




'''simple docstring'''





from math import pow


def      UpperCamelCase_(	snake_case							:       int							,				snake_case							:       int							,				snake_case							:       int							,				snake_case							:       int							,				snake_case							:       int							,				):

							'''simple docstring'''






							if current_sum == needed_sum:
														# If the sum of the powers is equal to needed_sum, then we have a solution.
														solutions_count += 1
														return current_sum, solutions_count

							snake_case_						=      int(pow(snake_case							,				snake_case     )     )
							if current_sum + i_to_n <= needed_sum:
														# If the sum of the powers is less than needed_sum, then continue adding powers.
														current_sum += i_to_n
														snake_case_					,      snake_case_						=      backtrack(
														    snake_case							,				snake_case							,				current_number + 1							,				snake_case							,				snake_case     )
														current_sum -= i_to_n
							if i_to_n < needed_sum:
														# If the power of i is less than needed_sum, then try with the next power.
														snake_case_					,      snake_case_						=      backtrack(
														    snake_case							,				snake_case							,				current_number + 1							,				snake_case							,				snake_case     )
							return current_sum, solutions_count


def      UpperCamelCase_(	snake_case							:       int							,				snake_case							:       int     ):

							'''simple docstring'''






							if not (1 <= needed_sum <= 1_0_0_0 and 2 <= power <= 1_0):
														raise ValueError(
														    "Invalid input\n"
														    "needed_sum must be between 1 and 1000, power between 2 and 10."     )

							return backtrack(snake_case							,				snake_case							,				1							,				0							,				0     )[1]  # Return the solutions_count


if __name__ == "__main__":
		import doctest

		doctest.testmod()











'''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


lowercase_					= logging.get_logger(__name__)

# General docstring
lowercase_					= """PoolFormerConfig"""

# Base docstring
lowercase_					= """sail/poolformer_s12"""
lowercase_					= [1, 512, 7, 7]

# Image classification docstring
lowercase_					= """sail/poolformer_s12"""
lowercase_					= """tabby, tabby cat"""

lowercase_					= [
    """sail/poolformer_s12""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]





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


class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A = None       )		->   None:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			drop_prob


    def 			snake_case_(		self ,		A       )		->   torch.Tensor:
        return drop_path(A ,		self.drop_prob ,		self.training       )





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




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A ,		A ,		A=None       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			patch_size if isinstance(A ,		collections.abc.Iterable       ) else (patch_size, patch_size)
        _SCREAMING_SNAKE_CASE									=			stride if isinstance(A ,		collections.abc.Iterable       ) else (stride, stride)
        _SCREAMING_SNAKE_CASE									=			padding if isinstance(A ,		collections.abc.Iterable       ) else (padding, padding)

        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		kernel_size=A ,		stride=A ,		padding=A       )
        _SCREAMING_SNAKE_CASE									=			norm_layer(A       ) if norm_layer else nn.Identity()





    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.projection(A       )
        _SCREAMING_SNAKE_CASE									=			self.norm(A       )
        return embeddings




class 					a_     (				nn.GroupNorm       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		**A       )		->   Union[str, Any]:
        super().__init__(1 ,		A ,		**A       )




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.AvgPoolad(A ,		stride=1 ,		padding=pool_size // 2 ,		count_include_pad=A       )





    def 			snake_case_(		self ,		A       )		->   Union[str, Any]:
        return self.pool(A       ) - hidden_states




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A       )		->   List[Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		1       )
        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		1       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerDropPath(A       )
        if isinstance(config.hidden_act ,		A       ):
            _SCREAMING_SNAKE_CASE									=			ACTaFN[config.hidden_act]
        else:
            _SCREAMING_SNAKE_CASE									=			config.hidden_act





    def 			snake_case_(		self ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.conva(A       )
        _SCREAMING_SNAKE_CASE									=			self.act_fn(A       )
        _SCREAMING_SNAKE_CASE									=			self.drop(A       )
        _SCREAMING_SNAKE_CASE									=			self.conva(A       )
        _SCREAMING_SNAKE_CASE									=			self.drop(A       )

        return hidden_states




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			PoolFormerPooling(A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerOutput(A ,		A ,		A ,		A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(A       )

        # Useful for training neural nets
        _SCREAMING_SNAKE_CASE									=			PoolFormerDropPath(A       ) if drop_path > 0.0 else nn.Identity()
        _SCREAMING_SNAKE_CASE									=			config.use_layer_scale
        if config.use_layer_scale:
            _SCREAMING_SNAKE_CASE									=			nn.Parameter(
                config.layer_scale_init_value * torch.ones((A)       ) ,		requires_grad=A       )
            _SCREAMING_SNAKE_CASE									=			nn.Parameter(
                config.layer_scale_init_value * torch.ones((A)       ) ,		requires_grad=A       )





    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        if self.use_layer_scale:
            _SCREAMING_SNAKE_CASE									=			self.pooling(self.before_norm(A       )       )
            _SCREAMING_SNAKE_CASE									=			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * pooling_output
            # First residual connection
            _SCREAMING_SNAKE_CASE									=			hidden_states + self.drop_path(A       )
            _SCREAMING_SNAKE_CASE									=			()

