






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


lowerCamelCase      :       List[str]    =						logging.get_logger(__name__)

# General docstring
lowerCamelCase      :       str    =						'''PoolFormerConfig'''

# Base docstring
lowerCamelCase      :       Any    =						'''sail/poolformer_s12'''
lowerCamelCase      :       Dict    =						[1, 512, 7, 7]

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

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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case = 0.0       ,			__snake_case = False							):
     if drop_prob == 0.0 or not training:
          return input
     __lowerCAmelCase             =       1 - drop_prob
     __lowerCAmelCase             =       (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
     __lowerCAmelCase             =       keep_prob + torch.rand(UpperCamelCase__       ,			dtype=input.dtype       ,			device=input.device							)
     random_tensor.floor_()  # binarize
     __lowerCAmelCase             =       input.div(UpperCamelCase__							) * random_tensor
     return output



class 				_UpperCamelCase      (nn.Module      ):


      def __init__(						self       ,    __UpperCamelCase = None						)->						None:
           super().__init__()
           __lowerCAmelCase             =       drop_prob


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						torch.Tensor:
           return drop_path(__A       ,    self.drop_prob       ,    self.training						)


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



class 				_UpperCamelCase      (nn.Module      ):


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

           __lowerCAmelCase             =       nn.Convad(__A       ,    __A       ,    kernel_size=__A       ,    stride=__A       ,    padding=__A						)
           __lowerCAmelCase             =       norm_layer(__A						) if norm_layer else nn.Identity()


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Optional[Any]:
           __lowerCAmelCase             =       self.projection(__A						)
           __lowerCAmelCase             =       self.norm(__A						)
           return embeddings



class 				_UpperCamelCase      (nn.GroupNorm      ):


      def __init__(						self       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Dict:
           super().__init__(1       ,    __A       ,    **__A						)



class 				_UpperCamelCase      (nn.Module      ):


      def __init__(						self       ,    __UpperCamelCase						)->						Any:
           super().__init__()
           __lowerCAmelCase             =       nn.AvgPoolad(__A       ,    stride=1       ,    padding=pool_size // 2       ,    count_include_pad=__A						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Optional[Any]:
           return self.pool(__A						) - hidden_states



class 				_UpperCamelCase      (nn.Module      ):


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						str:
           super().__init__()
           __lowerCAmelCase             =       nn.Convad(__A       ,    __A       ,    1						)
           __lowerCAmelCase             =       nn.Convad(__A       ,    __A       ,    1						)
           __lowerCAmelCase             =       PoolFormerDropPath(__A						)
           if isinstance(config.hidden_act       ,    __A						):
                __lowerCAmelCase             =       ACTaFN[config.hidden_act]
           else:
                __lowerCAmelCase             =       config.hidden_act


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       self.conva(__A						)
           __lowerCAmelCase             =       self.act_fn(__A						)
           __lowerCAmelCase             =       self.drop(__A						)
           __lowerCAmelCase             =       self.conva(__A						)
           __lowerCAmelCase             =       self.drop(__A						)

           return hidden_states



class 				_UpperCamelCase      (nn.Module      ):


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           super().__init__()
           __lowerCAmelCase             =       PoolFormerPooling(__A						)
           __lowerCAmelCase             =       PoolFormerOutput(__A       ,    __A       ,    __A       ,    __A						)
           __lowerCAmelCase             =       PoolFormerGroupNorm(__A						)
           __lowerCAmelCase             =       PoolFormerGroupNorm(__A						)

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


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						str:



           if self.use_layer_scale:
                __lowerCAmelCase             =       self.pooling(self.before_norm(__A						)						)
                __lowerCAmelCase             =       self.layer_scale_a.unsqueeze(-1						).unsqueeze(-1						) * pooling_output
                # First residual connection
                __lowerCAmelCase             =       hidden_states + self.drop_path(__A						)
                __lowerCAmelCase             =       ()

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

                __lowerCAmelCase             =       (output,) + outputs
                return outputs

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

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

                __lowerCAmelCase             =       (output,) + outputs
                return outputs



class 				_UpperCamelCase      (nn.Module      ):


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

           # patch embeddings
           __lowerCAmelCase             =       []
           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             =       nn.ModuleList(__A						)

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


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=False       ,    __UpperCamelCase=True						)->						Optional[int]:
           __lowerCAmelCase             =       () if output_hidden_states else None

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

                if output_hidden_states:
                     __lowerCAmelCase             =       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 				_UpperCamelCase      (UpperCamelCase__      ):
      snake_case_	=				PoolFormerConfig
      snake_case_	=				"""poolformer"""
      snake_case_	=				"""pixel_values"""
      snake_case_	=				True


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Dict:



           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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=False						)->						Any:



           if isinstance(__A       ,    __A						):
                __lowerCAmelCase             =       value

lowerCamelCase      :       Optional[Any]    =						R'''\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it 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 ([`PoolFormerConfig`]): 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'''

lowerCamelCase      :       List[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            [`PoolFormerImageProcessor.__call__`] for details.\n'''



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


      def __init__(						self       ,    __UpperCamelCase						)->						int:
           super().__init__(__A						)
           __lowerCAmelCase             =       config

           __lowerCAmelCase             =       PoolFormerEncoder(__A						)

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


      def 			__UpperCAmelCase		(						self						)->						Union[str, 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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    )->						Union[Tuple, BaseModelOutputWithNoAttention]:
           __lowerCAmelCase             =       (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           __lowerCAmelCase             =       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             =       self.encoder(
               __A       ,    output_hidden_states=__A       ,    return_dict=__A       ,    )
           __lowerCAmelCase             =       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 				_UpperCamelCase      (nn.Module      ):


      def __init__(						self       ,    __UpperCamelCase						)->						Tuple:
           super().__init__()
           __lowerCAmelCase             =       nn.Linear(config.hidden_size       ,    config.hidden_size						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						int:
           __lowerCAmelCase             =       self.dense(__A						)
           return output



@add_start_docstrings(
    """\n    PoolFormer Model transformer with an image classification head on top\n    """				,		UpperCamelCase__				,		)
class 				_UpperCamelCase      (UpperCamelCase__      ):


      def __init__(						self       ,    __UpperCamelCase						)->						int:
           super().__init__(__A						)
           __lowerCAmelCase             =       config.num_labels
           __lowerCAmelCase             =       PoolFormerModel(__A						)

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

           __lowerCAmelCase             =       self.poolformer(
               __A       ,    output_hidden_states=__A       ,    return_dict=__A       ,    )

           __lowerCAmelCase             =       outputs[0]

           __lowerCAmelCase             =       self.classifier(self.norm(__A						).mean([-2, -1]						)						)

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

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

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

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













def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int = 600_851_475_143     ):

		try:
				SCREAMING_SNAKE_CASE__								=   int(UpperCamelCase__     )
		except (TypeError, ValueError):
				raise TypeError("""Parameter n must be int or castable to int."""     )
		if n <= 0:
				raise ValueError("""Parameter n must be greater than or equal to one."""     )
		SCREAMING_SNAKE_CASE__								=   1
		SCREAMING_SNAKE_CASE__								=   2
		while i * i <= n:
				while n % i == 0:
						SCREAMING_SNAKE_CASE__								=   i
						n //= i
				i += 1
		if n > 1:
				SCREAMING_SNAKE_CASE__								=   n
		return int(UpperCamelCase__     )


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