




from __future__ import annotations



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Optional[int]      ):
					"""simple docstring"""

					lowerCAmelCase__          =			0
					lowerCAmelCase__          =			len(__snake_case      ) - 1

					while i < j:
										if nums[i] + nums[j] == target:
															return [i, j]
										elif nums[i] + nums[j] < target:
															lowerCAmelCase__          =			i + 1
										else:
															lowerCAmelCase__          =			j - 1

					return []


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")






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


UpperCamelCase								=					logging.get_logger(__name__)

# General docstring
UpperCamelCase								=					'PoolFormerConfig'

# Base docstring
UpperCamelCase								=					'sail/poolformer_s12'
UpperCamelCase								=					[1, 512, 7, 7]

# Image classification docstring
UpperCamelCase								=					'sail/poolformer_s12'
UpperCamelCase								=					'tabby, tabby cat'

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



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: float = 0.0       ,    lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

     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(lowerCAmelCase_       ,    dtype=input.dtype       ,    device=input.device      )
     random_tensor.floor_()  # binarize
     lowerCAmelCase__          =			input.div(lowerCAmelCase_      ) * random_tensor
     return output





class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[float] = None       )				->	None:
         super().__init__()
         lowerCAmelCase__          =			drop_prob


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					torch.Tensor       )				->	torch.Tensor:
         return drop_path(SCREAMING_SNAKE_CASE__       ,     self.drop_prob       ,     self.training       )


    def        a				(	self					:					Optional[Any]       )				->	str:
         return "p={}".format(self.drop_prob       )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str=None       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			patch_size if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (patch_size, patch_size)
         lowerCAmelCase__          =			stride if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (stride, stride)
         lowerCAmelCase__          =			padding if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (padding, padding)

         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     kernel_size=SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			norm_layer(SCREAMING_SNAKE_CASE__       ) if norm_layer else nn.Identity()


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.projection(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.norm(SCREAMING_SNAKE_CASE__       )
         return embeddings






class 			__lowerCamelCase	(   nn.GroupNorm   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Dict:
         super().__init__(1       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
         super().__init__()
         lowerCAmelCase__          =			nn.AvgPoolad(SCREAMING_SNAKE_CASE__       ,     stride=1       ,     padding=pool_size // 2       ,     count_include_pad=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[Any]:
         return self.pool(SCREAMING_SNAKE_CASE__       ) - hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Dict:
         super().__init__()
         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     1       )
         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     1       )
         lowerCAmelCase__          =			PoolFormerDropPath(SCREAMING_SNAKE_CASE__       )
         if isinstance(config.hidden_act       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			ACTaFN[config.hidden_act]
         else:
              lowerCAmelCase__          =			config.hidden_act


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	List[Any]:
         lowerCAmelCase__          =			self.conva(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.act_fn(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.drop(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conva(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.drop(SCREAMING_SNAKE_CASE__       )

         return hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			PoolFormerPooling(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerOutput(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__       )

         # Useful for training neural nets
         lowerCAmelCase__          =			PoolFormerDropPath(SCREAMING_SNAKE_CASE__       ) 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((SCREAMING_SNAKE_CASE__)       )       ,     requires_grad=SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			nn.Parameter(
                  config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__)       )       ,     requires_grad=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	int:
         if self.use_layer_scale:
              lowerCAmelCase__          =			self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__       )       )
              lowerCAmelCase__          =			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * pooling_output
              # First residual connection
              lowerCAmelCase__          =			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			()

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

              lowerCAmelCase__          =			(output,) + outputs
              return outputs

         else:
              lowerCAmelCase__          =			self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__       )       )       )
              # 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(SCREAMING_SNAKE_CASE__       )       )       )
              lowerCAmelCase__          =			hidden_states + layer_output

              lowerCAmelCase__          =			(output,) + outputs
              return outputs






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	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(SCREAMING_SNAKE_CASE__       )

         # 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(
                           SCREAMING_SNAKE_CASE__       ,     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(SCREAMING_SNAKE_CASE__       )       )

         lowerCAmelCase__          =			nn.ModuleList(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]=True       )				->	Dict:
         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(SCREAMING_SNAKE_CASE__       )
              # Send the embeddings through the blocks
              for _, blk in enumerate(SCREAMING_SNAKE_CASE__       ):
                   lowerCAmelCase__          =			blk(SCREAMING_SNAKE_CASE__       )
                   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=SCREAMING_SNAKE_CASE__       ,     hidden_states=SCREAMING_SNAKE_CASE__       )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				PoolFormerConfig
    snake_case__ =				"poolformer"
    snake_case__ =				"pixel_values"
    snake_case__ =				True


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     (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(SCREAMING_SNAKE_CASE__       ,     nn.LayerNorm       ):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=False       )				->	Tuple:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			value






UpperCamelCase								=					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'

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            [`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 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config

         lowerCAmelCase__          =			PoolFormerEncoder(SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					Optional[int]       )				->	Optional[Any]:
         return self.embeddings.patch_embeddings


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__       )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC       ,     output_type=SCREAMING_SNAKE_CASE__       ,     config_class=_CONFIG_FOR_DOC       ,     modality="vision"       ,     expected_output=_EXPECTED_OUTPUT_SHAPE       ,     )
    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = 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(
             SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			encoder_outputs[0]

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

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






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         super().__init__()
         lowerCAmelCase__          =			nn.Linear(config.hidden_size       ,     config.hidden_size       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	int:
         lowerCAmelCase__          =			self.dense(SCREAMING_SNAKE_CASE__       )
         return output






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




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[int]:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config.num_labels
         lowerCAmelCase__          =			PoolFormerModel(SCREAMING_SNAKE_CASE__       )

         # 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(SCREAMING_SNAKE_CASE__       )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT       ,     output_type=SCREAMING_SNAKE_CASE__       ,     config_class=_CONFIG_FOR_DOC       ,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,     )
    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.LongTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     )				->	Union[Tuple, ImageClassifierOutputWithNoAttention]:
         lowerCAmelCase__          =			return_dict if return_dict is not None else self.config.use_return_dict

         lowerCAmelCase__          =			self.poolformer(
             SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       ,     )

         lowerCAmelCase__          =			outputs[0]

         lowerCAmelCase__          =			self.classifier(self.norm(SCREAMING_SNAKE_CASE__       ).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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
              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(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

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

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