            _SCREAMING_SNAKE_CASE									=			self.output(self.after_norm(A       )       )
            _SCREAMING_SNAKE_CASE									=			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * layer_output
            # Second residual connection
            _SCREAMING_SNAKE_CASE									=			hidden_states + self.drop_path(A       )

            _SCREAMING_SNAKE_CASE									=			(output,) + outputs
            return outputs

        else:
            _SCREAMING_SNAKE_CASE									=			self.drop_path(self.pooling(self.before_norm(A       )       )       )
            # First residual connection
            _SCREAMING_SNAKE_CASE									=			pooling_output + hidden_states
            _SCREAMING_SNAKE_CASE									=			()

            # Second residual connection inside the PoolFormerOutput block
            _SCREAMING_SNAKE_CASE									=			self.drop_path(self.output(self.after_norm(A       )       )       )
            _SCREAMING_SNAKE_CASE									=			hidden_states + layer_output

            _SCREAMING_SNAKE_CASE									=			(output,) + outputs
            return outputs




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Any:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			config
        # stochastic depth decay rule
        _SCREAMING_SNAKE_CASE									=			[x.item() for x in torch.linspace(0 ,		config.drop_path_rate ,		sum(config.depths       )       )]

        # patch embeddings
        _SCREAMING_SNAKE_CASE									=			[]
        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] ,		)       )
        _SCREAMING_SNAKE_CASE									=			nn.ModuleList(A       )

        # Transformer blocks
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			0
        for i in range(config.num_encoder_blocks       ):
            # each block consists of layers
            _SCREAMING_SNAKE_CASE									=			[]
            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       )       )

        _SCREAMING_SNAKE_CASE									=			nn.ModuleList(A       )





    def 			snake_case_(		self ,		A ,		A=False ,		A=True       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			() if output_hidden_states else None

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

            if output_hidden_states:
                _SCREAMING_SNAKE_CASE									=			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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = PoolFormerConfig
    UpperCamelCase     = '''poolformer'''
    UpperCamelCase     = '''pixel_values'''
    UpperCamelCase     = True


    def 			snake_case_(		self ,		A       )		->   int:
        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       )		->   Dict:
        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			value




lowercase_					= 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.
"""

lowercase_					= 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.'''      ,    snake_case_      ,    )
class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   int:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			config

        _SCREAMING_SNAKE_CASE									=			PoolFormerEncoder(A       )

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


    def 			snake_case_(		self       )		->   Any:
        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 ,		)		->   Union[Tuple, BaseModelOutputWithNoAttention]:
        _SCREAMING_SNAKE_CASE									=			(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        _SCREAMING_SNAKE_CASE									=			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"""       )

        _SCREAMING_SNAKE_CASE									=			self.encoder(
            A ,		output_hidden_states=A ,		return_dict=A ,		)
        _SCREAMING_SNAKE_CASE									=			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 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Dict:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.Linear(config.hidden_size ,		config.hidden_size       )





    def 			snake_case_(		self ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.dense(A       )
        return output




@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''      ,    snake_case_      ,    )
class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Optional[Any]:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			config.num_labels
        _SCREAMING_SNAKE_CASE									=			PoolFormerModel(A       )

        # Final norm
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(config.hidden_sizes[-1]       )
        # Classifier head
        _SCREAMING_SNAKE_CASE									=			(
            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 ,		)		->   Union[Tuple, ImageClassifierOutputWithNoAttention]:
        _SCREAMING_SNAKE_CASE									=			return_dict if return_dict is not None else self.config.use_return_dict

        _SCREAMING_SNAKE_CASE									=			self.poolformer(
            A ,		output_hidden_states=A ,		return_dict=A ,		)

        _SCREAMING_SNAKE_CASE									=			outputs[0]

        _SCREAMING_SNAKE_CASE									=			self.classifier(self.norm(A       ).mean([-2, -1]       )       )

        _SCREAMING_SNAKE_CASE									=			None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    _SCREAMING_SNAKE_CASE									=			"""regression"""
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    _SCREAMING_SNAKE_CASE									=			"""single_label_classification"""
                else:
                    _SCREAMING_SNAKE_CASE									=			"""multi_label_classification"""

            if self.config.problem_type == "regression":
                _SCREAMING_SNAKE_CASE									=			MSELoss()
                if self.num_labels == 1:
                    _SCREAMING_SNAKE_CASE									=			loss_fct(logits.squeeze() ,		labels.squeeze()       )
                else:
                    _SCREAMING_SNAKE_CASE									=			loss_fct(A ,		A       )
            elif self.config.problem_type == "single_label_classification":
                _SCREAMING_SNAKE_CASE									=			CrossEntropyLoss()
                _SCREAMING_SNAKE_CASE									=			loss_fct(logits.view(-1 ,		self.num_labels       ) ,		labels.view(-1       )       )
            elif self.config.problem_type == "multi_label_classification":
                _SCREAMING_SNAKE_CASE									=			BCEWithLogitsLoss()
                _SCREAMING_SNAKE_CASE									=			loss_fct(A ,		A       )

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

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



