



def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase = " " )	->						list:
				a__	:							Optional[Any]      						= []

				a__	:							Any      						= 0
				for index, char in enumerate(__UpperCamelCase ):
								if char == separator:
												split_words.append(string[last_index:index] )
												a__	:							Tuple      						= index + 1
								elif index + 1 == len(__UpperCamelCase ):
												split_words.append(string[last_index : index + 1] )
				return split_words


if __name__ == "__main__":
						from doctest import testmod

						testmod()







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		       =      logging.get_logger(__name__)

# General docstring
lowerCamelCase		       =      """PoolFormerConfig"""

# Base docstring
lowerCamelCase		       =      """sail/poolformer_s12"""
lowerCamelCase		       =      [1, 5_12, 7, 7]

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

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






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


class 				_a						(       nn.Module			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase = None				):







											"""simple docstring"""



											super().__init__()
											a__	:							Optional[Any]      						= drop_prob



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											return drop_path(__UpperCAmelCase ,					self.drop_prob ,					self.training				)




							def 		_A	(							self				):







											"""simple docstring"""



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





class 				_a						(       nn.Module			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase=None				):







											"""simple docstring"""



											super().__init__()
											a__	:							Optional[Any]      						= patch_size if isinstance(__UpperCAmelCase ,					collections.abc.Iterable				) else (patch_size, patch_size)
											a__	:							List[str]      						= stride if isinstance(__UpperCAmelCase ,					collections.abc.Iterable				) else (stride, stride)
											a__	:							Union[str, Any]      						= padding if isinstance(__UpperCAmelCase ,					collections.abc.Iterable				) else (padding, padding)

											a__	:							int      						= nn.Convad(__UpperCAmelCase ,					__UpperCAmelCase ,					kernel_size=__UpperCAmelCase ,					stride=__UpperCAmelCase ,					padding=__UpperCAmelCase				)
											a__	:							Optional[Any]      						= norm_layer(__UpperCAmelCase				) if norm_layer else nn.Identity()




							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Dict      						= self.projection(__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= self.norm(__UpperCAmelCase				)
											return embeddings





class 				_a						(       nn.GroupNorm			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__(1 ,					__UpperCAmelCase ,					**__UpperCAmelCase				)





class 				_a						(       nn.Module			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__()
											a__	:							List[str]      						= nn.AvgPoolad(__UpperCAmelCase ,					stride=1 ,					padding=pool_size // 2 ,					count_include_pad=__UpperCAmelCase				)




							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											return self.pool(__UpperCAmelCase				) - hidden_states





class 				_a						(       nn.Module			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__()
											a__	:							str      						= nn.Convad(__UpperCAmelCase ,					__UpperCAmelCase ,					1				)
											a__	:							int      						= nn.Convad(__UpperCAmelCase ,					__UpperCAmelCase ,					1				)
											a__	:							Any      						= PoolFormerDropPath(__UpperCAmelCase				)
											if isinstance(config.hidden_act ,					__UpperCAmelCase				):
															a__	:							List[Any]      						= ACTaFN[config.hidden_act]
											else:
															a__	:							Union[str, Any]      						= config.hidden_act




							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.conva(__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= self.act_fn(__UpperCAmelCase				)
											a__	:							Dict      						= self.drop(__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= self.conva(__UpperCAmelCase				)
											a__	:							int      						= self.drop(__UpperCAmelCase				)

											return hidden_states





class 				_a						(       nn.Module			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__()
											a__	:							Any      						= PoolFormerPooling(__UpperCAmelCase				)
											a__	:							Any      						= PoolFormerOutput(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)
											a__	:							Any      						= PoolFormerGroupNorm(__UpperCAmelCase				)
											a__	:							Dict      						= PoolFormerGroupNorm(__UpperCAmelCase				)

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




							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											if self.use_layer_scale:
															a__	:							Any      						= self.pooling(self.before_norm(__UpperCAmelCase				)				)
															a__	:							int      						= self.layer_scale_a.unsqueeze(-1				).unsqueeze(-1				) * pooling_output
															# First residual connection
															a__	:							Optional[int]      						= hidden_states + self.drop_path(__UpperCAmelCase				)
															a__	:							Dict      						= ()

															a__	:							List[Any]      						= self.output(self.after_norm(__UpperCAmelCase				)				)
															a__	:							Union[str, Any]      						= self.layer_scale_a.unsqueeze(-1				).unsqueeze(-1				) * layer_output
															# Second residual connection
															a__	:							Optional[Any]      						= hidden_states + self.drop_path(__UpperCAmelCase				)

															a__	:							Optional[int]      						= (output,) + outputs
															return outputs

											else:
															a__	:							Optional[int]      						= self.drop_path(self.pooling(self.before_norm(__UpperCAmelCase				)				)				)
															# First residual connection
															a__	:							Tuple      						= pooling_output + hidden_states
															a__	:							Tuple      						= ()

