"""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


_a      :		List[Any]           =   logging.get_logger(__name__)

# General docstring
_a      :		Optional[int]           =   'RegNetConfig'

# Base docstring
_a      :		Union[str, Any]           =   'facebook/regnet-y-040'
_a      :		Tuple           =   [1, 1_088, 7, 7]

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

_a      :		Union[str, Any]           =   [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 						__A							( nn.Module		):






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






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     self.convolution(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.normalization(a__  )
       _lowerCAmelCase						:       List[Any]  			=     self.activation(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       Optional[int]  			=     RegNetConvLayer(
           config.num_channels				,     config.embedding_size				,     kernel_size=3				,     stride=2				,     activation=config.hidden_act  )
       _lowerCAmelCase						:       List[Any]  			=     config.num_channels






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     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						:       List[Any]  			=     self.embedder(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__				,     a__ = 2  ):
       super().__init__()
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.Convad(a__				,     a__				,     kernel_size=1				,     stride=a__				,     bias=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     nn.BatchNormad(a__  )






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       str  			=     self.convolution(a__  )
       _lowerCAmelCase						:       Tuple  			=     self.normalization(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__  ):
       super().__init__()

       _lowerCAmelCase						:       Dict  			=     nn.AdaptiveAvgPoolad((1, 1)  )
       _lowerCAmelCase						:       Tuple  			=     nn.Sequential(
           nn.Convad(a__				,     a__				,     kernel_size=1  )				,     nn.ReLU()				,     nn.Convad(a__				,     a__				,     kernel_size=1  )				,     nn.Sigmoid()				,     )






     def     __A  (		self				,     a__  ):
       # b c h w -> b c 1 1
       _lowerCAmelCase						:       Tuple  			=     self.pooler(a__  )
       _lowerCAmelCase						:       int  			=     self.attention(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     hidden_state * attention
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__ = 1  ):
       super().__init__()
       _lowerCAmelCase						:       Optional[Any]  			=     in_channels != out_channels or stride != 1
       _lowerCAmelCase						:       Optional[Any]  			=     max(1				,     out_channels // config.groups_width  )
       _lowerCAmelCase						:       Tuple  			=     (
           RegNetShortCut(a__				,     a__				,     stride=a__  ) if should_apply_shortcut else nn.Identity()
       )
       _lowerCAmelCase						:       str  			=     nn.Sequential(
           RegNetConvLayer(a__				,     a__				,     kernel_size=1				,     activation=config.hidden_act  )				,     RegNetConvLayer(a__				,     a__				,     stride=a__				,     groups=a__				,     activation=config.hidden_act  )				,     RegNetConvLayer(a__				,     a__				,     kernel_size=1				,     activation=a__  )				,     )
       _lowerCAmelCase						:       Union[str, Any]  			=     ACTaFN[config.hidden_act]






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Any  			=     hidden_state
       _lowerCAmelCase						:       Any  			=     self.layer(a__  )
       _lowerCAmelCase						:       str  			=     self.shortcut(a__  )
       hidden_state += residual
       _lowerCAmelCase						:       int  			=     self.activation(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__ = 1  ):
       super().__init__()
       _lowerCAmelCase						:       Dict  			=     in_channels != out_channels or stride != 1
       _lowerCAmelCase						:       List[Any]  			=     max(1				,     out_channels // config.groups_width  )
       _lowerCAmelCase						:       List[Any]  			=     (
           RegNetShortCut(a__				,     a__				,     stride=a__  ) if should_apply_shortcut else nn.Identity()
       )
       _lowerCAmelCase						:       Tuple  			=     nn.Sequential(
           RegNetConvLayer(a__				,     a__				,     kernel_size=1				,     activation=config.hidden_act  )				,     RegNetConvLayer(a__				,     a__				,     stride=a__				,     groups=a__				,     activation=config.hidden_act  )				,     RegNetSELayer(a__				,     reduced_channels=int(round(in_channels / 4  )  )  )				,     RegNetConvLayer(a__				,     a__				,     kernel_size=1				,     activation=a__  )				,     )
       _lowerCAmelCase						:       Dict  			=     ACTaFN[config.hidden_act]






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     hidden_state
       _lowerCAmelCase						:       Optional[int]  			=     self.layer(a__  )
       _lowerCAmelCase						:       List[Any]  			=     self.shortcut(a__  )
       hidden_state += residual
       _lowerCAmelCase						:       List[str]  			=     self.activation(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__ = 2				,     a__ = 2				,     ):
       super().__init__()

