



"""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 ...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


_UpperCAmelCase     =			logging.get_logger(__name__)

# General docstring
_UpperCAmelCase     =			"RegNetConfig"

# Base docstring
_UpperCAmelCase     =			"facebook/regnet-y-040"
_UpperCAmelCase     =			[1, 1_0_8_8, 7, 7]

# Image classification docstring
_UpperCAmelCase     =			"facebook/regnet-y-040"
_UpperCAmelCase     =			"tabby, tabby cat"

_UpperCAmelCase     =			[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 					a	(  nn.Module       ):



		def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int = 3       ,		lowerCAmelCase :					int = 1       ,		lowerCAmelCase :					int = 1       ,		lowerCAmelCase :					Optional[str] = "relu"       ,		)     ->					Optional[int]:


					'''simple docstring'''




					super().__init__()
					SCREAMING_SNAKE_CASE_: List[Any]     			=nn.Convad(
					    lowercase_       ,		lowercase_       ,		kernel_size=lowercase_       ,		stride=lowercase_       ,		padding=kernel_size // 2       ,		groups=lowercase_       ,		bias=lowercase_       ,		)
					SCREAMING_SNAKE_CASE_: str     			=nn.BatchNormad(lowercase_		)
					SCREAMING_SNAKE_CASE_: Optional[int]     			=ACTaFN[activation] if activation is not None else nn.Identity()





		def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					List[str]		)     ->					Dict:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Dict     			=self.convolution(lowercase_		)
					SCREAMING_SNAKE_CASE_: str     			=self.normalization(lowercase_		)
					SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.activation(lowercase_		)
					return hidden_state







class 					a	(  nn.Module       ):



		def __init__(						self :					List[Any]       ,		lowerCAmelCase :					RegNetConfig		)     ->					Optional[int]:


					'''simple docstring'''




					super().__init__()
					SCREAMING_SNAKE_CASE_: str     			=RegNetConvLayer(
					    config.num_channels       ,		config.embedding_size       ,		kernel_size=3       ,		stride=2       ,		activation=config.hidden_act		)
					SCREAMING_SNAKE_CASE_: Any     			=config.num_channels





		def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					Optional[Any]		)     ->					Optional[int]:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: List[str]     			=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."""		)
					SCREAMING_SNAKE_CASE_: Any     			=self.embedder(lowercase_		)
					return hidden_state







class 					a	(  nn.Module       ):



		def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int = 2		)     ->					List[Any]:


					'''simple docstring'''




					super().__init__()
					SCREAMING_SNAKE_CASE_: Optional[Any]     			=nn.Convad(lowercase_       ,		lowercase_       ,		kernel_size=1       ,		stride=lowercase_       ,		bias=lowercase_		)
					SCREAMING_SNAKE_CASE_: Union[str, Any]     			=nn.BatchNormad(lowercase_		)





		def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					Tensor		)     ->					List[str]:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Tuple     			=self.convolution(lowercase_		)
					SCREAMING_SNAKE_CASE_: str     			=self.normalization(lowercase_		)
					return hidden_state







class 					a	(  nn.Module       ):



		def __init__(						self :					str       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int		)     ->					Any:


					'''simple docstring'''




					super().__init__()

					SCREAMING_SNAKE_CASE_: int     			=nn.AdaptiveAvgPoolad((1, 1)		)
					SCREAMING_SNAKE_CASE_: int     			=nn.Sequential(
					    nn.Convad(lowercase_       ,		lowercase_       ,		kernel_size=1		)       ,		nn.ReLU()       ,		nn.Convad(lowercase_       ,		lowercase_       ,		kernel_size=1		)       ,		nn.Sigmoid()       ,		)





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


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: List[str]     			=self.pooler(lowercase_		)
					SCREAMING_SNAKE_CASE_: Optional[int]     			=self.attention(lowercase_		)
					SCREAMING_SNAKE_CASE_: Any     			=hidden_state * attention
					return hidden_state







class 					a	(  nn.Module       ):



		def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					RegNetConfig       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int = 1		)     ->					Dict:


