


'''simple docstring'''






from collections.abc import Iterable
from typing import Generic, TypeVar

__UpperCAmelCase					       =							TypeVar('''_T''')




class 	a__       (							Generic[_T]						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_ = None     ) ->							None:
									lowerCAmelCase__      =			list(iterable or []     )
									lowerCAmelCase__      =			[]







							def __len__(	self     ) ->							int:
									return len(self._stacka     ) + len(self._stacka     )







							def __repr__(	self     ) ->							str:
									return F"""Queue({tuple(self._stacka[::-1] + self._stacka     )})"""







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							None:
									self._stacka.append(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							_T:
									lowerCAmelCase__      =			self._stacka.pop
									lowerCAmelCase__      =			self._stacka.append

									if not self._stacka:
											while self._stacka:
													stacka_append(stacka_pop()     )

									if not self._stacka:
											raise IndexError('''Queue is empty'''     )
									return self._stacka.pop()


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

		testmod()



'''simple docstring'''






from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


__UpperCAmelCase					       =							logging.get_logger(__name__)

# General docstring
__UpperCAmelCase					       =							'''RegNetConfig'''

# Base docstring
__UpperCAmelCase					       =							'''facebook/regnet-y-040'''
__UpperCAmelCase					       =							[1, 1_088, 7, 7]

# Image classification docstring
__UpperCAmelCase					       =							'''facebook/regnet-y-040'''
__UpperCAmelCase					       =							'''tabby, tabby cat'''

__UpperCAmelCase					       =							[
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = 3      ,					lowerCamelCase_ = 1      ,					lowerCamelCase_ = 1      ,					lowerCamelCase_ = "relu"      ,					) ->							int:
														super().__init__()
														lowerCAmelCase__      =			nn.Convad(
														    lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=lowerCamelCase_      ,					stride=lowerCamelCase_      ,					padding=kernel_size // 2      ,					groups=lowerCamelCase_      ,					bias=lowerCamelCase_      ,					)
														lowerCAmelCase__      =			nn.BatchNormad(lowerCamelCase_     )
														lowerCAmelCase__      =			ACTaFN[activation] if activation is not None else nn.Identity()







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Dict:
														lowerCAmelCase__      =			self.convolution(lowerCamelCase_     )
														lowerCAmelCase__      =			self.normalization(lowerCamelCase_     )
														lowerCAmelCase__      =			self.activation(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Optional[Any]:
														super().__init__()
														lowerCAmelCase__      =			RegNetConvLayer(
														    config.num_channels      ,					config.embedding_size      ,					kernel_size=3      ,					stride=2      ,					activation=config.hidden_act     )
														lowerCAmelCase__      =			config.num_channels







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							int:
														lowerCAmelCase__      =			pixel_values.shape[1]
														if num_channels != self.num_channels:
																					raise ValueError(
																					    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''     )
														lowerCAmelCase__      =			self.embedder(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = 2     ) ->							Any:
														super().__init__()
														lowerCAmelCase__      =			nn.Convad(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1      ,					stride=lowerCamelCase_      ,					bias=lowerCamelCase_     )
														lowerCAmelCase__      =			nn.BatchNormad(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Tensor:
														lowerCAmelCase__      =			self.convolution(lowerCamelCase_     )
														lowerCAmelCase__      =			self.normalization(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							List[Any]:
														super().__init__()

