'''simple docstring'''





from cva import destroyAllWindows, imread, imshow, waitKey


def     __lowerCAmelCase						(       a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       img.shape[0], img.shape[1]

     # converting each pixel's color to its negative
     for i in range(a_		):
          for j in range(a_		):
               SCREAMING_SNAKE_CASE					:			Tuple    	=       [255, 255, 255] - img[i][j]

     return img


if __name__ == "__main__":
 # read original image
 _lowerCAmelCase  :Union[str, Any]  				=    imread("""image_data/lena.jpg""", 1)

 # convert to its negative
 _lowerCAmelCase  :str  				=    convert_to_negative(img)

 # show result image
 imshow("""negative of original image""", img)
 waitKey(0)
 destroyAllWindows()




'''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


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

# General docstring
_lowerCAmelCase  :Optional[Any]  				=    """RegNetConfig"""

# Base docstring
_lowerCAmelCase  :int  				=    """facebook/regnet-y-040"""
_lowerCAmelCase  :Any  				=    [1, 1_088, 7, 7]

# Image classification docstring
_lowerCAmelCase  :Optional[Any]  				=    """facebook/regnet-y-040"""
_lowerCAmelCase  :Optional[int]  				=    """tabby, tabby cat"""

_lowerCAmelCase  :List[Any]  				=    [
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 3    ,							lowercase__ = 1    ,							lowercase__ = 1    ,							lowercase__ = "relu"    ,							)							-> Any:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       nn.Convad(
             lowercase__    ,							lowercase__    ,							kernel_size=lowercase__    ,							stride=lowercase__    ,							padding=kernel_size // 2    ,							groups=lowercase__    ,							bias=lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			Dict    	=       nn.BatchNormad(lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       ACTaFN[activation] if activation is not None else nn.Identity()






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       self.convolution(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.normalization(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.activation(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> str:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       RegNetConvLayer(
             config.num_channels    ,							config.embedding_size    ,							kernel_size=3    ,							stride=2    ,							activation=config.hidden_act )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       config.num_channels






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> str:
         SCREAMING_SNAKE_CASE					:			Dict    	=       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.' )
         SCREAMING_SNAKE_CASE					:			str    	=       self.embedder(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 2 )							-> Optional[int]:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       nn.Convad(lowercase__    ,							lowercase__    ,							kernel_size=1    ,							stride=lowercase__    ,							bias=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       nn.BatchNormad(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Tensor:
         SCREAMING_SNAKE_CASE					:			int    	=       self.convolution(lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       self.normalization(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         super().__init__()

         SCREAMING_SNAKE_CASE					:			Any    	=       nn.AdaptiveAvgPoolad((1, 1) )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       nn.Sequential(
             nn.Convad(lowercase__    ,							lowercase__    ,							kernel_size=1 )    ,							nn.ReLU()    ,							nn.Convad(lowercase__    ,							lowercase__    ,							kernel_size=1 )    ,							nn.Sigmoid()    ,							)






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Dict:
         # b c h w -> b c 1 1
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.pooler(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.attention(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       hidden_state * attention
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







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






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Any:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       hidden_state
         SCREAMING_SNAKE_CASE					:			str    	=       self.layer(lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       self.shortcut(lowercase__ )
         hidden_state += residual
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.activation(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







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






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       hidden_state
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.layer(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.shortcut(lowercase__ )
         hidden_state += residual
         SCREAMING_SNAKE_CASE					:			int    	=       self.activation(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 2    ,							lowercase__ = 2    ,							)							-> Any:
         super().__init__()

         SCREAMING_SNAKE_CASE					:			Tuple    	=       RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

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






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.layers(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Dict:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			Dict    	=       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(
                 lowercase__    ,							config.embedding_size    ,							config.hidden_sizes[0]    ,							stride=2 if config.downsample_in_first_stage else 1    ,							depth=config.depths[0]    ,							) )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       zip(config.hidden_sizes    ,							config.hidden_sizes[1:] )
         for (in_channels, out_channels), depth in zip(lowercase__    ,							config.depths[1:] ):
              self.stages.append(RegNetStage(lowercase__    ,							lowercase__    ,							lowercase__    ,							depth=lowercase__ ) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = False    ,							lowercase__ = True )							-> BaseModelOutputWithNoAttention:
         SCREAMING_SNAKE_CASE					:			str    	=       () if output_hidden_states else None

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

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       stage_module(lowercase__ )

         if output_hidden_states:
              SCREAMING_SNAKE_CASE					:			int    	=       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=lowercase__    ,							hidden_states=lowercase__ )






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Any							=						RegNetConfig
    snake_case__							:   List[str]							=						"regnet"
    snake_case__							:   List[Any]							=						"pixel_values"
    snake_case__							:   Optional[Any]							=						True






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Any:
         if isinstance(lowercase__    ,							nn.Convad ):
              nn.init.kaiming_normal_(module.weight    ,							mode='fan_out'    ,							nonlinearity='relu' )
         elif isinstance(lowercase__    ,							(nn.BatchNormad, nn.GroupNorm) ):
              nn.init.constant_(module.weight    ,							1 )
              nn.init.constant_(module.bias    ,							0 )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=False )							-> Tuple:
         if isinstance(lowercase__    ,							lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Tuple    	=       value






_lowerCAmelCase  :Optional[Any]  				=    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.
"""

_lowerCAmelCase  :Optional[Any]  				=    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."   ,    _SCREAMING_SNAKE_CASE   ,    )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Union[str, Any]:
         super().__init__(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       config
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       RegNetEmbeddings(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       RegNetEncoder(lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       nn.AdaptiveAvgPoolad((1, 1) )
         # Initialize weights and apply final processing
         self.post_init()






    @add_start_docstrings_to_model_forward(lowercase__ )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC    ,							output_type=lowercase__    ,							config_class=_CONFIG_FOR_DOC    ,							modality='vision'    ,							expected_output=_EXPECTED_OUTPUT_SHAPE    ,							)
    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = None )							-> BaseModelOutputWithPoolingAndNoAttention:
         SCREAMING_SNAKE_CASE					:			Dict    	=       (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       return_dict if return_dict is not None else self.config.use_return_dict

         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.embedder(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.encoder(
             lowercase__    ,							output_hidden_states=lowercase__    ,							return_dict=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_outputs[0]

         SCREAMING_SNAKE_CASE					:			Dict    	=       self.pooler(lowercase__ )

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

         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=lowercase__    ,							pooler_output=lowercase__    ,							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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Dict:
         super().__init__(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       config.num_labels
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       RegNetModel(lowercase__ )
         # classification head
         SCREAMING_SNAKE_CASE					:			int    	=       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(lowercase__ )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT    ,							output_type=lowercase__    ,							config_class=_CONFIG_FOR_DOC    ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,							)
    def       _UpperCamelCase    (       self    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							)							-> ImageClassifierOutputWithNoAttention:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       return_dict if return_dict is not None else self.config.use_return_dict

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.regnet(lowercase__    ,							output_hidden_states=lowercase__    ,							return_dict=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       outputs.pooler_output if return_dict else outputs[1]

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.classifier(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       None

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

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

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



