




"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int = 1000     )  ->     int:
			__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				1, 1
			__lowerCamelCase					:	Union[str, Any]	       =				[]
			for i in range(1							,				n + 1     ):
						__lowerCamelCase					:	Any	       =				prev_numerator + 2 * prev_denominator
						__lowerCamelCase					:	Any	       =				prev_numerator + prev_denominator
						if len(str(A__     )     ) > len(str(A__     )     ):
									result.append(A__     )
						__lowerCamelCase					:	int	       =				numerator
						__lowerCamelCase					:	Optional[Any]	       =				denominator

			return len(A__     )


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









"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


a_				:    Tuple										=     logging.get_logger(__name__)

# General docstring
a_				:    List[str]										=     '''PoolFormerConfig'''

# Base docstring
a_				:    Optional[Any]										=     '''sail/poolformer_s12'''
a_				:    List[Any]										=     [1, 5_12, 7, 7]

# Image classification docstring
a_				:    Any										=     '''sail/poolformer_s12'''
a_				:    Optional[int]										=     '''tabby, tabby cat'''

a_				:    Optional[Any]										=     [
    '''sail/poolformer_s12''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]





def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    float = 0.0							,				A__:    bool = False     )  ->     Tuple:
			if drop_prob == 0.0 or not training:
						return input
			__lowerCamelCase					:	Dict	       =				1 - drop_prob
			__lowerCamelCase					:	List[Any]	       =				(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
			__lowerCamelCase					:	List[Any]	       =				keep_prob + torch.rand(A__							,				dtype=input.dtype							,				device=input.device     )
			random_tensor.floor_()  # binarize
			__lowerCamelCase					:	Any	       =				input.div(A__     ) * random_tensor
			return output

class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a = None     ):
								super().__init__()
								__lowerCamelCase					:	int	       =				drop_prob
					def     snake_case_				(    self						,	__a     ):
								return drop_path(__a						,	self.drop_prob						,	self.training     )





					def     snake_case_				(    self     ):
								return "p={}".format(self.drop_prob     )



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a=None     ):
								super().__init__()
								__lowerCamelCase					:	int	       =				patch_size if isinstance(__a						,	collections.abc.Iterable     ) else (patch_size, patch_size)
								__lowerCamelCase					:	int	       =				stride if isinstance(__a						,	collections.abc.Iterable     ) else (stride, stride)
								__lowerCamelCase					:	Optional[int]	       =				padding if isinstance(__a						,	collections.abc.Iterable     ) else (padding, padding)

								__lowerCamelCase					:	Optional[Any]	       =				nn.Convad(__a						,	__a						,	kernel_size=__a						,	stride=__a						,	padding=__a     )
								__lowerCamelCase					:	List[str]	       =				norm_layer(__a     ) if norm_layer else nn.Identity()





					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	List[Any]	       =				self.projection(__a     )
								__lowerCamelCase					:	Dict	       =				self.norm(__a     )
								return embeddings



class __lowercase(      nn.GroupNorm				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	**__a     ):
								super().__init__(1						,	__a						,	**__a     )



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a     ):
								super().__init__()
								__lowerCamelCase					:	str	       =				nn.AvgPoolad(__a						,	stride=1						,	padding=pool_size // 2						,	count_include_pad=__a     )





					def     snake_case_				(    self						,	__a     ):
								return self.pool(__a     ) - hidden_states



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a						,	__a     ):
								super().__init__()
								__lowerCamelCase					:	Any	       =				nn.Convad(__a						,	__a						,	1     )
								__lowerCamelCase					:	Dict	       =				nn.Convad(__a						,	__a						,	1     )
								__lowerCamelCase					:	List[Any]	       =				PoolFormerDropPath(__a     )
								if isinstance(config.hidden_act						,	__a     ):
											__lowerCamelCase					:	List[str]	       =				ACTaFN[config.hidden_act]
								else:
											__lowerCamelCase					:	str	       =				config.hidden_act





					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	int	       =				self.conva(__a     )
								__lowerCamelCase					:	Dict	       =				self.act_fn(__a     )
								__lowerCamelCase					:	List[str]	       =				self.drop(__a     )
								__lowerCamelCase					:	int	       =				self.conva(__a     )
								__lowerCamelCase					:	str	       =				self.drop(__a     )

								return hidden_states



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								super().__init__()
								__lowerCamelCase					:	Tuple	       =				PoolFormerPooling(__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				PoolFormerOutput(__a						,	__a						,	__a						,	__a     )
								__lowerCamelCase					:	List[Any]	       =				PoolFormerGroupNorm(__a     )
								__lowerCamelCase					:	List[Any]	       =				PoolFormerGroupNorm(__a     )

