





from __future__ import annotations

a__    :		Any      						=     1_0

def     snake_case							(UpperCamelCase			:     list[int]							):




	'''simple docstring'''







	lowerCamelCase__										=			1
	lowerCamelCase__										=			max(UpperCamelCase__							)
	while placement <= max_digit:
		# declare and initialize empty buckets
		lowerCamelCase__										=			[[] for _ in range(UpperCamelCase__							)]
		# split list_of_ints between the buckets
		for i in list_of_ints:
			lowerCamelCase__										=			int((i / placement) % RADIX							)
			buckets[tmp].append(UpperCamelCase__							)
		# put each buckets' contents into list_of_ints
		lowerCamelCase__										=			0
		for b in range(UpperCamelCase__							):
			for i in buckets[b]:
				lowerCamelCase__										=			i
				a += 1
        # move to next
		placement *= RADIX
	return list_of_ints


if __name__ == "__main__":
					import doctest

					doctest.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, 512, 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__:				Optional[Any]       , UpperCamelCase__:				float = 0.0       , UpperCamelCase__:				bool = False     ):
		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(UpperCamelCase__       , dtype=input.dtype       , device=input.device     )
		random_tensor.floor_()  # binarize
		SCREAMING_SNAKE_CASE__								=   input.div(UpperCamelCase__     ) * random_tensor
		return output




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Optional[Any]				,					__A     :Optional[float] = None		) -> None:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   drop_prob


							def   _snake_case	(				self     :Any				,					__A     :torch.Tensor		) -> torch.Tensor:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Dict		) -> str:



									"""simple docstring"""



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




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Dict				,					__A     :Optional[Any]				,					__A     :Dict				,					__A     :List[str]				,					__A     :Optional[Any]				,					__A     :Tuple				,					__A     :Optional[Any]=None		) -> Union[str, Any]:



									"""simple docstring"""



									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     :Dict				,					__A     :Union[str, Any]		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.projection(__A		)
									SCREAMING_SNAKE_CASE__								=   self.norm(__A		)
									return embeddings




class 	UpperCamelCase_							(  nn.GroupNorm     ):


							def __init__(				self     :Dict				,					__A     :Tuple				,					**__A     :Union[str, Any]		) -> Dict:



									"""simple docstring"""



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




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :List[str]				,					__A     :Optional[int]		) -> Any:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   nn.AvgPoolad(__A				,					stride=1				,					padding=pool_size // 2				,					count_include_pad=__A		)


							def   _snake_case	(				self     :Any				,					__A     :Optional[Any]		) -> Optional[Any]:



									"""simple docstring"""



									return self.pool(__A		) - hidden_states




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Optional[Any]				,					__A     :Tuple				,					__A     :Dict				,					__A     :int				,					__A     :Any		) -> str:



									"""simple docstring"""



									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     :Union[str, Any]				,					__A     :Optional[int]		) -> Dict:



									"""simple docstring"""



									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 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Any				,					__A     :str				,					__A     :List[str]				,					__A     :Tuple				,					__A     :Dict				,					__A     :Union[str, Any]				,					__A     :int		) -> Optional[int]:



									"""simple docstring"""



									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     :Optional[Any]				,					__A     :Optional[int]		) -> str:



									"""simple docstring"""



									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 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Union[str, Any]				,					__A     :List[Any]		) -> Union[str, Any]:



									"""simple docstring"""



									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     :str				,					__A     :Tuple				,					__A     :Dict=False				,					__A     :Tuple=True		) -> Optional[int]:



									"""simple docstring"""



									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 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       PoolFormerConfig
							lowerCamelCase_							     =       "poolformer"
							lowerCamelCase_							     =       "pixel_values"
							lowerCamelCase_							     =       True


							def   _snake_case	(				self     :Optional[Any]				,					__A     :Tuple		) -> Dict:



									"""simple docstring"""



									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     :str				,					__A     :Optional[Any]				,					__A     :Union[str, Any]=False		) -> Any:



									"""simple docstring"""



									if isinstance(__A				,					__A		):
											SCREAMING_SNAKE_CASE__								=   value



_lowerCamelCase 					=      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 					=      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     :Union[str, Any]				,					__A     :Any		) -> int:



									"""simple docstring"""



									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     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									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     :Dict				,					__A     :Optional[torch.FloatTensor] = None				,					__A     :Optional[bool] = None				,					__A     :Optional[bool] = None				,					) -> Union[Tuple, BaseModelOutputWithNoAttention]:



									"""simple docstring"""



									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 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :int				,					__A     :Optional[int]		) -> Tuple:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   nn.Linear(config.hidden_size				,					config.hidden_size		)


							def   _snake_case	(				self     :List[Any]				,					__A     :Dict		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   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     :str				,					__A     :Union[str, Any]		) -> int:



									"""simple docstring"""



									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     :int				,					__A     :Optional[torch.FloatTensor] = None				,					__A     :Optional[torch.LongTensor] = None				,					__A     :Optional[bool] = None				,					__A     :Optional[bool] = None				,					) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:



									"""simple docstring"""



									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		)