'''simple docstring'''




import pickle

import numpy as np
from matplotlib import pyplot as plt





class 					__magic_name__		:







			def __init__(			self       :						Optional[Any]       ,    lowercase_       :						Tuple       ,    lowercase_       :						Dict       ,    lowercase_       :						Dict       ,    lowercase_       :						Any       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Any=0.2       ,    lowercase_       :						int=0.2					):
										lowercase_      :    Optional[int]       		= bp_numa
										lowercase_      :    int       		= bp_numa
										lowercase_      :    Optional[int]       		= bp_numa
										lowercase_      :    List[str]       		= conva_get[:2]
										lowercase_      :    str       		= conva_get[2]
										lowercase_      :    Tuple       		= size_pa
										lowercase_      :    Union[str, Any]       		= rate_w
										lowercase_      :    Optional[Any]       		= rate_t
										lowercase_      :    str       		= [
										    np.mat(-1 * np.random.rand(self.conva[0]       ,    self.conva[0]					) + 0.5					)
										    for i in range(self.conva[1]					)
										]
										lowercase_      :    Dict       		= np.mat(-1 * np.random.rand(self.num_bpa       ,    self.num_bpa					) + 0.5					)
										lowercase_      :    List[Any]       		= np.mat(-1 * np.random.rand(self.num_bpa       ,    self.num_bpa					) + 0.5					)
										lowercase_      :    Union[str, Any]       		= -2 * np.random.rand(self.conva[1]					) + 1
										lowercase_      :    Union[str, Any]       		= -2 * np.random.rand(self.num_bpa					) + 1
										lowercase_      :    List[Any]       		= -2 * np.random.rand(self.num_bpa					) + 1







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Union[str, Any]					):
										# save model dict with pickle
										lowercase_      :    int       		= {
										    """num_bp1""": self.num_bpa,
										    """num_bp2""": self.num_bpa,
										    """num_bp3""": self.num_bpa,
										    """conv1""": self.conva,
										    """step_conv1""": self.step_conva,
										    """size_pooling1""": self.size_poolinga,
										    """rate_weight""": self.rate_weight,
										    """rate_thre""": self.rate_thre,
										    """w_conv1""": self.w_conva,
										    """wkj""": self.wkj,
										    """vji""": self.vji,
										    """thre_conv1""": self.thre_conva,
										    """thre_bp2""": self.thre_bpa,
										    """thre_bp3""": self.thre_bpa,
										}
										with open(lowercase_       ,    """wb"""					) as f:
																	pickle.dump(lowercase_       ,    lowercase_					)

										print(f'''Model saved： {save_path}'''					)







			@classmethod
			def 			SCREAMING_SNAKE_CASE_  (			cls       :						Any       ,    lowercase_       :						List[Any]					):
										# read saved model
										with open(lowercase_       ,    """rb"""					) as f:
																	lowercase_      :    List[Any]       		= pickle.load(lowercase_					)  # noqa: S301

										lowercase_      :    Any       		= model_dic.get("""conv1"""					)
										conv_get.append(model_dic.get("""step_conv1"""					)					)
										lowercase_      :    Tuple       		= model_dic.get("""size_pooling1"""					)
										lowercase_      :    Tuple       		= model_dic.get("""num_bp1"""					)
										lowercase_      :    Dict       		= model_dic.get("""num_bp2"""					)
										lowercase_      :    Tuple       		= model_dic.get("""num_bp3"""					)
										lowercase_      :    List[str]       		= model_dic.get("""rate_weight"""					)
										lowercase_      :    Optional[int]       		= model_dic.get("""rate_thre"""					)
										# create model instance
										lowercase_      :    List[str]       		= CNN(lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
										# modify model parameter
										lowercase_      :    int       		= model_dic.get("""w_conv1"""					)
										lowercase_      :    str       		= model_dic.get("""wkj"""					)
										lowercase_      :    List[Any]       		= model_dic.get("""vji"""					)
										lowercase_      :    Union[str, Any]       		= model_dic.get("""thre_conv1"""					)
										lowercase_      :    Union[str, Any]       		= model_dic.get("""thre_bp2"""					)
										lowercase_      :    List[str]       		= model_dic.get("""thre_bp3"""					)
										return conv_ins







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										return 1 / (1 + np.exp(-1 * x					))