       _lowerCAmelCase						:       List[Any]  			=     RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

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






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       List[Any]  			=     self.layers(a__  )
       return hidden_state







class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       Optional[int]  			=     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(
               a__				,     config.embedding_size				,     config.hidden_sizes[0]				,     stride=2 if config.downsample_in_first_stage else 1				,     depth=config.depths[0]				,     )  )
       _lowerCAmelCase						:       List[str]  			=     zip(config.hidden_sizes				,     config.hidden_sizes[1:]  )
       for (in_channels, out_channels), depth in zip(a__				,     config.depths[1:]  ):
         self.stages.append(RegNetStage(a__				,     a__				,     a__				,     depth=a__  )  )






     def     __A  (		self				,     a__				,     a__ = False				,     a__ = True  ):
       _lowerCAmelCase						:       Any  			=     () if output_hidden_states else None

       for stage_module in self.stages:
         if output_hidden_states:
           _lowerCAmelCase						:       Union[str, Any]  			=     hidden_states + (hidden_state,)

         _lowerCAmelCase						:       Union[str, Any]  			=     stage_module(a__  )

       if output_hidden_states:
         _lowerCAmelCase						:       Union[str, Any]  			=     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=a__				,     hidden_states=a__  )







class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Tuple							=		RegNetConfig
     _UpperCamelCase							:			int							=		"regnet"
     _UpperCamelCase							:			List[str]							=		"pixel_values"
     _UpperCamelCase							:			Union[str, Any]							=		True






     def     __A  (		self				,     a__  ):
       if isinstance(a__				,     nn.Convad  ):
         nn.init.kaiming_normal_(module.weight				,     mode="""fan_out"""				,     nonlinearity="""relu"""  )
       elif isinstance(a__				,     (nn.BatchNormad, nn.GroupNorm)  ):
         nn.init.constant_(module.weight				,     1  )
         nn.init.constant_(module.bias				,     0  )






     def     __A  (		self				,     a__				,     a__=False  ):
       if isinstance(a__				,     a__  ):
         _lowerCAmelCase						:       Optional[int]  			=     value







_a      :		List[Any]           =   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'

_a      :		List[Any]           =   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."    ,	SCREAMING_SNAKE_CASE_    ,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       List[Any]  			=     config
       _lowerCAmelCase						:       Any  			=     RegNetEmbeddings(a__  )
       _lowerCAmelCase						:       List[str]  			=     RegNetEncoder(a__  )
       _lowerCAmelCase						:       str  			=     nn.AdaptiveAvgPoolad((1, 1)  )
       # Initialize weights and apply final processing
       self.post_init()






     @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     __A  (		self				,     a__				,     a__ = None				,     a__ = None  ):
       _lowerCAmelCase						:       Any  			=     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowerCAmelCase						:       Tuple  			=     return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:       List[Any]  			=     self.embedder(a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     self.encoder(
           a__				,     output_hidden_states=a__				,     return_dict=a__  )

       _lowerCAmelCase						:       str  			=     encoder_outputs[0]

       _lowerCAmelCase						:       Optional[Any]  			=     self.pooler(a__  )

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

       return BaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=a__				,     pooler_output=a__				,     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    "    ,	SCREAMING_SNAKE_CASE_    ,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       Any  			=     config.num_labels
       _lowerCAmelCase						:       Optional[int]  			=     RegNetModel(a__  )
       # classification head
       _lowerCAmelCase						:       Optional[Any]  			=     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(a__  )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT				,     output_type=a__				,     config_class=_CONFIG_FOR_DOC				,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,     )
     def     __A  (		self				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     ):
       _lowerCAmelCase						:       List[Any]  			=     return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:       Union[str, Any]  			=     self.regnet(a__				,     output_hidden_states=a__				,     return_dict=a__  )

       _lowerCAmelCase						:       str  			=     outputs.pooler_output if return_dict else outputs[1]

       _lowerCAmelCase						:       str  			=     self.classifier(a__  )

       _lowerCAmelCase						:       List[str]  			=     None

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

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

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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LiltForQuestionAnswering''',
							    '''LiltForSequenceClassification''',
							    '''LiltForTokenClassification''',
							    '''LiltModel''',
							    '''LiltPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lilt import (
														    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LiltForQuestionAnswering,
														    LiltForSequenceClassification,
														    LiltForTokenClassification,
														    LiltModel,
														    LiltPreTrainedModel,
														)

else:
							import sys

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




