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

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


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



class 						lowercase_				(				nn.Module ):

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




        """simple docstring"""


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

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.convolution(__UpperCamelCase   )
        UpperCamelCase_											=				self.normalization(__UpperCamelCase   )
        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(__UpperCamelCase   ,		__UpperCamelCase   ,		kernel_size=1   )   ,		nn.ReLU()   ,		nn.Convad(__UpperCamelCase   ,		__UpperCamelCase   ,		kernel_size=1   )   ,		nn.Sigmoid()   ,		)

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.pooler(__UpperCamelCase   )
        UpperCamelCase_											=				self.attention(__UpperCamelCase   )
        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(__UpperCamelCase   ,		__UpperCamelCase   ,		stride=__UpperCamelCase   ) if should_apply_shortcut else nn.Identity()
        )
        UpperCamelCase_											=				nn.Sequential(
            RegNetConvLayer(__UpperCamelCase   ,		__UpperCamelCase   ,		kernel_size=1   ,		activation=config.hidden_act   )   ,		RegNetConvLayer(__UpperCamelCase   ,		__UpperCamelCase   ,		stride=__UpperCamelCase   ,		groups=__UpperCamelCase   ,		activation=config.hidden_act   )   ,		RegNetConvLayer(__UpperCamelCase   ,		__UpperCamelCase   ,		kernel_size=1   ,		activation=__UpperCamelCase   )   ,		)
        UpperCamelCase_											=				ACTaFN[config.hidden_act]

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				hidden_state
        UpperCamelCase_											=				self.layer(__UpperCamelCase   )
        UpperCamelCase_											=				self.shortcut(__UpperCamelCase   )
        hidden_state += residual
        UpperCamelCase_											=				self.activation(__UpperCamelCase   )
        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(__UpperCamelCase   ,		__UpperCamelCase   ,		stride=__UpperCamelCase   ) if should_apply_shortcut else nn.Identity()
        )
        UpperCamelCase_											=				nn.Sequential(
            RegNetConvLayer(__UpperCamelCase   ,		__UpperCamelCase   ,		kernel_size=1   ,		activation=config.hidden_act   )   ,		RegNetConvLayer(__UpperCamelCase   ,		__UpperCamelCase   ,		stride=__UpperCamelCase   ,		groups=__UpperCamelCase   ,		activation=config.hidden_act   )   ,		RegNetSELayer(__UpperCamelCase   ,		reduced_channels=int(round(in_channels / 4   )   )   )   ,		RegNetConvLayer(__UpperCamelCase   ,		__UpperCamelCase   ,		kernel_size=1   ,		activation=__UpperCamelCase   )   ,		)
        UpperCamelCase_											=				ACTaFN[config.hidden_act]

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				hidden_state
        UpperCamelCase_											=				self.layer(__UpperCamelCase   )
        UpperCamelCase_											=				self.shortcut(__UpperCamelCase   )
        hidden_state += residual
        UpperCamelCase_											=				self.activation(__UpperCamelCase   )
        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(
                __UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		stride=__UpperCamelCase   ,		)   ,		*[layer(__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ) for _ in range(depth - 1   )]   ,		)

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


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

 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(__UpperCamelCase   )

        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=__UpperCamelCase   ,		hidden_states=__UpperCamelCase   )



class 						lowercase_				(				__lowerCamelCase ):
 A__  : Any      					=			RegNetConfig
 A__  : Tuple      					=			"regnet"
 A__  : int      					=			"pixel_values"
 A__  : int      					=			True

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        if isinstance(__UpperCamelCase   ,		nn.Convad   ):
               nn.init.kaiming_normal_(module.weight   ,		mode="""fan_out"""   ,		nonlinearity="""relu"""   )
        elif isinstance(__UpperCamelCase   ,		(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(__UpperCamelCase   ,		__UpperCamelCase   ):
               UpperCamelCase_											=				value



_A		     =    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		     =    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   ,		__UpperCamelCase   ):




        """simple docstring"""


        super().__init__(__UpperCamelCase   )
        UpperCamelCase_											=				config
        UpperCamelCase_											=				RegNetEmbeddings(__UpperCamelCase   )
        UpperCamelCase_											=				RegNetEncoder(__UpperCamelCase   )
        UpperCamelCase_											=				nn.AdaptiveAvgPoolad((1, 1)   )
        # Initialize weights and apply final processing
        self.post_init()

 @add_start_docstrings_to_model_forward(__UpperCamelCase   )
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC   ,		output_type=__UpperCamelCase   ,		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(__UpperCamelCase   )

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

        UpperCamelCase_											=				encoder_outputs[0]

        UpperCamelCase_											=				self.pooler(__UpperCamelCase   )

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

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

 def __init__(					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        super().__init__(__UpperCamelCase   )
        UpperCamelCase_											=				config.num_labels
        UpperCamelCase_											=				RegNetModel(__UpperCamelCase   )
        # 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(__UpperCamelCase   )
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT   ,		output_type=__UpperCamelCase   ,		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(__UpperCamelCase   ,		output_hidden_states=__UpperCamelCase   ,		return_dict=__UpperCamelCase   )

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

        UpperCamelCase_											=				self.classifier(__UpperCamelCase   )

        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(__UpperCamelCase   ,		__UpperCamelCase   )
               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(__UpperCamelCase   ,		__UpperCamelCase   )

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

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










import random




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			Optional[int]      )      ->	tuple:
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      [], [], []
    for element in data:
        if element < pivot:
            less.append(__UpperCAmelCase      )
        elif element > pivot:
            greater.append(__UpperCAmelCase      )
        else:
            equal.append(__UpperCAmelCase      )
    return less, equal, greater





def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			int      )      ->	List[str]:
    # index = len(items) // 2 when trying to find the median
    #   (value of index when items is sorted)

    # invalid input
    if index >= len(__UpperCAmelCase      ) or index < 0:
        return None

    UpperCamelCase__							:				List[str]          =      items[random.randint(0		,   len(__UpperCAmelCase      ) - 1      )]
    UpperCamelCase__							:				List[Any]          =      0
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				int          =      _partition(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      len(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      len(__UpperCAmelCase      )

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(__UpperCAmelCase		,   __UpperCAmelCase      )
    # must be in larger
    else:
        return quick_select(__UpperCAmelCase		,   index - (m + count)      )

