
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		     =    logging.get_logger(__name__)

# General docstring
_A		     =    '''RegNetConfig'''

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

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

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

class 						lowercase_				(				nn.Module ):

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




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.convolution(_a   )
        UpperCamelCase_											=				self.normalization(_a   )
        UpperCamelCase_											=				self.activation(_a   )
        return hidden_state



class 						lowercase_				(				nn.Module ):

 def __init__(					self   ,		__UpperCamelCase   ):




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				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_											=				self.embedder(_a   )
        return hidden_state



class 						lowercase_				(				nn.Module ):

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




        """simple docstring"""


        super().__init__()
        UpperCamelCase_											=				nn.Convad(_a   ,		_a   ,		kernel_size=1   ,		stride=_a   ,		bias=_a   )
        UpperCamelCase_											=				nn.BatchNormad(_a   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.convolution(_a   )
        UpperCamelCase_											=				self.normalization(_a   )
        return hidden_state



class 						lowercase_				(				nn.Module ):

 def __init__(					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        super().__init__()

        UpperCamelCase_											=				nn.AdaptiveAvgPoolad((1, 1)   )
        UpperCamelCase_											=				nn.Sequential(
            nn.Convad(_a   ,		_a   ,		kernel_size=1   )   ,		nn.ReLU()   ,		nn.Convad(_a   ,		_a   ,		kernel_size=1   )   ,		nn.Sigmoid()   ,		)

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.pooler(_a   )
        UpperCamelCase_											=				self.attention(_a   )
        UpperCamelCase_											=				hidden_state * attention
        return hidden_state



class 						lowercase_				(				nn.Module ):

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




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				hidden_state
        UpperCamelCase_											=				self.layer(_a   )
        UpperCamelCase_											=				self.shortcut(_a   )
        hidden_state += residual
        UpperCamelCase_											=				self.activation(_a   )
        return hidden_state



class 						lowercase_				(				nn.Module ):

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




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				hidden_state
        UpperCamelCase_											=				self.layer(_a   )
        UpperCamelCase_											=				self.shortcut(_a   )
        hidden_state += residual
        UpperCamelCase_											=				self.activation(_a   )
        return hidden_state



class 						lowercase_				(				nn.Module ):

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




        """simple docstring"""


        super().__init__()

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

        UpperCamelCase_											=				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 lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.layers(_a   )
        return hidden_state



class 						lowercase_				(				nn.Module ):

 def __init__(					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        super().__init__()
        UpperCamelCase_											=				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]   ,		)   )
        UpperCamelCase_											=				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 lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase = False   ,		__UpperCamelCase = True   ):




        """simple docstring"""


        UpperCamelCase_											=				() if output_hidden_states else None

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

               UpperCamelCase_											=				stage_module(_a   )

        if output_hidden_states:
               UpperCamelCase_											=				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 						lowercase_				(				lowercase__ ):
 A__  : int      					=			RegNetConfig
 A__  : Any      					=			'''regnet'''
 A__  : Optional[Any]      					=			'''pixel_values'''
 A__  : List[str]      					=			True

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """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 lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase=False   ):




        """simple docstring"""


        if isinstance(_a   ,		_a   ):
               UpperCamelCase_											=				value



_A		     =    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.
'''

_A		     =    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."""							,		lowercase__							,		)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 						lowercase_				(				lowercase__ ):

 def __init__(					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        super().__init__(_a   )
        UpperCamelCase_											=				config
        UpperCamelCase_											=				RegNetEmbeddings(_a   )
        UpperCamelCase_											=				RegNetEncoder(_a   )
        UpperCamelCase_											=				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 lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ):




        """simple docstring"""


        UpperCamelCase_											=				(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        UpperCamelCase_											=				return_dict if return_dict is not None else self.config.use_return_dict

        UpperCamelCase_											=				self.embedder(_a   )

        UpperCamelCase_											=				self.encoder(
            _a   ,		output_hidden_states=_a   ,		return_dict=_a   )

        UpperCamelCase_											=				encoder_outputs[0]

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

 def __init__(					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        super().__init__(_a   )
        UpperCamelCase_											=				config.num_labels
        UpperCamelCase_											=				RegNetModel(_a   )
        # classification head
        UpperCamelCase_											=				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 lowerCamelCase_ (					self   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ,		):




        """simple docstring"""


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

        UpperCamelCase_											=				self.regnet(_a   ,		output_hidden_states=_a   ,		return_dict=_a   )

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

        UpperCamelCase_											=				self.classifier(_a   )

        UpperCamelCase_											=				None

        if labels is not None:
               if self.config.problem_type is None:
                      if self.num_labels == 1:
                             UpperCamelCase_											=				'regression'
                      elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                             UpperCamelCase_											=				'single_label_classification'
                      else:
                             UpperCamelCase_											=				'multi_label_classification'
               if self.config.problem_type == "regression":
                      UpperCamelCase_											=				MSELoss()
                      if self.num_labels == 1:
                             UpperCamelCase_											=				loss_fct(logits.squeeze()   ,		labels.squeeze()   )
                      else:
                             UpperCamelCase_											=				loss_fct(_a   ,		_a   )
               elif self.config.problem_type == "single_label_classification":
                      UpperCamelCase_											=				CrossEntropyLoss()
                      UpperCamelCase_											=				loss_fct(logits.view(-1   ,		self.num_labels   )   ,		labels.view(-1   )   )
               elif self.config.problem_type == "multi_label_classification":
                      UpperCamelCase_											=				BCEWithLogitsLoss()
                      UpperCamelCase_											=				loss_fct(_a   ,		_a   )

        if not return_dict:
               UpperCamelCase_											=				(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'''







import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


__lowerCAmelCase         =    logging.get_logger(__name__)







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     Tuple							,*_a      :     List[str]							,**_a      :     Any       ):


     '''simple docstring'''






     warnings.warn(
         'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
         ' Please use VideoMAEImageProcessor instead.'							,_a							,)
     super().__init__(*_a							,**_a       )



