



'''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


snake_case_ :	Any         =  logging.get_logger(__name__)

# General docstring
snake_case_ :	str         =  'RegNetConfig'

# Base docstring
snake_case_ :	Dict         =  'facebook/regnet-y-040'
snake_case_ :	Tuple         =  [1, 1088, 7, 7]

# Image classification docstring
snake_case_ :	Tuple         =  'facebook/regnet-y-040'
snake_case_ :	List[str]         =  'tabby, tabby cat'

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






class 							lowercase__	(							nn.Module   ):





	def __init__( self					:				List[Any] ,lowerCamelCase__					:				Tuple ,lowerCamelCase__					:				Optional[int] ,lowerCamelCase__					:				str = 3 ,lowerCamelCase__					:				List[str] = 1 ,lowerCamelCase__					:				str = 1 ,lowerCamelCase__					:				Any = "relu" ,):







							'''simple docstring'''
							super().__init__()
							_UpperCamelCase					:		Any 				=   nn.Convad(
							    __lowerCAmelCase ,__lowerCAmelCase ,kernel_size=__lowerCAmelCase ,stride=__lowerCAmelCase ,padding=kernel_size // 2 ,groups=__lowerCAmelCase ,bias=__lowerCAmelCase ,)
							_UpperCamelCase					:		int 				=   nn.BatchNormad(__lowerCAmelCase )
							_UpperCamelCase					:		Dict 				=   ACTaFN[activation] if activation is not None else nn.Identity()





	def 	UpperCamelCase_  ( self					:				str ,lowerCamelCase__					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   self.convolution(__lowerCAmelCase )
							_UpperCamelCase					:		List[Any] 				=   self.normalization(__lowerCAmelCase )
							_UpperCamelCase					:		Optional[int] 				=   self.activation(__lowerCAmelCase )
							return hidden_state






class 							lowercase__	(							nn.Module   ):





	def __init__( self					:				str ,lowerCamelCase__					:				Tuple ):







							'''simple docstring'''
							super().__init__()
							_UpperCamelCase					:		Optional[Any] 				=   RegNetConvLayer(
							    config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
							_UpperCamelCase					:		Optional[Any] 				=   config.num_channels





	def 	UpperCamelCase_  ( self					:				Any ,lowerCamelCase__					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   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.' )
							_UpperCamelCase					:		Union[str, Any] 				=   self.embedder(__lowerCAmelCase )
							return hidden_state






class 							lowercase__	(							nn.Module   ):





	def __init__( self					:				Union[str, Any] ,lowerCamelCase__					:				Union[str, Any] ,lowerCamelCase__					:				List[str] ,lowerCamelCase__					:				Any = 2 ):







							'''simple docstring'''
							super().__init__()
							_UpperCamelCase					:		Tuple 				=   nn.Convad(__lowerCAmelCase ,__lowerCAmelCase ,kernel_size=1 ,stride=__lowerCAmelCase ,bias=__lowerCAmelCase )
							_UpperCamelCase					:		Optional[int] 				=   nn.BatchNormad(__lowerCAmelCase )





	def 	UpperCamelCase_  ( self					:				Optional[Any] ,lowerCamelCase__					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   self.convolution(__lowerCAmelCase )
							_UpperCamelCase					:		Optional[Any] 				=   self.normalization(__lowerCAmelCase )
							return hidden_state






class 							lowercase__	(							nn.Module   ):





	def __init__( self					:				Any ,lowerCamelCase__					:				List[Any] ,lowerCamelCase__					:				Tuple ):







							'''simple docstring'''
							super().__init__()

							_UpperCamelCase					:		int 				=   nn.AdaptiveAvgPoolad((1, 1) )
							_UpperCamelCase					:		List[str] 				=   nn.Sequential(
							    nn.Convad(__lowerCAmelCase ,__lowerCAmelCase ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(__lowerCAmelCase ,__lowerCAmelCase ,kernel_size=1 ) ,nn.Sigmoid() ,)





	def 	UpperCamelCase_  ( self					:				List[str] ,lowerCamelCase__					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   self.pooler(__lowerCAmelCase )
							_UpperCamelCase					:		str 				=   self.attention(__lowerCAmelCase )
							_UpperCamelCase					:		Dict 				=   hidden_state * attention
							return hidden_state






class 							lowercase__	(							nn.Module   ):





	def __init__( self					:				Dict ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Dict ,lowerCamelCase__					:				int ,lowerCamelCase__					:				int = 1 ):