														lowerCAmelCase__      =			nn.AdaptiveAvgPoolad((1, 1)     )
														lowerCAmelCase__      =			nn.Sequential(
														    nn.Convad(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1     )      ,					nn.ReLU()      ,					nn.Convad(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1     )      ,					nn.Sigmoid()      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							int:
														# b c h w -> b c 1 1
														lowerCAmelCase__      =			self.pooler(lowerCamelCase_     )
														lowerCAmelCase__      =			self.attention(lowerCamelCase_     )
														lowerCAmelCase__      =			hidden_state * attention
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = 1     ) ->							Optional[int]:
														super().__init__()
														lowerCAmelCase__      =			in_channels != out_channels or stride != 1
														lowerCAmelCase__      =			max(1      ,					out_channels // config.groups_width     )
														lowerCAmelCase__      =			(
														    RegNetShortCut(lowerCamelCase_      ,					lowerCamelCase_      ,					stride=lowerCamelCase_     ) if should_apply_shortcut else nn.Identity()
														)
														lowerCAmelCase__      =			nn.Sequential(
														    RegNetConvLayer(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1      ,					activation=config.hidden_act     )      ,					RegNetConvLayer(lowerCamelCase_      ,					lowerCamelCase_      ,					stride=lowerCamelCase_      ,					groups=lowerCamelCase_      ,					activation=config.hidden_act     )      ,					RegNetConvLayer(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1      ,					activation=lowerCamelCase_     )      ,					)
														lowerCAmelCase__      =			ACTaFN[config.hidden_act]







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__      =			hidden_state
														lowerCAmelCase__      =			self.layer(lowerCamelCase_     )
														lowerCAmelCase__      =			self.shortcut(lowerCamelCase_     )
														hidden_state += residual
														lowerCAmelCase__      =			self.activation(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = 1     ) ->							Optional[int]:
														super().__init__()
														lowerCAmelCase__      =			in_channels != out_channels or stride != 1
														lowerCAmelCase__      =			max(1      ,					out_channels // config.groups_width     )
														lowerCAmelCase__      =			(
														    RegNetShortCut(lowerCamelCase_      ,					lowerCamelCase_      ,					stride=lowerCamelCase_     ) if should_apply_shortcut else nn.Identity()
														)
														lowerCAmelCase__      =			nn.Sequential(
														    RegNetConvLayer(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1      ,					activation=config.hidden_act     )      ,					RegNetConvLayer(lowerCamelCase_      ,					lowerCamelCase_      ,					stride=lowerCamelCase_      ,					groups=lowerCamelCase_      ,					activation=config.hidden_act     )      ,					RegNetSELayer(lowerCamelCase_      ,					reduced_channels=int(round(in_channels / 4     )     )     )      ,					RegNetConvLayer(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1      ,					activation=lowerCamelCase_     )      ,					)
														lowerCAmelCase__      =			ACTaFN[config.hidden_act]







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			hidden_state
														lowerCAmelCase__      =			self.layer(lowerCamelCase_     )
														lowerCAmelCase__      =			self.shortcut(lowerCamelCase_     )
														hidden_state += residual
														lowerCAmelCase__      =			self.activation(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = 2      ,					lowerCamelCase_ = 2      ,					) ->							Dict:
														super().__init__()

														lowerCAmelCase__      =			RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

														lowerCAmelCase__      =			nn.Sequential(
														    # downsampling is done in the first layer with stride of 2
														    layer(
														        lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					stride=lowerCamelCase_      ,					)      ,					*[layer(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) for _ in range(depth - 1     )]      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__      =			self.layers(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Optional[int]:
														super().__init__()
														lowerCAmelCase__      =			nn.ModuleList([]     )
														# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
														self.stages.append(
														    RegNetStage(
														        lowerCamelCase_      ,					config.embedding_size      ,					config.hidden_sizes[0]      ,					stride=2 if config.downsample_in_first_stage else 1      ,					depth=config.depths[0]      ,					)     )
														lowerCAmelCase__      =			zip(config.hidden_sizes      ,					config.hidden_sizes[1:]     )
														for (in_channels, out_channels), depth in zip(lowerCamelCase_      ,					config.depths[1:]     ):
																					self.stages.append(RegNetStage(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					depth=lowerCamelCase_     )     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = False      ,					lowerCamelCase_ = True     ) ->							BaseModelOutputWithNoAttention:
														lowerCAmelCase__      =			() if output_hidden_states else None

														for stage_module in self.stages:
																					if output_hidden_states:
																												lowerCAmelCase__      =			hidden_states + (hidden_state,)

																					lowerCAmelCase__      =			stage_module(lowerCamelCase_     )

														if output_hidden_states:
																					lowerCAmelCase__      =			hidden_states + (hidden_state,)

														if not return_dict:
																					return tuple(v for v in [hidden_state, hidden_states] if v is not None     )

														return BaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase_      ,					hidden_states=lowerCamelCase_     )




