




"""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


__magic_name__				     =     logging.get_logger(__name__)

# General docstring
__magic_name__				     =     """RegNetConfig"""

# Base docstring
__magic_name__				     =     """facebook/regnet-y-040"""
__magic_name__				     =     [1, 10_88, 7, 7]

# Image classification docstring
__magic_name__				     =     """facebook/regnet-y-040"""
__magic_name__				     =     """tabby, tabby cat"""

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






class        SCREAMING_SNAKE_CASE__   (						nn.Module      ):
   def __init__(       self			:				Dict   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int = 3   ,	SCREAMING_SNAKE_CASE_			:				int = 1   ,	SCREAMING_SNAKE_CASE_			:				int = 1   ,	SCREAMING_SNAKE_CASE_			:				Optional[str] = "relu"   ,	):
          super().__init__()
          lowerCamelCase__          =						nn.Convad(
              UpperCAmelCase__   ,	UpperCAmelCase__   ,	kernel_size=UpperCAmelCase__   ,	stride=UpperCAmelCase__   ,	padding=kernel_size // 2   ,	groups=UpperCAmelCase__   ,	bias=UpperCAmelCase__   ,	)
          lowerCamelCase__          =						nn.BatchNormad(UpperCAmelCase__							)
          lowerCamelCase__          =						ACTaFN[activation] if activation is not None else nn.Identity()





   def    __UpperCAmelCase       (       self			:				Optional[int]   ,	SCREAMING_SNAKE_CASE_			:				Any							):
          lowerCamelCase__          =						self.convolution(UpperCAmelCase__							)
          lowerCamelCase__          =						self.normalization(UpperCAmelCase__							)
          lowerCamelCase__          =						self.activation(UpperCAmelCase__							)
          return hidden_state






class        SCREAMING_SNAKE_CASE__   (						nn.Module      ):
   def __init__(       self			:				str   ,	SCREAMING_SNAKE_CASE_			:				RegNetConfig							):
          super().__init__()
          lowerCamelCase__          =						RegNetConvLayer(
              config.num_channels   ,	config.embedding_size   ,	kernel_size=3   ,	stride=2   ,	activation=config.hidden_act							)
          lowerCamelCase__          =						config.num_channels





   def    __UpperCAmelCase       (       self			:				Union[str, Any]   ,	SCREAMING_SNAKE_CASE_			:				List[str]							):
          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(UpperCAmelCase__							)
          return hidden_state






class        SCREAMING_SNAKE_CASE__   (						nn.Module      ):
   def __init__(       self			:				List[Any]   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int = 2							):
          super().__init__()
          lowerCamelCase__          =						nn.Convad(UpperCAmelCase__   ,	UpperCAmelCase__   ,	kernel_size=1   ,	stride=UpperCAmelCase__   ,	bias=UpperCAmelCase__							)
          lowerCamelCase__          =						nn.BatchNormad(UpperCAmelCase__							)





   def    __UpperCAmelCase       (       self			:				Tuple   ,	SCREAMING_SNAKE_CASE_			:				Tensor							):
          lowerCamelCase__          =						self.convolution(UpperCAmelCase__							)
          lowerCamelCase__          =						self.normalization(UpperCAmelCase__							)
          return hidden_state






class        SCREAMING_SNAKE_CASE__   (						nn.Module      ):
   def __init__(       self			:				Union[str, Any]   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int							):
          super().__init__()

          lowerCamelCase__          =						nn.AdaptiveAvgPoolad((1, 1)							)
          lowerCamelCase__          =						nn.Sequential(
              nn.Convad(UpperCAmelCase__   ,	UpperCAmelCase__   ,	kernel_size=1							)   ,	nn.ReLU()   ,	nn.Convad(UpperCAmelCase__   ,	UpperCAmelCase__   ,	kernel_size=1							)   ,	nn.Sigmoid()   ,	)





   def    __UpperCAmelCase       (       self			:				Optional[int]   ,	SCREAMING_SNAKE_CASE_			:				List[Any]							):
          lowerCamelCase__          =						self.pooler(UpperCAmelCase__							)
          lowerCamelCase__          =						self.attention(UpperCAmelCase__							)
          lowerCamelCase__          =						hidden_state * attention
          return hidden_state