					'''simple docstring'''




					super().__init__()
					SCREAMING_SNAKE_CASE_: List[Any]     			=in_channels != out_channels or stride != 1
					SCREAMING_SNAKE_CASE_: Optional[int]     			=max(1       ,		out_channels // config.groups_width		)
					SCREAMING_SNAKE_CASE_: Dict     			=(
					    RegNetShortCut(lowercase_       ,		lowercase_       ,		stride=lowercase_		) if should_apply_shortcut else nn.Identity()
					)
					SCREAMING_SNAKE_CASE_: List[Any]     			=nn.Sequential(
					    RegNetConvLayer(lowercase_       ,		lowercase_       ,		kernel_size=1       ,		activation=config.hidden_act		)       ,		RegNetConvLayer(lowercase_       ,		lowercase_       ,		stride=lowercase_       ,		groups=lowercase_       ,		activation=config.hidden_act		)       ,		RegNetConvLayer(lowercase_       ,		lowercase_       ,		kernel_size=1       ,		activation=lowercase_		)       ,		)
					SCREAMING_SNAKE_CASE_: int     			=ACTaFN[config.hidden_act]





		def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Any		)     ->					Tuple:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Any     			=hidden_state
					SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.layer(lowercase_		)
					SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.shortcut(lowercase_		)
					hidden_state += residual
					SCREAMING_SNAKE_CASE_: str     			=self.activation(lowercase_		)
					return hidden_state







class 					a	(  nn.Module       ):



		def __init__(						self :					Optional[Any]       ,		lowerCAmelCase :					RegNetConfig       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int = 1		)     ->					Dict:


					'''simple docstring'''




					super().__init__()
					SCREAMING_SNAKE_CASE_: str     			=in_channels != out_channels or stride != 1
					SCREAMING_SNAKE_CASE_: int     			=max(1       ,		out_channels // config.groups_width		)
					SCREAMING_SNAKE_CASE_: int     			=(
					    RegNetShortCut(lowercase_       ,		lowercase_       ,		stride=lowercase_		) if should_apply_shortcut else nn.Identity()
					)
					SCREAMING_SNAKE_CASE_: Union[str, Any]     			=nn.Sequential(
					    RegNetConvLayer(lowercase_       ,		lowercase_       ,		kernel_size=1       ,		activation=config.hidden_act		)       ,		RegNetConvLayer(lowercase_       ,		lowercase_       ,		stride=lowercase_       ,		groups=lowercase_       ,		activation=config.hidden_act		)       ,		RegNetSELayer(lowercase_       ,		reduced_channels=int(round(in_channels / 4		)		)		)       ,		RegNetConvLayer(lowercase_       ,		lowercase_       ,		kernel_size=1       ,		activation=lowercase_		)       ,		)
					SCREAMING_SNAKE_CASE_: Optional[int]     			=ACTaFN[config.hidden_act]





		def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					Union[str, Any]		)     ->					str:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_state
					SCREAMING_SNAKE_CASE_: str     			=self.layer(lowercase_		)
					SCREAMING_SNAKE_CASE_: int     			=self.shortcut(lowercase_		)
					hidden_state += residual
					SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.activation(lowercase_		)
					return hidden_state







class 					a	(  nn.Module       ):



		def __init__(						self :					str       ,		lowerCAmelCase :					RegNetConfig       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int = 2       ,		lowerCAmelCase :					int = 2       ,		)     ->					List[str]:


					'''simple docstring'''




					super().__init__()

					SCREAMING_SNAKE_CASE_: str     			=RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

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





		def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					List[str]		)     ->					Optional[Any]:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Tuple     			=self.layers(lowercase_		)
					return hidden_state







class 					a	(  nn.Module       ):



		def __init__(						self :					Dict       ,		lowerCAmelCase :					RegNetConfig		)     ->					int:


					'''simple docstring'''




					super().__init__()
					SCREAMING_SNAKE_CASE_: Optional[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(
					    RegNetStage(
					        lowercase_       ,		config.embedding_size       ,		config.hidden_sizes[0]       ,		stride=2 if config.downsample_in_first_stage else 1       ,		depth=config.depths[0]       ,		)		)
					SCREAMING_SNAKE_CASE_: Optional[Any]     			=zip(config.hidden_sizes       ,		config.hidden_sizes[1:]		)
					for (in_channels, out_channels), depth in zip(lowercase_       ,		config.depths[1:]		):
								self.stages.append(RegNetStage(lowercase_       ,		lowercase_       ,		lowercase_       ,		depth=lowercase_		)		)





		def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tensor       ,		lowerCAmelCase :					bool = False       ,		lowerCAmelCase :					bool = True		)     ->					List[Any]:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Tuple     			=() if output_hidden_states else None

					for stage_module in self.stages:
								if output_hidden_states:
											SCREAMING_SNAKE_CASE_: Union[str, Any]     			=hidden_states + (hidden_state,)

