


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 ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


snake_case				:			str	     = logging.get_logger(__name__)

# General docstring
snake_case				:			str	     = '''RegNetConfig'''

# Base docstring
snake_case				:			Tuple	     = '''facebook/regnet-y-040'''
snake_case				:			Optional[Any]	     = [1, 10_88, 7, 7]

# Image classification docstring
snake_case				:			Dict	     = '''facebook/regnet-y-040'''
snake_case				:			Tuple	     = '''tabby, tabby cat'''

snake_case				:			Optional[int]	     = [
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]







class 			snake_case_  (nn.Module ):
					def __init__( self  :Any     ,__snake_case  :str     ,__snake_case  :List[Any]     ,__snake_case  :List[str] = 3     ,__snake_case  :Tuple = 1     ,__snake_case  :List[str] = 1     ,__snake_case  :str = "relu"     ,)   ->       List[Any]:
												super().__init__()
												a__			 =	nn.Convad(
												    _UpperCamelCase     ,_UpperCamelCase     ,kernel_size=_UpperCamelCase     ,stride=_UpperCamelCase     ,padding=kernel_size // 2     ,groups=_UpperCamelCase     ,bias=_UpperCamelCase     ,)
												a__			 =	nn.BatchNormad(_UpperCamelCase )
												a__			 =	ACTaFN[activation] if activation is not None else nn.Identity()



					def 					lowerCamelCase__( self  :Optional[Any]     ,__snake_case  :str )   ->       str:
												a__			 =	self.convolution(_UpperCamelCase )
												a__			 =	self.normalization(_UpperCamelCase )
												a__			 =	self.activation(_UpperCamelCase )
												return hidden_state







class 			snake_case_  (nn.Module ):
					def __init__( self  :Tuple     ,__snake_case  :List[str] )   ->       Tuple:
												super().__init__()
												a__			 =	RegNetConvLayer(
												    config.num_channels     ,config.embedding_size     ,kernel_size=3     ,stride=2     ,activation=config.hidden_act )
												a__			 =	config.num_channels



					def 					lowerCamelCase__( self  :List[Any]     ,__snake_case  :Any )   ->       Optional[Any]:
												a__			 =	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__			 =	self.embedder(_UpperCamelCase )
												return hidden_state







class 			snake_case_  (nn.Module ):
					def __init__( self  :Union[str, Any]     ,__snake_case  :Union[str, Any]     ,__snake_case  :Optional[int]     ,__snake_case  :Optional[int] = 2 )   ->       List[str]:
												super().__init__()
												a__			 =	nn.Convad(_UpperCamelCase     ,_UpperCamelCase     ,kernel_size=1     ,stride=_UpperCamelCase     ,bias=_UpperCamelCase )
												a__			 =	nn.BatchNormad(_UpperCamelCase )



					def 					lowerCamelCase__( self  :Any     ,__snake_case  :str )   ->       Any:
												a__			 =	self.convolution(_UpperCamelCase )
												a__			 =	self.normalization(_UpperCamelCase )
												return hidden_state







class 			snake_case_  (nn.Module ):
					def __init__( self  :Union[str, Any]     ,__snake_case  :Dict     ,__snake_case  :Optional[int] )   ->       List[str]:
												super().__init__()

												a__			 =	nn.AdaptiveAvgPoolad((1, 1) )
												a__			 =	nn.Sequential(
												    nn.Convad(_UpperCamelCase     ,_UpperCamelCase     ,kernel_size=1 )     ,nn.ReLU()     ,nn.Convad(_UpperCamelCase     ,_UpperCamelCase     ,kernel_size=1 )     ,nn.Sigmoid()     ,)



					def 					lowerCamelCase__( self  :Union[str, Any]     ,__snake_case  :Optional[Any] )   ->       Optional[int]:
												# b c h w -> b c 1 1
												a__			 =	self.pooler(_UpperCamelCase )
												a__			 =	self.attention(_UpperCamelCase )
												a__			 =	hidden_state * attention
												return hidden_state