															# Second residual connection inside the PoolFormerOutput block
															a__	:							Optional[int]      						= self.drop_path(self.output(self.after_norm(__UpperCAmelCase				)				)				)
															a__	:							str      						= hidden_states + layer_output

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





class 				_a						(       nn.Module			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__()
											a__	:							Any      						= config
											# stochastic depth decay rule
											a__	:							List[str]      						= [x.item() for x in torch.linspace(0 ,					config.drop_path_rate ,					sum(config.depths				)				)]

											# patch embeddings
											a__	:							List[Any]      						= []
											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] ,					)				)
											a__	:							Any      						= nn.ModuleList(__UpperCAmelCase				)

											# Transformer blocks
											a__	:							Optional[int]      						= []
											a__	:							List[Any]      						= 0
											for i in range(config.num_encoder_blocks				):
															# each block consists of layers
															a__	:							str      						= []
															if i != 0:
																			cur += config.depths[i - 1]
															for j in range(config.depths[i]				):
																			layers.append(
																			    PoolFormerLayer(
																			        __UpperCAmelCase ,					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(__UpperCAmelCase				)				)

											a__	:							Any      						= nn.ModuleList(__UpperCAmelCase				)




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=False ,					__UpperCAmelCase=True				):







											"""simple docstring"""



											a__	:							int      						= () if output_hidden_states else None

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

															if output_hidden_states:
																			a__	:							Optional[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=__UpperCAmelCase ,					hidden_states=__UpperCAmelCase				)





class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:Optional[int] =       PoolFormerConfig
							A				:List[str] =       "poolformer"
							A				:Tuple =       "pixel_values"
							A				:List[str] =       True



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											if isinstance(__UpperCAmelCase ,					(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(__UpperCAmelCase ,					nn.LayerNorm				):
															module.bias.data.zero_()
															module.weight.data.fill_(1.0				)




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=False				):







											"""simple docstring"""



											if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				):
															a__	:							Dict      						= value





lowerCamelCase		       =      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.
"""

lowerCamelCase		       =      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."       ,  SCREAMING_SNAKE_CASE       ,  )
class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= config

											a__	:							int      						= PoolFormerEncoder(__UpperCAmelCase				)

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



							def 		_A	(							self				):







											"""simple docstring"""



											return self.embeddings.patch_embeddings




							@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 		_A	(							self ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					):







											"""simple docstring"""



											a__	:							int      						= (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											a__	:							str      						= 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"				)

											a__	:							List[Any]      						= self.encoder(
											    __UpperCAmelCase ,					output_hidden_states=__UpperCAmelCase ,					return_dict=__UpperCAmelCase ,					)
											a__	:							Any      						= encoder_outputs[0]

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

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





class 				_a						(       nn.Module			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__()
											a__	:							Any      						= nn.Linear(config.hidden_size ,					config.hidden_size				)




							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= self.dense(__UpperCAmelCase				)
											return output





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





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= config.num_labels
											a__	:							int      						= PoolFormerModel(__UpperCAmelCase				)

											# Final norm
											a__	:							Any      						= PoolFormerGroupNorm(config.hidden_sizes[-1]				)
											# Classifier head
											a__	:							Dict      						= (
											    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 		_A	(							self ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					):







											"""simple docstring"""



											a__	:							str      						= return_dict if return_dict is not None else self.config.use_return_dict

											a__	:							str      						= self.poolformer(
											    __UpperCAmelCase ,					output_hidden_states=__UpperCAmelCase ,					return_dict=__UpperCAmelCase ,					)

											a__	:							Optional[Any]      						= outputs[0]

											a__	:							Union[str, Any]      						= self.classifier(self.norm(__UpperCAmelCase				).mean([-2, -1]				)				)

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

															if self.config.problem_type == "regression":
																			a__	:							int      						= MSELoss()
																			if self.num_labels == 1:
																							a__	:							str      						= loss_fct(logits.squeeze() ,					labels.squeeze()				)
																			else:
																							a__	:							Union[str, Any]      						= loss_fct(__UpperCAmelCase ,					__UpperCAmelCase				)
															elif self.config.problem_type == "single_label_classification":
																			a__	:							Any      						= CrossEntropyLoss()
																			a__	:							Optional[Any]      						= loss_fct(logits.view(-1 ,					self.num_labels				) ,					labels.view(-1				)				)
															elif self.config.problem_type == "multi_label_classification":
																			a__	:							Union[str, Any]      						= BCEWithLogitsLoss()
																			a__	:							Dict      						= loss_fct(__UpperCAmelCase ,					__UpperCAmelCase				)

											if not return_dict:
															a__	:							List[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				)