								SCREAMING_SNAKE_CASE_: Dict     			=stage_module(lowercase_		)

					if output_hidden_states:
								SCREAMING_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=lowercase_       ,		hidden_states=lowercase_		)







class 					a	(  _UpperCAmelCase       ):
		UpperCamelCase	:				List[str]     						=					RegNetConfig
		UpperCamelCase	:				Any     						=					'regnet'
		UpperCamelCase	:				List[str]     						=					'pixel_values'
		UpperCamelCase	:				str     						=					True



		def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					Optional[Any]		)     ->					List[str]:


					'''simple docstring'''




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





		def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					Optional[int]       ,		lowerCAmelCase :					Any=False		)     ->					Union[str, Any]:


					'''simple docstring'''




					if isinstance(lowercase_       ,		lowercase_		):
								SCREAMING_SNAKE_CASE_: List[str]     			=value







_UpperCAmelCase     =			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"

_UpperCAmelCase     =			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.'	,      _UpperCAmelCase	,      )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 					a	(  _UpperCAmelCase       ):



		def __init__(						self :					Any       ,		lowerCAmelCase :					Any		)     ->					int:


					'''simple docstring'''




					super().__init__(lowercase_		)
					SCREAMING_SNAKE_CASE_: List[str]     			=config
					SCREAMING_SNAKE_CASE_: Union[str, Any]     			=RegNetEmbeddings(lowercase_		)
					SCREAMING_SNAKE_CASE_: Union[str, Any]     			=RegNetEncoder(lowercase_		)
					SCREAMING_SNAKE_CASE_: str     			=nn.AdaptiveAvgPoolad((1, 1)		)
					# Initialize weights and apply final processing
					self.post_init()





		@add_start_docstrings_to_model_forward(lowercase_		)
		@add_code_sample_docstrings(
		    checkpoint=_CHECKPOINT_FOR_DOC       ,		output_type=lowercase_       ,		config_class=_CONFIG_FOR_DOC       ,		modality="""vision"""       ,		expected_output=_EXPECTED_OUTPUT_SHAPE       ,		)
		def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tensor       ,		lowerCAmelCase :					Optional[bool] = None       ,		lowerCAmelCase :					Optional[bool] = None		)     ->					int:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: List[Any]     			=(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					SCREAMING_SNAKE_CASE_: Tuple     			=return_dict if return_dict is not None else self.config.use_return_dict

					SCREAMING_SNAKE_CASE_: str     			=self.embedder(lowercase_		)

					SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.encoder(
					    lowercase_       ,		output_hidden_states=lowercase_       ,		return_dict=lowercase_		)

					SCREAMING_SNAKE_CASE_: List[Any]     			=encoder_outputs[0]

					SCREAMING_SNAKE_CASE_: str     			=self.pooler(lowercase_		)

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

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







@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '	,      _UpperCAmelCase	,      )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 					a	(  _UpperCAmelCase       ):



		def __init__(						self :					Dict       ,		lowerCAmelCase :					str		)     ->					Dict:


					'''simple docstring'''




					super().__init__(lowercase_		)
					SCREAMING_SNAKE_CASE_: Any     			=config.num_labels
					SCREAMING_SNAKE_CASE_: List[str]     			=RegNetModel(lowercase_		)
					# classification head
					SCREAMING_SNAKE_CASE_: 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(lowercase_		)
		@add_code_sample_docstrings(
		    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,		output_type=lowercase_       ,		config_class=_CONFIG_FOR_DOC       ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,		)
		def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					Optional[torch.FloatTensor] = None       ,		lowerCAmelCase :					Optional[torch.LongTensor] = None       ,		lowerCAmelCase :					Optional[bool] = None       ,		lowerCAmelCase :					Optional[bool] = None       ,		)     ->					List[Any]:


					'''simple docstring'''




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

					SCREAMING_SNAKE_CASE_: Optional[int]     			=self.regnet(lowercase_       ,		output_hidden_states=lowercase_       ,		return_dict=lowercase_		)

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

					SCREAMING_SNAKE_CASE_: List[Any]     			=self.classifier(lowercase_		)

					SCREAMING_SNAKE_CASE_: Optional[int]     			=None

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

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

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








"""simple docstring"""
def      __magic_name__				(							lowercase = 200_0000 ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=[0 for i in range(n + 1 )]
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=1
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=1

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


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