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Union[str, Any]					):
										return round(lowercase_       ,    3					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Tuple       ,    lowercase_       :						Tuple       ,    lowercase_       :						List[Any]       ,    lowercase_       :						Dict					):
										# convolution process
										lowercase_      :    List[str]       		= convs[0]
										lowercase_      :    Tuple       		= convs[1]
										lowercase_      :    Tuple       		= np.shape(lowercase_					)[0]
										# get the data slice of original image data, data_focus
										lowercase_      :    Dict       		= []
										for i_focus in range(0       ,    size_data - size_conv + 1       ,    lowercase_					):
																	for j_focus in range(0       ,    size_data - size_conv + 1       ,    lowercase_					):
																								lowercase_      :    Optional[int]       		= data[
																								    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																								]
																								data_focus.append(lowercase_					)
        # calculate the feature map of every single kernel, and saved as list of matrix
										lowercase_      :    List[Any]       		= []
										lowercase_      :    int       		= int((size_data - size_conv) / conv_step + 1					)
										for i_map in range(lowercase_					):
																	lowercase_      :    Any       		= []
																	for i_focus in range(len(lowercase_					)					):
																								lowercase_      :    List[Any]       		= (
																								    np.sum(np.multiply(data_focus[i_focus]       ,    w_convs[i_map]					)					)
																								    - thre_convs[i_map]
																								)
																								featuremap.append(self.sig(lowercase_					)					)
																	lowercase_      :    Optional[int]       		= np.asmatrix(lowercase_					).reshape(
																	    lowercase_       ,    lowercase_					)
																	data_featuremap.append(lowercase_					)

										# expanding the data slice to One dimenssion
										lowercase_      :    Optional[Any]       		= []
										for each_focus in data_focus:
																	focusa_list.extend(self.Expand_Mat(lowercase_					)					)
										lowercase_      :    Tuple       		= np.asarray(lowercase_					)
										return focus_list, data_featuremap







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						str       ,    lowercase_       :						List[str]       ,    lowercase_       :						Tuple="average_pool"					):
										# pooling process
										lowercase_      :    int       		= len(featuremaps[0]					)
										lowercase_      :    Optional[int]       		= int(size_map / size_pooling					)
										lowercase_      :    Dict       		= []
										for i_map in range(len(lowercase_					)					):
																	lowercase_      :    Any       		= featuremaps[i_map]
																	lowercase_      :    List[Any]       		= []
																	for i_focus in range(0       ,    lowercase_       ,    lowercase_					):
																								for j_focus in range(0       ,    lowercase_       ,    lowercase_					):
																															lowercase_      :    List[Any]       		= feature_map[
																															    i_focus : i_focus + size_pooling,
																															    j_focus : j_focus + size_pooling,
																															]
																															if pooling_type == "average_pool":
																																						# average pooling
																																						map_pooled.append(np.average(lowercase_					)					)
																															elif pooling_type == "max_pooling":
																																						# max pooling
																																						map_pooled.append(np.max(lowercase_					)					)
																	lowercase_      :    int       		= np.asmatrix(lowercase_					).reshape(lowercase_       ,    lowercase_					)
																	featuremap_pooled.append(lowercase_					)
										return featuremap_pooled







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Optional[Any]					):
										# expanding three dimension data to one dimension list
										lowercase_      :    Optional[int]       		= []
										for i in range(len(lowercase_					)					):
																	lowercase_      :    List[str]       		= np.shape(data[i]					)
																	lowercase_      :    Optional[Any]       		= data[i].reshape(1       ,    shapes[0] * shapes[1]					)
																	lowercase_      :    int       		= data_listed.getA().tolist()[0]
																	data_expanded.extend(lowercase_					)
										lowercase_      :    Union[str, Any]       		= np.asarray(lowercase_					)
										return data_expanded







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Dict					):
										# expanding matrix to one dimension list
										lowercase_      :    Optional[int]       		= np.asarray(lowercase_					)
										lowercase_      :    Optional[Any]       		= np.shape(lowercase_					)
										lowercase_      :    Tuple       		= data_mat.reshape(1       ,    shapes[0] * shapes[1]					)
										return data_expanded







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						int       ,    lowercase_       :						Tuple       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Dict       ,    lowercase_       :						Optional[int]					):
										lowercase_      :    Tuple       		= []
										lowercase_      :    List[str]       		= 0
										for i_map in range(lowercase_					):
																	lowercase_      :    Any       		= np.ones((size_map, size_map)					)
																	for i in range(0       ,    lowercase_       ,    lowercase_					):
																								for j in range(0       ,    lowercase_       ,    lowercase_					):
																															lowercase_      :    List[str]       		= pd_pool[
																															    i_pool
																															]
																															lowercase_      :    Tuple       		= i_pool + 1
																	lowercase_      :    Dict       		= np.multiply(
																	    lowercase_       ,    np.multiply(out_map[i_map]       ,    (1 - out_map[i_map])					)					)
																	pd_all.append(lowercase_					)
										return pd_all







