
"""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


A__      :	Union[str, Any]          =    logging.get_logger(__name__)

# General docstring
A__      :	Tuple          =    """RegNetConfig"""

# Base docstring
A__      :	List[Any]          =    """facebook/regnet-y-040"""
A__      :	Union[str, Any]          =    [1, 1_088, 7, 7]

# Image classification docstring
A__      :	List[Any]          =    """facebook/regnet-y-040"""
A__      :	List[str]          =    """tabby, tabby cat"""

A__      :	List[Any]          =    [
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]







class   _lowercase						(       nn.Module				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 3 ,			__UpperCamelCase = 1 ,			__UpperCamelCase = 1 ,			__UpperCamelCase = "relu" ,			)->					Any:
										super().__init__()
										UpperCAmelCase__							: Union[str, Any] 							=						nn.Convad(
										    __UpperCamelCase ,			__UpperCamelCase ,			kernel_size=__UpperCamelCase ,			stride=__UpperCamelCase ,			padding=kernel_size // 2 ,			groups=__UpperCamelCase ,			bias=__UpperCamelCase ,			)
										UpperCAmelCase__							: Optional[Any] 							=						nn.BatchNormad(__UpperCamelCase  )
										UpperCAmelCase__							: List[Any] 							=						ACTaFN[activation] if activation is not None else nn.Identity()



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Any:
										UpperCAmelCase__							: List[str] 							=						self.convolution(__UpperCamelCase  )
										UpperCAmelCase__							: Optional[int] 							=						self.normalization(__UpperCamelCase  )
										UpperCAmelCase__							: Any 							=						self.activation(__UpperCamelCase  )
										return hidden_state







class   _lowercase						(       nn.Module				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase  )->					str:
										super().__init__()
										UpperCAmelCase__							: Dict 							=						RegNetConvLayer(
										    config.num_channels ,			config.embedding_size ,			kernel_size=3 ,			stride=2 ,			activation=config.hidden_act  )
										UpperCAmelCase__							: Optional[int] 							=						config.num_channels



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Any:
										UpperCAmelCase__							: Dict 							=						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."  )
										UpperCAmelCase__							: Optional[Any] 							=						self.embedder(__UpperCamelCase  )
										return hidden_state







class   _lowercase						(       nn.Module				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 2  )->					Union[str, Any]:
										super().__init__()
										UpperCAmelCase__							: Optional[Any] 							=						nn.Convad(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1 ,			stride=__UpperCamelCase ,			bias=__UpperCamelCase  )
										UpperCAmelCase__							: int 							=						nn.BatchNormad(__UpperCamelCase  )



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tensor:
										UpperCAmelCase__							: List[str] 							=						self.convolution(__UpperCamelCase  )
										UpperCAmelCase__							: str 							=						self.normalization(__UpperCamelCase  )
										return hidden_state







class   _lowercase						(       nn.Module				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[int]:
										super().__init__()

										UpperCAmelCase__							: Union[str, Any] 							=						nn.AdaptiveAvgPoolad((1, 1)  )
										UpperCAmelCase__							: Union[str, Any] 							=						nn.Sequential(
										    nn.Convad(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1  ) ,			nn.ReLU() ,			nn.Convad(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1  ) ,			nn.Sigmoid() ,			)



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
										# b c h w -> b c 1 1
										UpperCAmelCase__							: Union[str, Any] 							=						self.pooler(__UpperCamelCase  )
										UpperCAmelCase__							: Any 							=						self.attention(__UpperCamelCase  )
										UpperCAmelCase__							: List[str] 							=						hidden_state * attention
										return hidden_state







class   _lowercase						(       nn.Module				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 1  )->					List[str]:
										super().__init__()
										UpperCAmelCase__							: Tuple 							=						in_channels != out_channels or stride != 1
										UpperCAmelCase__							: List[str] 							=						max(1 ,			out_channels // config.groups_width  )
										UpperCAmelCase__							: str 							=						(
										    RegNetShortCut(__UpperCamelCase ,			__UpperCamelCase ,			stride=__UpperCamelCase  ) if should_apply_shortcut else nn.Identity()
										)
										UpperCAmelCase__							: List[Any] 							=						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  ) ,			)
										UpperCAmelCase__							: str 							=						ACTaFN[config.hidden_act]



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:
										UpperCAmelCase__							: Optional[Any] 							=						hidden_state
										UpperCAmelCase__							: Dict 							=						self.layer(__UpperCamelCase  )
										UpperCAmelCase__							: Optional[Any] 							=						self.shortcut(__UpperCamelCase  )
										hidden_state += residual
										UpperCAmelCase__							: Tuple 							=						self.activation(__UpperCamelCase  )
										return hidden_state







class   _lowercase						(       nn.Module				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 1  )->					str:
										super().__init__()
										UpperCAmelCase__							: Any 							=						in_channels != out_channels or stride != 1
										UpperCAmelCase__							: str 							=						max(1 ,			out_channels // config.groups_width  )
										UpperCAmelCase__							: Union[str, Any] 							=						(
										    RegNetShortCut(__UpperCamelCase ,			__UpperCamelCase ,			stride=__UpperCamelCase  ) if should_apply_shortcut else nn.Identity()
										)
										UpperCAmelCase__							: List[str] 							=						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  ) ,			)
										UpperCAmelCase__							: List[str] 							=						ACTaFN[config.hidden_act]