class        SCREAMING_SNAKE_CASE__   (						nn.Module      ):
   def __init__(       self			:				str   ,	SCREAMING_SNAKE_CASE_			:				RegNetConfig   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int = 1							):
          super().__init__()
          lowerCamelCase__          =						in_channels != out_channels or stride != 1
          lowerCamelCase__          =						max(1   ,	out_channels // config.groups_width							)
          lowerCamelCase__          =						(
              RegNetShortCut(UpperCAmelCase__   ,	UpperCAmelCase__   ,	stride=UpperCAmelCase__							) if should_apply_shortcut else nn.Identity()
          )
          lowerCamelCase__          =						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__							)   ,	)
          lowerCamelCase__          =						ACTaFN[config.hidden_act]





   def    __UpperCAmelCase       (       self			:				List[Any]   ,	SCREAMING_SNAKE_CASE_			:				Optional[Any]							):
          lowerCamelCase__          =						hidden_state
          lowerCamelCase__          =						self.layer(UpperCAmelCase__							)
          lowerCamelCase__          =						self.shortcut(UpperCAmelCase__							)
          hidden_state += residual
          lowerCamelCase__          =						self.activation(UpperCAmelCase__							)
          return hidden_state






class        SCREAMING_SNAKE_CASE__   (						nn.Module      ):
   def __init__(       self			:				List[Any]   ,	SCREAMING_SNAKE_CASE_			:				RegNetConfig   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int = 1							):
          super().__init__()
          lowerCamelCase__          =						in_channels != out_channels or stride != 1
          lowerCamelCase__          =						max(1   ,	out_channels // config.groups_width							)
          lowerCamelCase__          =						(
              RegNetShortCut(UpperCAmelCase__   ,	UpperCAmelCase__   ,	stride=UpperCAmelCase__							) if should_apply_shortcut else nn.Identity()
          )
          lowerCamelCase__          =						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__							)   ,	)
          lowerCamelCase__          =						ACTaFN[config.hidden_act]





   def    __UpperCAmelCase       (       self			:				Any   ,	SCREAMING_SNAKE_CASE_			:				Tuple							):
          lowerCamelCase__          =						hidden_state
          lowerCamelCase__          =						self.layer(UpperCAmelCase__							)
          lowerCamelCase__          =						self.shortcut(UpperCAmelCase__							)
          hidden_state += residual
          lowerCamelCase__          =						self.activation(UpperCAmelCase__							)
          return hidden_state






class        SCREAMING_SNAKE_CASE__   (						nn.Module      ):
   def __init__(       self			:				Dict   ,	SCREAMING_SNAKE_CASE_			:				RegNetConfig   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int   ,	SCREAMING_SNAKE_CASE_			:				int = 2   ,	SCREAMING_SNAKE_CASE_			:				int = 2   ,	):
          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(
                  UpperCAmelCase__   ,	UpperCAmelCase__   ,	UpperCAmelCase__   ,	stride=UpperCAmelCase__   ,	)   ,	*[layer(UpperCAmelCase__   ,	UpperCAmelCase__   ,	UpperCAmelCase__							) for _ in range(depth - 1							)]   ,	)





   def    __UpperCAmelCase       (       self			:				List[Any]   ,	SCREAMING_SNAKE_CASE_			:				Union[str, Any]							):
          lowerCamelCase__          =						self.layers(UpperCAmelCase__							)
          return hidden_state






class        SCREAMING_SNAKE_CASE__   (						nn.Module      ):
   def __init__(       self			:				int   ,	SCREAMING_SNAKE_CASE_			:				RegNetConfig							):
          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(
                  UpperCAmelCase__   ,	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(UpperCAmelCase__   ,	config.depths[1:]							):
                 self.stages.append(RegNetStage(UpperCAmelCase__   ,	UpperCAmelCase__   ,	UpperCAmelCase__   ,	depth=UpperCAmelCase__							)							)





   def    __UpperCAmelCase       (       self			:				List[str]   ,	SCREAMING_SNAKE_CASE_			:				Tensor   ,	SCREAMING_SNAKE_CASE_			:				bool = False   ,	SCREAMING_SNAKE_CASE_			:				bool = True							):
          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(UpperCAmelCase__							)