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						Tuple       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						Any       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						Any       ,    lowercase_       :						Any=bool					):
										# model traning
										print("""----------------------Start Training-------------------------"""					)
										print((""" - - Shape: Train_Data  """, np.shape(lowercase_					))					)
										print((""" - - Shape: Teach_Data  """, np.shape(lowercase_					))					)
										lowercase_      :    Tuple       		= 0
										lowercase_      :    Optional[Any]       		= []
										lowercase_      :    str       		= 10000
										while rp < n_repeat and mse >= error_accuracy:
																	lowercase_      :    Any       		= 0
																	print(f'''-------------Learning Time {rp}--------------'''					)
																	for p in range(len(lowercase_					)					):
																								# print('------------Learning Image: %d--------------'%p)
																								lowercase_      :    Any       		= np.asmatrix(datas_train[p]					)
																								lowercase_      :    Optional[int]       		= np.asarray(datas_teach[p]					)
																								lowercase_		,		lowercase_      :    List[Any]       		= self.convolute(
																								    lowercase_       ,    self.conva       ,    self.w_conva       ,    self.thre_conva       ,    conv_step=self.step_conva       ,    )
																								lowercase_      :    List[Any]       		= self.pooling(lowercase_       ,    self.size_poolinga					)
																								lowercase_      :    List[str]       		= np.shape(lowercase_					)
																								lowercase_      :    Dict       		= self._expand(lowercase_					)
																								lowercase_      :    Tuple       		= data_bp_input

																								lowercase_      :    Union[str, Any]       		= np.dot(lowercase_       ,    self.vji.T					) - self.thre_bpa
																								lowercase_      :    Tuple       		= self.sig(lowercase_					)
																								lowercase_      :    Tuple       		= np.dot(lowercase_       ,    self.wkj.T					) - self.thre_bpa
																								lowercase_      :    Tuple       		= self.sig(lowercase_					)

																								# --------------Model Leaning ------------------------
																								# calculate error and gradient---------------
																								lowercase_      :    int       		= np.multiply(
																								    (data_teach - bp_outa)       ,    np.multiply(lowercase_       ,    (1 - bp_outa)					)					)
																								lowercase_      :    str       		= np.multiply(
																								    np.dot(lowercase_       ,    self.wkj					)       ,    np.multiply(lowercase_       ,    (1 - bp_outa)					)					)
																								lowercase_      :    List[Any]       		= np.dot(lowercase_       ,    self.vji					)

																								lowercase_      :    Optional[int]       		= pd_i_all / (self.size_poolinga * self.size_poolinga)
																								lowercase_      :    Tuple       		= pd_conva_pooled.T.getA().tolist()
																								lowercase_      :    Any       		= self._calculate_gradient_from_pool(
																								    lowercase_       ,    lowercase_       ,    shape_featuremapa[0]       ,    shape_featuremapa[1]       ,    self.size_poolinga       ,    )
																								# weight and threshold learning process---------
																								# convolution layer
																								for k_conv in range(self.conva[1]					):
																															lowercase_      :    Dict       		= self._expand_mat(pd_conva_all[k_conv]					)
																															lowercase_      :    Union[str, Any]       		= self.rate_weight * np.dot(lowercase_       ,    lowercase_					)

																															lowercase_      :    str       		= self.w_conva[k_conv] + delta_w.reshape(
																															    (self.conva[0], self.conva[0])					)

																															lowercase_      :    Optional[Any]       		= (
																															    self.thre_conva[k_conv]
																															    - np.sum(pd_conva_all[k_conv]					) * self.rate_thre
																															)
																								# all connected layer
																								lowercase_      :    Optional[int]       		= self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																								lowercase_      :    int       		= self.vji + pd_j_all.T * bp_outa * self.rate_weight
																								lowercase_      :    List[Any]       		= self.thre_bpa - pd_k_all * self.rate_thre
																								lowercase_      :    Union[str, Any]       		= self.thre_bpa - pd_j_all * self.rate_thre
																								# calculate the sum error of all single image
																								lowercase_      :    Any       		= np.sum(abs(data_teach - bp_outa					)					)
																								error_count += errors
																								# print('   ----Teach      ',data_teach)
																								# print('   ----BP_output  ',bp_out3)
																	lowercase_      :    Dict       		= rp + 1
																	lowercase_      :    Any       		= error_count / patterns
																	all_mse.append(lowercase_					)