								# Useful for training neural nets
								__lowerCamelCase					:	Any	       =				PoolFormerDropPath(__a     ) if drop_path > 0.0 else nn.Identity()
								__lowerCamelCase					:	Tuple	       =				config.use_layer_scale
								if config.use_layer_scale:
											__lowerCamelCase					:	List[str]	       =				nn.Parameter(
											    config.layer_scale_init_value * torch.ones((__a)     )						,	requires_grad=__a     )
											__lowerCamelCase					:	Optional[int]	       =				nn.Parameter(
											    config.layer_scale_init_value * torch.ones((__a)     )						,	requires_grad=__a     )





					def     snake_case_				(    self						,	__a     ):
								if self.use_layer_scale:
											__lowerCamelCase					:	Union[str, Any]	       =				self.pooling(self.before_norm(__a     )     )
											__lowerCamelCase					:	Any	       =				self.layer_scale_a.unsqueeze(-1     ).unsqueeze(-1     ) * pooling_output
											# First residual connection
											__lowerCamelCase					:	Optional[Any]	       =				hidden_states + self.drop_path(__a     )
											__lowerCamelCase					:	Tuple	       =				()

											__lowerCamelCase					:	Optional[Any]	       =				self.output(self.after_norm(__a     )     )
											__lowerCamelCase					:	List[Any]	       =				self.layer_scale_a.unsqueeze(-1     ).unsqueeze(-1     ) * layer_output
											# Second residual connection
											__lowerCamelCase					:	List[Any]	       =				hidden_states + self.drop_path(__a     )

											__lowerCamelCase					:	Optional[Any]	       =				(output,) + outputs
											return outputs

								else:
											__lowerCamelCase					:	Tuple	       =				self.drop_path(self.pooling(self.before_norm(__a     )     )     )
											# First residual connection
											__lowerCamelCase					:	List[str]	       =				pooling_output + hidden_states
											__lowerCamelCase					:	int	       =				()

											# Second residual connection inside the PoolFormerOutput block
											__lowerCamelCase					:	List[str]	       =				self.drop_path(self.output(self.after_norm(__a     )     )     )
											__lowerCamelCase					:	str	       =				hidden_states + layer_output

											__lowerCamelCase					:	int	       =				(output,) + outputs
											return outputs



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a     ):
								super().__init__()
								__lowerCamelCase					:	int	       =				config
								# stochastic depth decay rule
								__lowerCamelCase					:	int	       =				[x.item() for x in torch.linspace(0						,	config.drop_path_rate						,	sum(config.depths     )     )]

								# patch embeddings
								__lowerCamelCase					:	List[str]	       =				[]
								for i in range(config.num_encoder_blocks     ):
											embeddings.append(
											    PoolFormerEmbeddings(
											        patch_size=config.patch_sizes[i]						,	stride=config.strides[i]						,	padding=config.padding[i]						,	num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]						,	hidden_size=config.hidden_sizes[i]						,	)     )
								__lowerCamelCase					:	Optional[int]	       =				nn.ModuleList(__a     )

								# Transformer blocks
								__lowerCamelCase					:	Any	       =				[]
								__lowerCamelCase					:	int	       =				0
								for i in range(config.num_encoder_blocks     ):
											# each block consists of layers
											__lowerCamelCase					:	Optional[int]	       =				[]
											if i != 0:
														cur += config.depths[i - 1]
											for j in range(config.depths[i]     ):
														layers.append(
														    PoolFormerLayer(
														        __a						,	num_channels=config.hidden_sizes[i]						,	pool_size=config.pool_size						,	hidden_size=config.hidden_sizes[i]						,	intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio     )						,	drop_path=dpr[cur + j]						,	)     )
											blocks.append(nn.ModuleList(__a     )     )

								__lowerCamelCase					:	str	       =				nn.ModuleList(__a     )





					def     snake_case_				(    self						,	__a						,	__a=False						,	__a=True     ):
								__lowerCamelCase					:	Union[str, Any]	       =				() if output_hidden_states else None

								__lowerCamelCase					:	int	       =				pixel_values
								for idx, layers in enumerate(zip(self.patch_embeddings						,	self.block     )     ):
											__lowerCamelCase       ,      __lowerCamelCase					:	Any	       =				layers
											# Get patch embeddings from hidden_states
											__lowerCamelCase					:	Any	       =				embedding_layer(__a     )
											# Send the embeddings through the blocks
											for _, blk in enumerate(__a     ):
														__lowerCamelCase					:	Optional[int]	       =				blk(__a     )
														__lowerCamelCase					:	Tuple	       =				layer_outputs[0]

											if output_hidden_states:
														__lowerCamelCase					:	Union[str, Any]	       =				all_hidden_states + (hidden_states,)

								if not return_dict:
											return tuple(v for v in [hidden_states, all_hidden_states] if v is not None     )

								return BaseModelOutputWithNoAttention(last_hidden_state=__a						,	hidden_states=__a     )



class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Tuple     		= PoolFormerConfig
					__a						:						Tuple     		= 'poolformer'
					__a						:						Optional[int]     		= 'pixel_values'
					__a						:						Optional[Any]     		= True
					def     snake_case_				(    self						,	__a     ):
								if isinstance(__a						,	(nn.Linear, nn.Convad)     ):
											module.weight.data.normal_(mean=0.0						,	std=self.config.initializer_range     )
											if module.bias is not None:
														module.bias.data.zero_()
								elif isinstance(__a						,	nn.LayerNorm     ):
											module.bias.data.zero_()
											module.weight.data.fill_(1.0     )





					def     snake_case_				(    self						,	__a						,	__a=False     ):
								if isinstance(__a						,	__a     ):
											__lowerCamelCase					:	Union[str, Any]	       =				value



a_				:    Union[str, Any]										=     R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): 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
            [`PoolFormerImageProcessor.__call__`] for details.
'''