class 			snake_case_  (nn.Module ):
					def __init__( self  :Optional[Any]     ,__snake_case  :Optional[Any]     ,__snake_case  :List[str]     ,__snake_case  :int     ,__snake_case  :str = 1 )   ->       Tuple:
												super().__init__()
												a__			 =	in_channels != out_channels or stride != 1
												a__			 =	max(1     ,out_channels // config.groups_width )
												a__			 =	(
												    RegNetShortCut(_UpperCamelCase     ,_UpperCamelCase     ,stride=_UpperCamelCase ) if should_apply_shortcut else nn.Identity()
												)
												a__			 =	nn.Sequential(
												    RegNetConvLayer(_UpperCamelCase     ,_UpperCamelCase     ,kernel_size=1     ,activation=config.hidden_act )     ,RegNetConvLayer(_UpperCamelCase     ,_UpperCamelCase     ,stride=_UpperCamelCase     ,groups=_UpperCamelCase     ,activation=config.hidden_act )     ,RegNetConvLayer(_UpperCamelCase     ,_UpperCamelCase     ,kernel_size=1     ,activation=_UpperCamelCase )     ,)
												a__			 =	ACTaFN[config.hidden_act]



					def 					lowerCamelCase__( self  :Optional[Any]     ,__snake_case  :List[Any] )   ->       List[str]:
												a__			 =	hidden_state
												a__			 =	self.layer(_UpperCamelCase )
												a__			 =	self.shortcut(_UpperCamelCase )
												hidden_state += residual
												a__			 =	self.activation(_UpperCamelCase )
												return hidden_state







class 			snake_case_  (nn.Module ):
					def __init__( self  :str     ,__snake_case  :Any     ,__snake_case  :Optional[Any]     ,__snake_case  :List[str]     ,__snake_case  :Optional[int] = 1 )   ->       Dict:
												super().__init__()
												a__			 =	in_channels != out_channels or stride != 1
												a__			 =	max(1     ,out_channels // config.groups_width )
												a__			 =	(
												    RegNetShortCut(_UpperCamelCase     ,_UpperCamelCase     ,stride=_UpperCamelCase ) if should_apply_shortcut else nn.Identity()
												)
												a__			 =	nn.Sequential(
												    RegNetConvLayer(_UpperCamelCase     ,_UpperCamelCase     ,kernel_size=1     ,activation=config.hidden_act )     ,RegNetConvLayer(_UpperCamelCase     ,_UpperCamelCase     ,stride=_UpperCamelCase     ,groups=_UpperCamelCase     ,activation=config.hidden_act )     ,RegNetSELayer(_UpperCamelCase     ,reduced_channels=int(round(in_channels / 4 ) ) )     ,RegNetConvLayer(_UpperCamelCase     ,_UpperCamelCase     ,kernel_size=1     ,activation=_UpperCamelCase )     ,)
												a__			 =	ACTaFN[config.hidden_act]



					def 					lowerCamelCase__( self  :Any     ,__snake_case  :Optional[int] )   ->       Optional[int]:
												a__			 =	hidden_state
												a__			 =	self.layer(_UpperCamelCase )
												a__			 =	self.shortcut(_UpperCamelCase )
												hidden_state += residual
												a__			 =	self.activation(_UpperCamelCase )
												return hidden_state







class 			snake_case_  (nn.Module ):
					def __init__( self  :List[Any]     ,__snake_case  :Tuple     ,__snake_case  :Optional[Any]     ,__snake_case  :Optional[int]     ,__snake_case  :Union[str, Any] = 2     ,__snake_case  :List[Any] = 2     ,)   ->       List[Any]:
												super().__init__()

												a__			 =	RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

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



					def 					lowerCamelCase__( self  :Dict     ,__snake_case  :Tuple )   ->       Tuple:
												a__			 =	self.layers(_UpperCamelCase )
												return hidden_state







