



from typing import TYPE_CHECKING

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


a_    :  Optional[int]						  =		{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  int						  =		[
         'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ResNetForImageClassification',
         'ResNetModel',
         'ResNetPreTrainedModel',
         'ResNetBackbone',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Optional[Any]						  =		[
         'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFResNetForImageClassification',
         'TFResNetModel',
         'TFResNetPreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Union[str, Any]						  =		[
         'FlaxResNetForImageClassification',
         'FlaxResNetModel',
         'FlaxResNetPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_resnet import (
              RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              ResNetBackbone,
              ResNetForImageClassification,
              ResNetModel,
              ResNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_resnet import (
              TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFResNetForImageClassification,
              TFResNetModel,
              TFResNetPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
     import sys

     a_    :  Any						  =		_LazyModule(__name__, globals()['__file__'], _import_structure)




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_    :  Optional[int]						  =		logging.get_logger(__name__)

# General docstring
a_    :  List[str]						  =		'RegNetConfig'

# Base docstring
a_    :  Union[str, Any]						  =		'facebook/regnet-y-040'
a_    :  Optional[Any]						  =		[1, 1_0_8_8, 7, 7]

# Image classification docstring
a_    :  Dict						  =		'facebook/regnet-y-040'
a_    :  List[Any]						  =		'tabby, tabby cat'

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





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a = 3					,      __a = 1					,      __a = 1					,      __a = "relu"					,      ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		nn.Convad(
               __a					,      __a					,      kernel_size=__a					,      stride=__a					,      padding=kernel_size // 2					,      groups=__a					,      bias=__a					,      )
           lowerCamelCase             =		nn.BatchNormad(__a     )
           lowerCamelCase             =		ACTaFN[activation] if activation is not None else nn.Identity()







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.convolution(__a     )
           lowerCamelCase             =		self.normalization(__a     )
           lowerCamelCase             =		self.activation(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		RegNetConvLayer(
               config.num_channels					,      config.embedding_size					,      kernel_size=3					,      stride=2					,      activation=config.hidden_act     )
           lowerCamelCase             =		config.num_channels







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           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(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a = 2     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		nn.Convad(__a					,      __a					,      kernel_size=1					,      stride=__a					,      bias=__a     )
           lowerCamelCase             =		nn.BatchNormad(__a     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.convolution(__a     )
           lowerCamelCase             =		self.normalization(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a     ):






           '''simple docstring'''



           super().__init__()

           lowerCamelCase             =		nn.AdaptiveAvgPoolad((1, 1)     )
           lowerCamelCase             =		nn.Sequential(
               nn.Convad(__a					,      __a					,      kernel_size=1     )					,      nn.ReLU()					,      nn.Convad(__a					,      __a					,      kernel_size=1     )					,      nn.Sigmoid()					,      )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.pooler(__a     )
           lowerCamelCase             =		self.attention(__a     )
           lowerCamelCase             =		hidden_state * attention
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a					,      __a = 1     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		in_channels != out_channels or stride != 1
           lowerCamelCase             =		max(1					,      out_channels // config.groups_width     )
           lowerCamelCase             =		(
               RegNetShortCut(__a					,      __a					,      stride=__a     ) if should_apply_shortcut else nn.Identity()
           )
           lowerCamelCase             =		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             =		ACTaFN[config.hidden_act]







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		hidden_state
           lowerCamelCase             =		self.layer(__a     )
           lowerCamelCase             =		self.shortcut(__a     )
           hidden_state += residual
           lowerCamelCase             =		self.activation(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a					,      __a = 1     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		in_channels != out_channels or stride != 1
           lowerCamelCase             =		max(1					,      out_channels // config.groups_width     )
           lowerCamelCase             =		(
               RegNetShortCut(__a					,      __a					,      stride=__a     ) if should_apply_shortcut else nn.Identity()
           )
           lowerCamelCase             =		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             =		ACTaFN[config.hidden_act]







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		hidden_state
           lowerCamelCase             =		self.layer(__a     )
           lowerCamelCase             =		self.shortcut(__a     )
           hidden_state += residual
           lowerCamelCase             =		self.activation(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a					,      __a = 2					,      __a = 2					,      ):






           '''simple docstring'''



           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(
                   __a					,      __a					,      __a					,      stride=__a					,      )					,      *[layer(__a					,      __a					,      __a     ) for _ in range(depth - 1     )]					,      )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.layers(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           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(
                   __a					,      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(__a					,      config.depths[1:]     ):
                 self.stages.append(RegNetStage(__a					,      __a					,      __a					,      depth=__a     )     )







     def     _a   (self					,      __a					,      __a = False					,      __a = True     ):






           '''simple docstring'''



           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(__a     )

           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=__a					,      hidden_states=__a     )





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      RegNetConfig
     _A      =      'regnet'
     _A      =      'pixel_values'
     _A      =      True







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           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     ):






           '''simple docstring'''



           if isinstance(__a					,      __a     ):
                 lowerCamelCase             =		value



a_    :  Dict						  =		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_    :  Optional[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.'							,     UpperCAmelCase_							,     )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__(__a     )
           lowerCamelCase             =		config
           lowerCamelCase             =		RegNetEmbeddings(__a     )
           lowerCamelCase             =		RegNetEncoder(__a     )
           lowerCamelCase             =		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     ):






           '''simple docstring'''



           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(__a     )

           lowerCamelCase             =		self.encoder(
               __a					,      output_hidden_states=__a					,      return_dict=__a     )

           lowerCamelCase             =		encoder_outputs[0]

           lowerCamelCase             =		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    '							,     UpperCAmelCase_							,     )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__(__a     )
           lowerCamelCase             =		config.num_labels
           lowerCamelCase             =		RegNetModel(__a     )
           # 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(__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					,      ):






           '''simple docstring'''



           lowerCamelCase             =		return_dict if return_dict is not None else self.config.use_return_dict

           lowerCamelCase             =		self.regnet(__a					,      output_hidden_states=__a					,      return_dict=__a     )

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

           lowerCamelCase             =		self.classifier(__a     )

           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(__a					,      __a     )
                 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(__a					,      __a     )

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

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