

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


lowercase_									=   logging.get_logger(__name__)

# General docstring
lowercase_									=   'RegNetConfig'

# Base docstring
lowercase_									=   'facebook/regnet-y-040'
lowercase_									=   [1, 1_0_8_8, 7, 7]

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

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





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 3							,   lowerCAmelCase = 1							,   lowerCAmelCase = 1							,   lowerCAmelCase = "relu"							,   )  -> Any:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =self.convolution(lowerCAmelCase					)
        _lowercase	       =self.normalization(lowerCAmelCase					)
        _lowercase	       =self.activation(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> str:
        '''simple docstring'''






        _lowercase	       =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.'					)
        _lowercase	       =self.embedder(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 2					)  -> Union[str, Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =nn.Convad(lowerCAmelCase							,   lowerCAmelCase							,   kernel_size=1							,   stride=lowerCAmelCase							,   bias=lowerCAmelCase					)
        _lowercase	       =nn.BatchNormad(lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase					)  -> Tensor:
        '''simple docstring'''






        _lowercase	       =self.convolution(lowerCAmelCase					)
        _lowercase	       =self.normalization(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        super().__init__()

        _lowercase	       =nn.AdaptiveAvgPoolad((1, 1)					)
        _lowercase	       =nn.Sequential(
            nn.Convad(lowerCAmelCase							,   lowerCAmelCase							,   kernel_size=1					)							,   nn.ReLU()							,   nn.Convad(lowerCAmelCase							,   lowerCAmelCase							,   kernel_size=1					)							,   nn.Sigmoid()							,   )




  def   A__     (				self							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.pooler(lowerCAmelCase					)
        _lowercase	       =self.attention(lowerCAmelCase					)
        _lowercase	       =hidden_state * attention
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 1					)  -> str:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =hidden_state
        _lowercase	       =self.layer(lowerCAmelCase					)
        _lowercase	       =self.shortcut(lowerCAmelCase					)
        hidden_state += residual
        _lowercase	       =self.activation(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 1					)  -> Optional[int]:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =hidden_state
        _lowercase	       =self.layer(lowerCAmelCase					)
        _lowercase	       =self.shortcut(lowerCAmelCase					)
        hidden_state += residual
        _lowercase	       =self.activation(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 2							,   lowerCAmelCase = 2							,   )  -> int:
        '''simple docstring'''






        super().__init__()

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

        _lowercase	       =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   A__     (				self							,   lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        _lowercase	       =self.layers(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase					)  -> str:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =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]							,   )					)
        _lowercase	       =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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = False							,   lowerCAmelCase = True					)  -> BaseModelOutputWithNoAttention:
        '''simple docstring'''






        _lowercase	       =() if output_hidden_states else None

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

              _lowercase	       =stage_module(lowerCAmelCase					)

        if output_hidden_states:
              _lowercase	       =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  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  RegNetConfig
  _a									=  """regnet"""
  _a									=  """pixel_values"""
  _a									=  True




  def   A__     (				self							,   lowerCAmelCase					)  -> Optional[int]:
        '''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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=False					)  -> Optional[Any]:
        '''simple docstring'''






        if isinstance(lowerCAmelCase							,   lowerCAmelCase					):
              _lowercase	       =value
lowercase_									=   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'

lowercase_									=   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  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)
        _lowercase	       =config
        _lowercase	       =RegNetEmbeddings(lowerCAmelCase					)
        _lowercase	       =RegNetEncoder(lowerCAmelCase					)
        _lowercase	       =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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None					)  -> BaseModelOutputWithPoolingAndNoAttention:
        '''simple docstring'''






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

        _lowercase	       =self.embedder(lowerCAmelCase					)

        _lowercase	       =self.encoder(
            lowerCAmelCase							,   output_hidden_states=lowerCAmelCase							,   return_dict=lowerCAmelCase					)

        _lowercase	       =encoder_outputs[0]

        _lowercase	       =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.
    """			,			SCREAMING_SNAKE_CASE			,			)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)
        _lowercase	       =config.num_labels
        _lowercase	       =RegNetModel(lowerCAmelCase					)
        # classification head
        _lowercase	       =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   A__     (				self							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> ImageClassifierOutputWithNoAttention:
        '''simple docstring'''






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

        _lowercase	       =self.regnet(lowerCAmelCase							,   output_hidden_states=lowerCAmelCase							,   return_dict=lowerCAmelCase					)

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

        _lowercase	       =self.classifier(lowerCAmelCase					)

        _lowercase	       =None

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

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

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





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

      # each value
      for y in range(A__     ):
            for x in range(A__     ):
                  # distance from center
                  _lowercase	       =x - ksize // 2
                  _lowercase	       =y - ksize // 2

                  # degree to radiant
                  _lowercase	       =theta / 180 * np.pi
                  _lowercase	       =np.cos(_theta     )
                  _lowercase	       =np.sin(_theta     )

                  # get kernel x
                  _lowercase	       =cos_theta * px + sin_theta * py

                  # get kernel y
                  _lowercase	       =-sin_theta * px + cos_theta * py

                  # fill kernel
                  _lowercase	       =np.exp(
                      -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

      return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

  imshow('Original', gray)
  imshow('Gabor filter with 20x20 mask and 6 directions', out)

  waitKey(0)