										def draw_error():
																	lowercase_      :    str       		= [error_accuracy for i in range(int(n_repeat * 1.2					)					)]
																	plt.plot(lowercase_       ,    """+-"""					)
																	plt.plot(lowercase_       ,    """r--"""					)
																	plt.xlabel("""Learning Times"""					)
																	plt.ylabel("""All_mse"""					)
																	plt.grid(lowercase_       ,    alpha=0.5					)
																	plt.show()

										print("""------------------Training Complished---------------------"""					)
										print((""" - - Training epoch: """, rp, f'''     - - Mse: {mse:.6f}''')					)
										if draw_e:
																	draw_error()
										return mse







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						List[str]					):
										# model predict
										lowercase_      :    List[Any]       		= []
										print("""-------------------Start Testing-------------------------"""					)
										print((""" - - Shape: Test_Data  """, np.shape(lowercase_					))					)
										for p in range(len(lowercase_					)					):
																	lowercase_      :    Any       		= np.asmatrix(datas_test[p]					)
																	lowercase_		,		lowercase_      :    Any       		= self.convolute(
																	    lowercase_       ,    self.conva       ,    self.w_conva       ,    self.thre_conva       ,    conv_step=self.step_conva       ,    )
																	lowercase_      :    List[str]       		= self.pooling(lowercase_       ,    self.size_poolinga					)
																	lowercase_      :    List[Any]       		= self._expand(lowercase_					)

																	lowercase_      :    str       		= data_bp_input
																	lowercase_      :    int       		= bp_outa * self.vji.T - self.thre_bpa
																	lowercase_      :    Union[str, Any]       		= self.sig(lowercase_					)
																	lowercase_      :    Tuple       		= bp_outa * self.wkj.T - self.thre_bpa
																	lowercase_      :    Any       		= self.sig(lowercase_					)
																	produce_out.extend(bp_outa.getA().tolist()					)
										lowercase_      :    List[Any]       		= [list(map(self.do_round       ,    lowercase_					)					) for each in produce_out]
										return np.asarray(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Any					):
										# return the data of image after convoluting process so we can check it out
										lowercase_      :    str       		= np.asmatrix(lowercase_					)
										lowercase_		,		lowercase_      :    Optional[int]       		= self.convolute(
										    lowercase_       ,    self.conva       ,    self.w_conva       ,    self.thre_conva       ,    conv_step=self.step_conva       ,    )
										lowercase_      :    Dict       		= self.pooling(lowercase_       ,    self.size_poolinga					)

										return data_conveda, data_pooleda


if __name__ == "__main__":
						pass






'''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


_lowercase		:				Tuple	       =		logging.get_logger(__name__)

# General docstring
_lowercase		:				List[str]	       =		"RegNetConfig"

# Base docstring
_lowercase		:				Dict	       =		"facebook/regnet-y-040"
_lowercase		:				Union[str, Any]	       =		[1, 1088, 7, 7]

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

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





class 					__magic_name__		(					nn.Module):







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Dict       		= self.convolution(lowercase_					)
										lowercase_      :    str       		= self.normalization(lowercase_					)
										lowercase_      :    Optional[Any]       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						Optional[Any]					):
										lowercase_      :    List[str]       		= 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_      :    Any       		= self.embedder(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						Tensor					):
										lowercase_      :    Tuple       		= self.convolution(lowercase_					)
										lowercase_      :    str       		= self.normalization(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						str       ,    lowercase_       :						int       ,    lowercase_       :						int					):
										super().__init__()