							'''simple docstring'''
							super().__init__()
							_UpperCamelCase					:		Dict 				=   in_channels != out_channels or stride != 1
							_UpperCamelCase					:		Optional[int] 				=   max(1 ,out_channels // config.groups_width )
							_UpperCamelCase					:		Optional[Any] 				=   (
							    RegNetShortCut(__lowerCAmelCase ,__lowerCAmelCase ,stride=__lowerCAmelCase ) if should_apply_shortcut else nn.Identity()
							)
							_UpperCamelCase					:		Optional[int] 				=   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 ) ,)
							_UpperCamelCase					:		str 				=   ACTaFN[config.hidden_act]





	def 	UpperCamelCase_  ( self					:				Optional[Any] ,lowerCamelCase__					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   hidden_state
							_UpperCamelCase					:		Union[str, Any] 				=   self.layer(__lowerCAmelCase )
							_UpperCamelCase					:		List[str] 				=   self.shortcut(__lowerCAmelCase )
							hidden_state += residual
							_UpperCamelCase					:		Dict 				=   self.activation(__lowerCAmelCase )
							return hidden_state






class 							lowercase__	(							nn.Module   ):





	def __init__( self					:				Union[str, Any] ,lowerCamelCase__					:				Any ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Optional[Any] ,lowerCamelCase__					:				Optional[Any] = 1 ):







							'''simple docstring'''
							super().__init__()
							_UpperCamelCase					:		Optional[int] 				=   in_channels != out_channels or stride != 1
							_UpperCamelCase					:		List[Any] 				=   max(1 ,out_channels // config.groups_width )
							_UpperCamelCase					:		Optional[Any] 				=   (
							    RegNetShortCut(__lowerCAmelCase ,__lowerCAmelCase ,stride=__lowerCAmelCase ) if should_apply_shortcut else nn.Identity()
							)
							_UpperCamelCase					:		List[str] 				=   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 ) ,)
							_UpperCamelCase					:		List[str] 				=   ACTaFN[config.hidden_act]





	def 	UpperCamelCase_  ( self					:				List[str] ,lowerCamelCase__					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   hidden_state
							_UpperCamelCase					:		Optional[Any] 				=   self.layer(__lowerCAmelCase )
							_UpperCamelCase					:		Any 				=   self.shortcut(__lowerCAmelCase )
							hidden_state += residual
							_UpperCamelCase					:		Optional[Any] 				=   self.activation(__lowerCAmelCase )
							return hidden_state






class 							lowercase__	(							nn.Module   ):





	def __init__( self					:				Dict ,lowerCamelCase__					:				int ,lowerCamelCase__					:				List[str] ,lowerCamelCase__					:				Optional[Any] ,lowerCamelCase__					:				List[str] = 2 ,lowerCamelCase__					:				str = 2 ,):







							'''simple docstring'''
							super().__init__()

							_UpperCamelCase					:		Optional[int] 				=   RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

							_UpperCamelCase					:		Optional[int] 				=   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 	UpperCamelCase_  ( self					:				Optional[Any] ,lowerCamelCase__					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   self.layers(__lowerCAmelCase )
							return hidden_state






class 							lowercase__	(							nn.Module   ):





	def __init__( self					:				Union[str, Any] ,lowerCamelCase__					:				str ):







							'''simple docstring'''
							super().__init__()
							_UpperCamelCase					:		Any 				=   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] ,) )
							_UpperCamelCase					:		Optional[int] 				=   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 	UpperCamelCase_  ( self					:				Optional[Any] ,lowerCamelCase__					:				Tuple ,lowerCamelCase__					:				Any = False ,lowerCamelCase__					:				Any = True ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   () if output_hidden_states else None

							for stage_module in self.stages:
													if output_hidden_states:
																			_UpperCamelCase					:		List[str] 				=   hidden_states + (hidden_state,)

													_UpperCamelCase					:		Optional[Any] 				=   stage_module(__lowerCAmelCase )

							if output_hidden_states:
													_UpperCamelCase					:		str 				=   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 							lowercase__	(							lowerCAmelCase__   ):
	lowercase__  				=			RegNetConfig
	lowercase__  				=			'''regnet'''
	lowercase__  				=			'''pixel_values'''
	lowercase__  				=			True





	def 	UpperCamelCase_  ( self					:				Optional[int] ,lowerCamelCase__					:				List[str] ):