          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=UpperCAmelCase__   ,	hidden_states=UpperCAmelCase__							)






class        SCREAMING_SNAKE_CASE__   (						lowercase__      ):
   snake_case			   =       RegNetConfig
   snake_case			   =       "regnet"
   snake_case			   =       "pixel_values"
   snake_case			   =       True
   def    __UpperCAmelCase       (       self			:				Union[str, Any]   ,	SCREAMING_SNAKE_CASE_			:				Tuple							):
          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    __UpperCAmelCase       (       self			:				Optional[int]   ,	SCREAMING_SNAKE_CASE_			:				Any   ,	SCREAMING_SNAKE_CASE_			:				Optional[int]=False							):
          if isinstance(UpperCAmelCase__   ,	UpperCAmelCase__							):
                 lowerCamelCase__          =						value






__magic_name__				     =     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.
"""

__magic_name__				     =     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        SCREAMING_SNAKE_CASE__   (						lowercase__      ):
   def __init__(       self			:				Any   ,	SCREAMING_SNAKE_CASE_			:				List[Any]							):
          super().__init__(UpperCAmelCase__							)
          lowerCamelCase__          =						config
          lowerCamelCase__          =						RegNetEmbeddings(UpperCAmelCase__							)
          lowerCamelCase__          =						RegNetEncoder(UpperCAmelCase__							)
          lowerCamelCase__          =						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    __UpperCAmelCase       (       self			:				List[str]   ,	SCREAMING_SNAKE_CASE_			:				Tensor   ,	SCREAMING_SNAKE_CASE_			:				Optional[bool] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[bool] = None							):
          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(UpperCAmelCase__							)

          lowerCamelCase__          =						self.encoder(
              UpperCAmelCase__   ,	output_hidden_states=UpperCAmelCase__   ,	return_dict=UpperCAmelCase__							)

          lowerCamelCase__          =						encoder_outputs[0]

          lowerCamelCase__          =						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    " ,				lowercase__ ,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class        SCREAMING_SNAKE_CASE__   (						lowercase__      ):
   def __init__(       self			:				str   ,	SCREAMING_SNAKE_CASE_			:				Any							):
          super().__init__(UpperCAmelCase__							)
          lowerCamelCase__          =						config.num_labels
          lowerCamelCase__          =						RegNetModel(UpperCAmelCase__							)
          # 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(UpperCAmelCase__							)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT   ,	output_type=UpperCAmelCase__   ,	config_class=_CONFIG_FOR_DOC   ,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,	)
   def    __UpperCAmelCase       (       self			:				str   ,	SCREAMING_SNAKE_CASE_			:				Optional[torch.FloatTensor] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[torch.LongTensor] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[bool] = None   ,	SCREAMING_SNAKE_CASE_			:				Optional[bool] = None   ,	):
          lowerCamelCase__          =						return_dict if return_dict is not None else self.config.use_return_dict

          lowerCamelCase__          =						self.regnet(UpperCAmelCase__   ,	output_hidden_states=UpperCAmelCase__   ,	return_dict=UpperCAmelCase__							)

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

          lowerCamelCase__          =						self.classifier(UpperCAmelCase__							)

          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(UpperCAmelCase__   ,	UpperCAmelCase__							)
                 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(UpperCAmelCase__   ,	UpperCAmelCase__							)

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

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









'''simple docstring'''





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										if dst_width < 0 or dst_height < 0:
																raise ValueError('''Destination width/height should be > 0'''   )

										lowercase :							Union[str, Any]    		=img
										lowercase :							Union[str, Any]    		=img.shape[1]
										lowercase :							str    		=img.shape[0]
										lowercase :							Union[str, Any]    		=dst_width
										lowercase :							str    		=dst_height

										lowercase :							str    		=self.src_w / self.dst_w
										lowercase :							Optional[Any]    		=self.src_h / self.dst_h

										lowercase :							int    		=(
										    np.ones((self.dst_h, self.dst_w, 3)		,					np.uinta   ) * 255
										)







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										for i in range(self.dst_h   ):
																for j in range(self.dst_w   ):
																						lowercase :							List[Any]    		=self.img[self.get_y(UpperCAmelCase__   )][self.get_x(UpperCAmelCase__   )]







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_x * x   )







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_y * y   )


if __name__ == "__main__":
							UpperCamelCase_						,   UpperCamelCase_    						=       800, 600
							UpperCamelCase_    						=       imread("""image_data/lena.jpg""", 1)
							UpperCamelCase_    						=       NearestNeighbour(im, dst_w, dst_h)
							n.process()

							imshow(
							    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
							)
							waitKey(0)
							destroyAllWindows()