										lowercase_      :    int       		= nn.AdaptiveAvgPoolad((1, 1)					)
										lowercase_      :    int       		= nn.Sequential(
										    nn.Convad(lowercase_       ,    lowercase_       ,    kernel_size=1					)       ,    nn.ReLU()       ,    nn.Convad(lowercase_       ,    lowercase_       ,    kernel_size=1					)       ,    nn.Sigmoid()       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						Any					):
										# b c h w -> b c 1 1
										lowercase_      :    List[str]       		= self.pooler(lowercase_					)
										lowercase_      :    Optional[int]       		= self.attention(lowercase_					)
										lowercase_      :    Any       		= hidden_state * attention
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[int]       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 1					):
										super().__init__()
										lowercase_      :    List[Any]       		= in_channels != out_channels or stride != 1
										lowercase_      :    Optional[int]       		= max(1       ,    out_channels // config.groups_width					)
										lowercase_      :    Dict       		= (
										    RegNetShortCut(lowercase_       ,    lowercase_       ,    stride=lowercase_					) if should_apply_shortcut else nn.Identity()
										)
										lowercase_      :    List[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_					)       ,    )
										lowercase_      :    int       		= ACTaFN[config.hidden_act]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Any					):
										lowercase_      :    Any       		= hidden_state
										lowercase_      :    Union[str, Any]       		= self.layer(lowercase_					)
										lowercase_      :    Union[str, Any]       		= self.shortcut(lowercase_					)
										hidden_state += residual
										lowercase_      :    str       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[Any]       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 1					):
										super().__init__()
										lowercase_      :    str       		= in_channels != out_channels or stride != 1
										lowercase_      :    int       		= max(1       ,    out_channels // config.groups_width					)
										lowercase_      :    int       		= (
										    RegNetShortCut(lowercase_       ,    lowercase_       ,    stride=lowercase_					) if should_apply_shortcut else nn.Identity()
										)
										lowercase_      :    Union[str, Any]       		= 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_					)       ,    )
										lowercase_      :    Optional[int]       		= ACTaFN[config.hidden_act]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										lowercase_      :    Optional[int]       		= hidden_state
										lowercase_      :    str       		= self.layer(lowercase_					)
										lowercase_      :    int       		= self.shortcut(lowercase_					)
										hidden_state += residual
										lowercase_      :    Optional[Any]       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						str       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 2       ,    lowercase_       :						int = 2       ,    ):
										super().__init__()

										lowercase_      :    str       		= RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

										lowercase_      :    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 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Tuple       		= self.layers(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Dict       ,    lowercase_       :						RegNetConfig					):
										super().__init__()
										lowercase_      :    Optional[Any]       		= 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]       ,    )					)
										lowercase_      :    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 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						Tensor       ,    lowercase_       :						bool = False       ,    lowercase_       :						bool = True					):
										lowercase_      :    Tuple       		= () if output_hidden_states else None

										for stage_module in self.stages:
																	if output_hidden_states:
																								lowercase_      :    Union[str, Any]       		= hidden_states + (hidden_state,)

																	lowercase_      :    Dict       		= stage_module(lowercase_					)

										if output_hidden_states:
																	lowercase_      :    Optional[Any]       		= 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 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = RegNetConfig
			UpperCamelCase__       = '''regnet'''
			UpperCamelCase__       = '''pixel_values'''
			UpperCamelCase__       = True







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Optional[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 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Any=False					):
										if isinstance(lowercase_       ,    lowercase_					):
																	lowercase_      :    List[str]       		= value




_lowercase		:				Dict	       =		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		:				Any	       =		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.''',  _UpperCAmelCase,  )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 					__magic_name__		(					_UpperCAmelCase):







			def __init__(			self       :						Any       ,    lowercase_       :						Any					):
										super().__init__(lowercase_					)
										lowercase_      :    List[str]       		= config
										lowercase_      :    Union[str, Any]       		= RegNetEmbeddings(lowercase_					)
										lowercase_      :    Union[str, Any]       		= RegNetEncoder(lowercase_					)
										lowercase_      :    str       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						Tensor       ,    lowercase_       :						Optional[bool] = None       ,    lowercase_       :						Optional[bool] = None					):
										lowercase_      :    List[Any]       		= (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										lowercase_      :    Tuple       		= return_dict if return_dict is not None else self.config.use_return_dict

										lowercase_      :    str       		= self.embedder(lowercase_					)

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

										lowercase_      :    List[Any]       		= encoder_outputs[0]

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







			def __init__(			self       :						Dict       ,    lowercase_       :						str					):
										super().__init__(lowercase_					)
										lowercase_      :    Any       		= config.num_labels
										lowercase_      :    List[str]       		= RegNetModel(lowercase_					)
										# classification head
										lowercase_      :    Any       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Optional[torch.FloatTensor] = None       ,    lowercase_       :						Optional[torch.LongTensor] = None       ,    lowercase_       :						Optional[bool] = None       ,    lowercase_       :						Optional[bool] = None       ,    ):
										lowercase_      :    Union[str, Any]       		= return_dict if return_dict is not None else self.config.use_return_dict

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

										lowercase_      :    Optional[int]       		= outputs.pooler_output if return_dict else outputs[1]

										lowercase_      :    List[Any]       		= self.classifier(lowercase_					)

										lowercase_      :    Optional[int]       		= None

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

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

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