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
										UpperCAmelCase__							: Any 							=						hidden_state
										UpperCAmelCase__							: Optional[Any] 							=						self.layer(__UpperCamelCase  )
										UpperCAmelCase__							: Union[str, Any] 							=						self.shortcut(__UpperCamelCase  )
										hidden_state += residual
										UpperCAmelCase__							: str 							=						self.activation(__UpperCamelCase  )
										return hidden_state







class   _lowercase						(       nn.Module				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 2 ,			__UpperCamelCase = 2 ,			)->					str:
										super().__init__()

										UpperCAmelCase__							: Optional[int] 							=						RegNetXLayer if config.layer_type == "x" else RegNetYLayer

										UpperCAmelCase__							: Any 							=						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 ,			__UpperCamelCase  )->					Optional[int]:
										UpperCAmelCase__							: List[Any] 							=						self.layers(__UpperCamelCase  )
										return hidden_state







class   _lowercase						(       nn.Module				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase  )->					Tuple:
										super().__init__()
										UpperCAmelCase__							: 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(
										    RegNetStage(
										        __UpperCamelCase ,			config.embedding_size ,			config.hidden_sizes[0] ,			stride=2 if config.downsample_in_first_stage else 1 ,			depth=config.depths[0] ,			)  )
										UpperCAmelCase__							: int 							=						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 ,			__UpperCamelCase ,			__UpperCamelCase = False ,			__UpperCamelCase = True  )->					BaseModelOutputWithNoAttention:
										UpperCAmelCase__							: Dict 							=						() if output_hidden_states else None

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

															UpperCAmelCase__							: Optional[Any] 							=						stage_module(__UpperCamelCase  )

										if output_hidden_states:
															UpperCAmelCase__							: Tuple 							=						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						(       lowerCAmelCase_				):


					'''simple docstring'''





					_A								=							RegNetConfig
					_A								=							'regnet'
					_A								=							'pixel_values'
					_A								=							True



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
										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 ,			__UpperCamelCase ,			__UpperCamelCase=False  )->					str:
										if isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
															UpperCAmelCase__							: str 							=						value





A__      :	Union[str, 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 ([`RegNetConfig`]): 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.
"""

A__      :	List[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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""







@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'	,   lowerCAmelCase_	,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class   _lowercase						(       lowerCAmelCase_				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase  )->					Tuple:
										super().__init__(__UpperCamelCase  )
										UpperCAmelCase__							: List[str] 							=						config
										UpperCAmelCase__							: Dict 							=						RegNetEmbeddings(__UpperCamelCase  )
										UpperCAmelCase__							: List[str] 							=						RegNetEncoder(__UpperCamelCase  )
										UpperCAmelCase__							: List[str] 							=						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 ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None  )->					BaseModelOutputWithPoolingAndNoAttention:
										UpperCAmelCase__							: Dict 							=						(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										UpperCAmelCase__							: Any 							=						return_dict if return_dict is not None else self.config.use_return_dict

										UpperCAmelCase__							: Optional[Any] 							=						self.embedder(__UpperCamelCase  )

										UpperCAmelCase__							: Any 							=						self.encoder(
										    __UpperCamelCase ,			output_hidden_states=__UpperCamelCase ,			return_dict=__UpperCamelCase  )

										UpperCAmelCase__							: Dict 							=						encoder_outputs[0]

										UpperCAmelCase__							: Tuple 							=						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(
    '\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    '	,   lowerCAmelCase_	,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class   _lowercase						(       lowerCAmelCase_				):


					'''simple docstring'''





					def __init__(      self ,			__UpperCamelCase  )->					List[Any]:
										super().__init__(__UpperCamelCase  )
										UpperCAmelCase__							: str 							=						config.num_labels
										UpperCAmelCase__							: List[Any] 							=						RegNetModel(__UpperCamelCase  )
										# classification head
										UpperCAmelCase__							: 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  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					ImageClassifierOutputWithNoAttention:
										UpperCAmelCase__							: Union[str, Any] 							=						return_dict if return_dict is not None else self.config.use_return_dict

										UpperCAmelCase__							: Any 							=						self.regnet(__UpperCamelCase ,			output_hidden_states=__UpperCamelCase ,			return_dict=__UpperCamelCase  )

										UpperCAmelCase__							: List[str] 							=						outputs.pooler_output if return_dict else outputs[1]

										UpperCAmelCase__							: Optional[Any] 							=						self.classifier(__UpperCamelCase  )

										UpperCAmelCase__							: Union[str, Any] 							=						None

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

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

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





"""simple docstring"""




from math import isqrt



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						[True] * max_number
     for i in range(2       ,    isqrt(max_number - 1		) + 1		):
          if is_prime[i]:
               for j in range(i**2       ,    lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: List[Any] 							=						False

     return [i for i in range(2       ,    lowerCAmelCase		) if is_prime[i]]



def 							a__ (				lowerCAmelCase	:       int = 10**8		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						calculate_prime_numbers(max_number // 2		)

     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Tuple 							=						len(lowerCAmelCase		) - 1
     while left <= right:
          while prime_numbers[left] * prime_numbers[right] >= max_number:
               right -= 1
          semiprimes_count += right - left + 1
          left += 1

     return semiprimes_count


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



