states=__SCREAMING_SNAKE_CASE     ,	return_dict=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	outputs.hidden_states

            __snake_case            =	()
            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            =	(feature_maps,)
                 if output_hidden_states:
                      output += (outputs.hidden_states,)
                 return output

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












'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     if divisor % 5 == 0 or divisor % 2 == 0:
          return 0
     __snake_case            =	1
     __snake_case            =	1
     while repunit:
          __snake_case            =	(10 * repunit + 1) % divisor
          repunit_index += 1
     return repunit_index







def       _UpperCamelCase			(_lowerCamelCase				:  int = 1_00_00_00						)-> int:




     '''simple docstring'''





     __snake_case            =	limit - 1
     if divisor % 2 == 0:
          divisor += 1
     while least_divisible_repunit(_lowerCamelCase						) <= limit:
          divisor += 2
     return divisor


if __name__ == "__main__":
     print(F"""{solution() = }""")






