





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a__					:			Tuple          =     {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			List[str]          =     ['''EncoderDecoderModel''']

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			int          =     ['''TFEncoderDecoderModel''']

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			str          =     ['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
			from .configuration_encoder_decoder import EncoderDecoderConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_encoder_decoder import EncoderDecoderModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
			import sys

			a__					:			Optional[int]          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










'''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


a__					:			Dict          =     logging.get_logger(__name__)

# General docstring
a__					:			Any          =     '''PoolFormerConfig'''

# Base docstring
a__					:			List[str]          =     '''sail/poolformer_s12'''
a__					:			Tuple          =     [1, 512, 7, 7]

# Image classification docstring
a__					:			Dict          =     '''sail/poolformer_s12'''
a__					:			Optional[Any]          =     '''tabby, tabby cat'''

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




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






class 				__snake_case     (					nn.Module							):





	def __init__(		self      ,       UpperCamelCase_ = None		)      ->		None:
		super().__init__()
		snake_case__	       =   drop_prob





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		torch.Tensor:
		return drop_path(UpperCamelCase_      ,       self.drop_prob      ,       self.training		)





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






class 				__snake_case     (					nn.Module							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		Optional[Any]:
		super().__init__()
		snake_case__	       =   patch_size if isinstance(UpperCamelCase_      ,       collections.abc.Iterable		) else (patch_size, patch_size)
		snake_case__	       =   stride if isinstance(UpperCamelCase_      ,       collections.abc.Iterable		) else (stride, stride)
		snake_case__	       =   padding if isinstance(UpperCamelCase_      ,       collections.abc.Iterable		) else (padding, padding)

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





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		int:
		snake_case__	       =   self.projection(UpperCamelCase_		)
		snake_case__	       =   self.norm(UpperCamelCase_		)
		return embeddings






class 				__snake_case     (					nn.GroupNorm							):





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






class 				__snake_case     (					nn.Module							):





	def __init__(		self      ,       UpperCamelCase_		)      ->		int:
		super().__init__()
		snake_case__	       =   nn.AvgPoolad(UpperCamelCase_      ,       stride=1      ,       padding=pool_size // 2      ,       count_include_pad=UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		str:
		return self.pool(UpperCamelCase_		) - hidden_states






class 				__snake_case     (					nn.Module							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()
		snake_case__	       =   nn.Convad(UpperCamelCase_      ,       UpperCamelCase_      ,       1		)
		snake_case__	       =   nn.Convad(UpperCamelCase_      ,       UpperCamelCase_      ,       1		)
		snake_case__	       =   PoolFormerDropPath(UpperCamelCase_		)
		if isinstance(config.hidden_act      ,       UpperCamelCase_		):
			snake_case__	       =   ACTaFN[config.hidden_act]
		else:
			snake_case__	       =   config.hidden_act





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   self.conva(UpperCamelCase_		)
		snake_case__	       =   self.act_fn(UpperCamelCase_		)
		snake_case__	       =   self.drop(UpperCamelCase_		)
		snake_case__	       =   self.conva(UpperCamelCase_		)
		snake_case__	       =   self.drop(UpperCamelCase_		)

		return hidden_states






class 				__snake_case     (					nn.Module							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Tuple:
		super().__init__()
		snake_case__	       =   PoolFormerPooling(UpperCamelCase_		)
		snake_case__	       =   PoolFormerOutput(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)
		snake_case__	       =   PoolFormerGroupNorm(UpperCamelCase_		)
		snake_case__	       =   PoolFormerGroupNorm(UpperCamelCase_		)

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





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

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

			snake_case__	       =   (output,) + outputs
			return outputs

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

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

			snake_case__	       =   (output,) + outputs
			return outputs






class 				__snake_case     (					nn.Module							):





	def __init__(		self      ,       UpperCamelCase_		)      ->		Dict:
		super().__init__()
		snake_case__	       =   config
		# stochastic depth decay rule
		snake_case__	       =   [x.item() for x in torch.linspace(0      ,       config.drop_path_rate      ,       sum(config.depths		)		)]

		# patch embeddings
		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]      ,       )		)
		snake_case__	       =   nn.ModuleList(UpperCamelCase_		)

		# Transformer blocks
		snake_case__	       =   []
		snake_case__	       =   0
		for i in range(config.num_encoder_blocks		):
			# each block consists of layers
			snake_case__	       =   []
			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_		)		)

		snake_case__	       =   nn.ModuleList(UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=False      ,       UpperCamelCase_=True		)      ->		Dict:
		snake_case__	       =   () if output_hidden_states else None

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

			if output_hidden_states:
				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=UpperCamelCase_      ,       hidden_states=UpperCamelCase_		)






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  PoolFormerConfig
	__lowerCAmelCase       						=  '''poolformer'''
	__lowerCAmelCase       						=  '''pixel_values'''
	__lowerCAmelCase       						=  True





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		str:
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=False		)      ->		int:
		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		):
			snake_case__	       =   value





a__					:			int          =     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.
'''

a__					:			Union[str, Any]          =     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.'''       ,       __magic_name__       ,       )
class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_		)      ->		Any:
		super().__init__(UpperCamelCase_		)
		snake_case__	       =   config

		snake_case__	       =   PoolFormerEncoder(UpperCamelCase_		)

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





	def   _snake_case   (		self		)      ->		str:
		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   _snake_case   (		self      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       )      ->		Union[Tuple, BaseModelOutputWithNoAttention]:
		snake_case__	       =   (
		    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
		)
		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'		)

		snake_case__	       =   self.encoder(
		    UpperCamelCase_      ,       output_hidden_states=UpperCamelCase_      ,       return_dict=UpperCamelCase_      ,       )
		snake_case__	       =   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 				__snake_case     (					nn.Module							):





	def __init__(		self      ,       UpperCamelCase_		)      ->		str:
		super().__init__()
		snake_case__	       =   nn.Linear(config.hidden_size      ,       config.hidden_size		)





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   self.dense(UpperCamelCase_		)
		return output






@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''       ,       __magic_name__       ,       )
class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_		)      ->		Optional[Any]:
		super().__init__(UpperCamelCase_		)
		snake_case__	       =   config.num_labels
		snake_case__	       =   PoolFormerModel(UpperCamelCase_		)

		# Final norm
		snake_case__	       =   PoolFormerGroupNorm(config.hidden_sizes[-1]		)
		# Classifier head
		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(UpperCamelCase_		)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,       output_type=UpperCamelCase_      ,       config_class=_CONFIG_FOR_DOC      ,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,       )
	def   _snake_case   (		self      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       )      ->		Union[Tuple, ImageClassifierOutputWithNoAttention]:
		snake_case__	       =   return_dict if return_dict is not None else self.config.use_return_dict

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

		snake_case__	       =   outputs[0]

		snake_case__	       =   self.classifier(self.norm(UpperCamelCase_		).mean([-2, -1]		)		)

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

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

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

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