class 			snake_case_  (nn.Module ):
					def __init__( self  :Optional[int]     ,__snake_case  :Tuple )   ->       List[Any]:
												super().__init__()
												a__			 =	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(
												    RegNetStage(
												        _UpperCamelCase     ,config.embedding_size     ,config.hidden_sizes[0]     ,stride=2 if config.downsample_in_first_stage else 1     ,depth=config.depths[0]     ,) )
												a__			 =	zip(config.hidden_sizes     ,config.hidden_sizes[1:] )
												for (in_channels, out_channels), depth in zip(_UpperCamelCase     ,config.depths[1:] ):
																			self.stages.append(RegNetStage(_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase     ,depth=_UpperCamelCase ) )



					def 					lowerCamelCase__( self  :Any     ,__snake_case  :Union[str, Any]     ,__snake_case  :Optional[int] = False     ,__snake_case  :List[str] = True )   ->       Optional[Any]:
												a__			 =	() if output_hidden_states else None

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

																			a__			 =	stage_module(_UpperCamelCase )

												if output_hidden_states:
																			a__			 =	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 			snake_case_  (A__ ):
					UpperCAmelCase__   :       List[str]        =     RegNetConfig
					UpperCAmelCase__   :       Any        =     """regnet"""
					UpperCAmelCase__   :       Optional[int]        =     """pixel_values"""
					UpperCAmelCase__   :       Any        =     True
					def 					lowerCamelCase__( self  :Union[str, Any]     ,__snake_case  :List[Any] )   ->       Any:
												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 					lowerCamelCase__( self  :Any     ,__snake_case  :Optional[int]     ,__snake_case  :str=False )   ->       int:
												if isinstance(_UpperCamelCase     ,_UpperCamelCase ):
																			a__			 =	value





snake_case				:			List[str]	     = r'''\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'''

snake_case				:			Any	     = r'''\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'''







@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.'''      ,		A__      ,		)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 			snake_case_  (A__ ):
					def __init__( self  :int     ,__snake_case  :List[str] )   ->       Tuple:
												super().__init__(_UpperCamelCase )
												a__			 =	config
												a__			 =	RegNetEmbeddings(_UpperCamelCase )
												a__			 =	RegNetEncoder(_UpperCamelCase )
												a__			 =	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 					lowerCamelCase__( self  :str     ,__snake_case  :Union[str, Any]     ,__snake_case  :Optional[Any] = None     ,__snake_case  :List[str] = None )   ->       List[Any]:
												a__			 =	(
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												a__			 =	return_dict if return_dict is not None else self.config.use_return_dict

												a__			 =	self.embedder(_UpperCamelCase )

												a__			 =	self.encoder(
												    _UpperCamelCase     ,output_hidden_states=_UpperCamelCase     ,return_dict=_UpperCamelCase )

												a__			 =	encoder_outputs[0]

												a__			 =	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(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''      ,		A__      ,		)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 			snake_case_  (A__ ):
					def __init__( self  :int     ,__snake_case  :Tuple )   ->       List[Any]:
												super().__init__(_UpperCamelCase )
												a__			 =	config.num_labels
												a__			 =	RegNetModel(_UpperCamelCase )
												# classification head
												a__			 =	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 					lowerCamelCase__( self  :Dict     ,__snake_case  :Tuple = None     ,__snake_case  :Optional[int] = None     ,__snake_case  :Optional[Any] = None     ,__snake_case  :Dict = None     ,)   ->       Any:
												a__			 =	return_dict if return_dict is not None else self.config.use_return_dict

												a__			 =	self.regnet(_UpperCamelCase     ,output_hidden_states=_UpperCamelCase     ,return_dict=_UpperCamelCase )

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

												a__			 =	self.classifier(_UpperCamelCase )

												a__			 =	None

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

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

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











def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 2_00_00_00						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   [0 for i in range(n + 1						)]
   _UpperCAmelCase           =   1
   _UpperCAmelCase           =   1

   for i in range(2				,	int(n**0.5						) + 1						):
      if primality_list[i] == 0:
         for j in range(i * i				,	n + 1				,	SCREAMING_SNAKE_CASE_						):
            _UpperCAmelCase           =   1
   _UpperCAmelCase           =   0
   for i in range(SCREAMING_SNAKE_CASE_						):
      if primality_list[i] == 0:
         sum_of_primes += i
   return sum_of_primes


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