@add_start_docstrings(
    'The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'	,	lowercase__	,	)
class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	__a     ):
								super().__init__(__a     )
								__lowerCamelCase					:	Optional[Any]	       =				config

								__lowerCamelCase					:	Any	       =				PoolFormerEncoder(__a     )

								# Initialize weights and apply final processing
								self.post_init()
					def     snake_case_				(    self     ):
								return self.embeddings.patch_embeddings





					@add_start_docstrings_to_model_forward(__a     )
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC						,	output_type=__a						,	config_class=_CONFIG_FOR_DOC						,	modality='vision'						,	expected_output=_EXPECTED_OUTPUT_SHAPE						,	)
					def     snake_case_				(    self						,	__a = None						,	__a = None						,	__a = None						,	):
								__lowerCamelCase					:	Union[str, Any]	       =				(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__lowerCamelCase					:	Dict	       =				return_dict if return_dict is not None else self.config.use_return_dict

								if pixel_values is None:
											raise ValueError('You have to specify pixel_values'     )

								__lowerCamelCase					:	Any	       =				self.encoder(
								    __a						,	output_hidden_states=__a						,	return_dict=__a						,	)
								__lowerCamelCase					:	int	       =				encoder_outputs[0]

								if not return_dict:
											return (sequence_output, None) + encoder_outputs[1:]

								return BaseModelOutputWithNoAttention(
								    last_hidden_state=__a						,	hidden_states=encoder_outputs.hidden_states						,	)



class __lowercase(      nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a     ):
								super().__init__()
								__lowerCamelCase					:	Optional[Any]	       =				nn.Linear(config.hidden_size						,	config.hidden_size     )





					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	List[Any]	       =				self.dense(__a     )
								return output



@add_start_docstrings(
    '\n    PoolFormer Model transformer with an image classification head on top\n    '	,	lowercase__	,	)
class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	__a     ):
								super().__init__(__a     )
								__lowerCamelCase					:	str	       =				config.num_labels
								__lowerCamelCase					:	Optional[Any]	       =				PoolFormerModel(__a     )

								# Final norm
								__lowerCamelCase					:	str	       =				PoolFormerGroupNorm(config.hidden_sizes[-1]     )
								# Classifier head
								__lowerCamelCase					:	Optional[Any]	       =				(
								    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(__a     )
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT						,	output_type=__a						,	config_class=_CONFIG_FOR_DOC						,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,	)
					def     snake_case_				(    self						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	):
								__lowerCamelCase					:	Any	       =				return_dict if return_dict is not None else self.config.use_return_dict

								__lowerCamelCase					:	Tuple	       =				self.poolformer(
								    __a						,	output_hidden_states=__a						,	return_dict=__a						,	)

								__lowerCamelCase					:	int	       =				outputs[0]

								__lowerCamelCase					:	Optional[int]	       =				self.classifier(self.norm(__a     ).mean([-2, -1]     )     )

								__lowerCamelCase					:	Union[str, Any]	       =				None
								if labels is not None:
											if self.config.problem_type is None:
														if self.num_labels == 1:
																	__lowerCamelCase					:	Any	       =				'regression'
														elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																	__lowerCamelCase					:	Any	       =				'single_label_classification'
														else:
																	__lowerCamelCase					:	Optional[Any]	       =				'multi_label_classification'

											if self.config.problem_type == "regression":
														__lowerCamelCase					:	int	       =				MSELoss()
														if self.num_labels == 1:
																	__lowerCamelCase					:	Optional[Any]	       =				loss_fct(logits.squeeze()						,	labels.squeeze()     )
														else:
																	__lowerCamelCase					:	Optional[Any]	       =				loss_fct(__a						,	__a     )
											elif self.config.problem_type == "single_label_classification":
														__lowerCamelCase					:	Tuple	       =				CrossEntropyLoss()
														__lowerCamelCase					:	int	       =				loss_fct(logits.view(-1						,	self.num_labels     )						,	labels.view(-1     )     )
											elif self.config.problem_type == "multi_label_classification":
														__lowerCamelCase					:	List[Any]	       =				BCEWithLogitsLoss()
														__lowerCamelCase					:	Optional[Any]	       =				loss_fct(__a						,	__a     )

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

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