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     List[Any]					=   RegNetConfig
							lowercase__    :     Tuple					=   "regnet"
							lowercase__    :     List[str]					=   "pixel_values"
							lowercase__    :     Tuple					=   True







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							int:
														if isinstance(lowerCamelCase_      ,					nn.Convad     ):
																					nn.init.kaiming_normal_(module.weight      ,					mode='''fan_out'''      ,					nonlinearity='''relu'''     )
														elif isinstance(lowerCamelCase_      ,					(nn.BatchNormad, nn.GroupNorm)     ):
																					nn.init.constant_(module.weight      ,					1     )
																					nn.init.constant_(module.bias      ,					0     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=False     ) ->							int:
														if isinstance(lowerCamelCase_      ,					lowerCamelCase_     ):
																					lowerCAmelCase__      =			value

__UpperCAmelCase					       =							R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): 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.
'''

__UpperCAmelCase					       =							R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''




@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."       ,       a__       ,       )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Optional[int]:
														super().__init__(lowerCamelCase_     )
														lowerCAmelCase__      =			config
														lowerCAmelCase__      =			RegNetEmbeddings(lowerCamelCase_     )
														lowerCAmelCase__      =			RegNetEncoder(lowerCamelCase_     )
														lowerCAmelCase__      =			nn.AdaptiveAvgPoolad((1, 1)     )
														# Initialize weights and apply final processing
														self.post_init()







							@add_start_docstrings_to_model_forward(lowerCamelCase_     )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC      ,					output_type=lowerCamelCase_      ,					config_class=_CONFIG_FOR_DOC      ,					modality='''vision'''      ,					expected_output=_EXPECTED_OUTPUT_SHAPE      ,					)
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None     ) ->							BaseModelOutputWithPoolingAndNoAttention:
														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

														lowerCAmelCase__      =			self.embedder(lowerCamelCase_     )

														lowerCAmelCase__      =			self.encoder(
														    lowerCamelCase_      ,					output_hidden_states=lowerCamelCase_      ,					return_dict=lowerCamelCase_     )

														lowerCAmelCase__      =			encoder_outputs[0]

														lowerCAmelCase__      =			self.pooler(lowerCamelCase_     )

														if not return_dict:
																					return (last_hidden_state, pooled_output) + encoder_outputs[1:]

														return BaseModelOutputWithPoolingAndNoAttention(
														    last_hidden_state=lowerCamelCase_      ,					pooler_output=lowerCamelCase_      ,					hidden_states=encoder_outputs.hidden_states      ,					)




@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "       ,       a__       ,       )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Optional[Any]:
														super().__init__(lowerCamelCase_     )
														lowerCAmelCase__      =			config.num_labels
														lowerCAmelCase__      =			RegNetModel(lowerCamelCase_     )
														# classification head
														lowerCAmelCase__      =			nn.Sequential(
														    nn.Flatten()      ,					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(lowerCamelCase_     )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,					output_type=lowerCamelCase_      ,					config_class=_CONFIG_FOR_DOC      ,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,					)
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					) ->							ImageClassifierOutputWithNoAttention:
														lowerCAmelCase__      =			return_dict if return_dict is not None else self.config.use_return_dict

														lowerCAmelCase__      =			self.regnet(lowerCamelCase_      ,					output_hidden_states=lowerCamelCase_      ,					return_dict=lowerCamelCase_     )

														lowerCAmelCase__      =			outputs.pooler_output if return_dict else outputs[1]

														lowerCAmelCase__      =			self.classifier(lowerCamelCase_     )

														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(lowerCamelCase_      ,					lowerCamelCase_     )
																					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(lowerCamelCase_      ,					lowerCamelCase_     )

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

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