							'''simple docstring'''


							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 	UpperCamelCase_  ( self					:				int ,lowerCamelCase__					:				Optional[Any] ,lowerCamelCase__					:				str=False ):







							'''simple docstring'''


							if isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
													_UpperCamelCase					:		Optional[Any] 				=   value
snake_case_ :	str         =  r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    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 ([`RegNetConfig`]): 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'

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






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





	def __init__( self					:				str ,lowerCamelCase__					:				Any ):







							'''simple docstring'''
							super().__init__(__lowerCAmelCase )
							_UpperCamelCase					:		Optional[Any] 				=   config
							_UpperCamelCase					:		Optional[Any] 				=   RegNetEmbeddings(__lowerCAmelCase )
							_UpperCamelCase					:		int 				=   RegNetEncoder(__lowerCAmelCase )
							_UpperCamelCase					:		str 				=   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 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				int ,lowerCamelCase__					:				Any = None ,lowerCamelCase__					:				Optional[int] = None ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							_UpperCamelCase					:		List[str] 				=   return_dict if return_dict is not None else self.config.use_return_dict

							_UpperCamelCase					:		str 				=   self.embedder(__lowerCAmelCase )

							_UpperCamelCase					:		List[str] 				=   self.encoder(
							    __lowerCAmelCase ,output_hidden_states=__lowerCAmelCase ,return_dict=__lowerCAmelCase )

							_UpperCamelCase					:		List[Any] 				=   encoder_outputs[0]

							_UpperCamelCase					:		Union[str, Any] 				=   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(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """						,					lowerCAmelCase__						,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 							lowercase__	(							lowerCAmelCase__   ):





	def __init__( self					:				Union[str, Any] ,lowerCamelCase__					:				List[Any] ):







							'''simple docstring'''
							super().__init__(__lowerCAmelCase )
							_UpperCamelCase					:		Tuple 				=   config.num_labels
							_UpperCamelCase					:		Optional[int] 				=   RegNetModel(__lowerCAmelCase )
							# classification head
							_UpperCamelCase					:		List[str] 				=   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 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				List[str] = None ,lowerCamelCase__					:				List[Any] = None ,lowerCamelCase__					:				str = None ,lowerCamelCase__					:				Optional[int] = None ,):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   return_dict if return_dict is not None else self.config.use_return_dict

							_UpperCamelCase					:		Optional[Any] 				=   self.regnet(__lowerCAmelCase ,output_hidden_states=__lowerCAmelCase ,return_dict=__lowerCAmelCase )

							_UpperCamelCase					:		List[Any] 				=   outputs.pooler_output if return_dict else outputs[1]

							_UpperCamelCase					:		int 				=   self.classifier(__lowerCAmelCase )

							_UpperCamelCase					:		Optional[int] 				=   None

							if labels is not None:
													if self.config.problem_type is None:
																			if self.num_labels == 1:
																									_UpperCamelCase					:		Dict 				=   'regression'
																			elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																									_UpperCamelCase					:		List[Any] 				=   'single_label_classification'
																			else:
																									_UpperCamelCase					:		Union[str, Any] 				=   'multi_label_classification'
													if self.config.problem_type == "regression":
																			_UpperCamelCase					:		Tuple 				=   MSELoss()
																			if self.num_labels == 1:
																									_UpperCamelCase					:		Tuple 				=   loss_fct(logits.squeeze() ,labels.squeeze() )
																			else:
																									_UpperCamelCase					:		Optional[Any] 				=   loss_fct(__lowerCAmelCase ,__lowerCAmelCase )
													elif self.config.problem_type == "single_label_classification":
																			_UpperCamelCase					:		int 				=   CrossEntropyLoss()
																			_UpperCamelCase					:		Dict 				=   loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
													elif self.config.problem_type == "multi_label_classification":
																			_UpperCamelCase					:		int 				=   BCEWithLogitsLoss()
																			_UpperCamelCase					:		Optional[Any] 				=   loss_fct(__lowerCAmelCase ,__lowerCAmelCase )

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

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



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase          =  {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''InstructBlipQFormerModel''',
						    '''InstructBlipPreTrainedModel''',
						    '''InstructBlipForConditionalGeneration''',
						    '''InstructBlipVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




