"""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


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

# General docstring
__lowerCAmelCase     :				List[str]    				=    "PoolFormerConfig"

# Base docstring
__lowerCAmelCase     :				Optional[int]    				=    "sail/poolformer_s12"
__lowerCAmelCase     :				Any    				=    [1, 5_12, 7, 7]

# Image classification docstring
__lowerCAmelCase     :				List[str]    				=    "sail/poolformer_s12"
__lowerCAmelCase     :				Dict    				=    "tabby, tabby cat"

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__ = 0.0      ,	lowerCamelCase__ = 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    a_   (  nn.Module      ):




					def __init__(						self    :  Union[str, Any]	,    snake_case__    :  Optional[float] = None  ):
							super().__init__()
							lowerCAmelCase__   = drop_prob




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  torch.Tensor  ):
							return drop_path(snake_case__	,    self.drop_prob	,    self.training  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							return "p={}".format(self.drop_prob  )

class    a_   (  nn.Module      ):




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

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  Optional[Any]  ):
							lowerCAmelCase__   = self.projection(snake_case__  )
							lowerCAmelCase__   = self.norm(snake_case__  )
							return embeddings

class    a_   (  nn.GroupNorm      ):




					def __init__(						self    :  Dict	,    snake_case__    :  Union[str, Any]	,    **snake_case__    :  List[str]  ):
							super().__init__(1	,    snake_case__	,    **snake_case__  )

class    a_   (  nn.Module      ):




					def __init__(						self    :  Tuple	,    snake_case__    :  Dict  ):
							super().__init__()
							lowerCAmelCase__   = nn.AvgPoolad(snake_case__	,    stride=1	,    padding=pool_size // 2	,    count_include_pad=snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Dict  ):
							return self.pool(snake_case__  ) - hidden_states

class    a_   (  nn.Module      ):




					def __init__(						self    :  str	,    snake_case__    :  List[str]	,    snake_case__    :  List[str]	,    snake_case__    :  List[Any]	,    snake_case__    :  Optional[Any]  ):
							super().__init__()
							lowerCAmelCase__   = nn.Convad(snake_case__	,    snake_case__	,    1  )
							lowerCAmelCase__   = nn.Convad(snake_case__	,    snake_case__	,    1  )
							lowerCAmelCase__   = PoolFormerDropPath(snake_case__  )
							if isinstance(config.hidden_act	,    snake_case__  ):
									lowerCAmelCase__   = ACTaFN[config.hidden_act]
							else:
									lowerCAmelCase__   = config.hidden_act




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  Optional[int]  ):
							lowerCAmelCase__   = self.conva(snake_case__  )
							lowerCAmelCase__   = self.act_fn(snake_case__  )
							lowerCAmelCase__   = self.drop(snake_case__  )
							lowerCAmelCase__   = self.conva(snake_case__  )
							lowerCAmelCase__   = self.drop(snake_case__  )

							return hidden_states

class    a_   (  nn.Module      ):




					def __init__(						self    :  Tuple	,    snake_case__    :  List[str]	,    snake_case__    :  Tuple	,    snake_case__    :  Optional[Any]	,    snake_case__    :  Dict	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  List[Any]  ):
							super().__init__()
							lowerCAmelCase__   = PoolFormerPooling(snake_case__  )
							lowerCAmelCase__   = PoolFormerOutput(snake_case__	,    snake_case__	,    snake_case__	,    snake_case__  )
							lowerCAmelCase__   = PoolFormerGroupNorm(snake_case__  )
							lowerCAmelCase__   = PoolFormerGroupNorm(snake_case__  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Union[str, Any]  ):
							if self.use_layer_scale:
									lowerCAmelCase__   = self.pooling(self.before_norm(snake_case__  )  )
									lowerCAmelCase__   = self.layer_scale_a.unsqueeze(-1  ).unsqueeze(-1  ) * pooling_output
									# First residual connection
									lowerCAmelCase__   = hidden_states + self.drop_path(snake_case__  )
									lowerCAmelCase__   = ()

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

									lowerCAmelCase__   = (output,) + outputs
									return outputs

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

									lowerCAmelCase__   = (output,) + outputs
									return outputs

class    a_   (  nn.Module      ):




					def __init__(						self    :  Optional[int]	,    snake_case__    :  Dict  ):
							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(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(
											        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(snake_case__  )  )

							lowerCAmelCase__   = nn.ModuleList(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  str	,    snake_case__    :  List[str]=False	,    snake_case__    :  List[Any]=True  ):
							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(snake_case__  )
									# Send the embeddings through the blocks
									for _, blk in enumerate(snake_case__  ):
											lowerCAmelCase__   = blk(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=snake_case__	,    hidden_states=snake_case__  )

class    a_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						str      =     PoolFormerConfig
					UpperCamelCase_			:						str      =     "poolformer"
					UpperCamelCase_			:						Optional[int]      =     "pixel_values"
					UpperCamelCase_			:						Optional[Any]      =     True




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Optional[Any]  ):
							if isinstance(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(snake_case__	,    nn.LayerNorm  ):
									module.bias.data.zero_()
									module.weight.data.fill_(1.0  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  List[str]=False  ):
							if isinstance(snake_case__	,    snake_case__  ):
									lowerCAmelCase__   = value
__lowerCAmelCase     :				str    				=    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     :				Optional[int]    				=    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    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  Optional[Any]	,    snake_case__    :  Optional[Any]  ):
							super().__init__(snake_case__  )
							lowerCAmelCase__   = config

							lowerCAmelCase__   = PoolFormerEncoder(snake_case__  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							return self.embeddings.patch_embeddings




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

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

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

class    a_   (  nn.Module      ):




					def __init__(						self    :  Tuple	,    snake_case__    :  Optional[int]  ):
							super().__init__()
							lowerCAmelCase__   = nn.Linear(config.hidden_size	,    config.hidden_size  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  List[Any]  ):
							lowerCAmelCase__   = self.dense(snake_case__  )
							return output

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




					def __init__(						self    :  Dict	,    snake_case__    :  int  ):
							super().__init__(snake_case__  )
							lowerCAmelCase__   = config.num_labels
							lowerCAmelCase__   = PoolFormerModel(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(snake_case__  )
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT	,    output_type=snake_case__	,    config_class=_CONFIG_FOR_DOC	,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,    )
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[torch.LongTensor] = None	,    snake_case__    :  Optional[bool] = None	,    snake_case__    :  Optional[bool] = None	,    ):
							lowerCAmelCase__   = return_dict if return_dict is not None else self.config.use_return_dict

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

							lowerCAmelCase__   = outputs[0]

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

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

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





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return " ".join(
		    """""".join(word[::-1]  ) if len(lowerCamelCase__  ) > 4 else word for word in sentence.split()  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(reverse_long_words("Hey wollef sroirraw"))




