
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
   from ... import PreTrainedTokenizerBase, TensorType

__lowerCamelCase							=       logging.get_logger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		int						     =     "vision-encoder-decoder"
   A__					:		Tuple						     =     True



   def __init__(self		:					Optional[Any]    ,						**snake_case__		:					Optional[int] )		->	List[Any]:


          '''simple docstring'''


          super().__init__(**snake_case__ )
          if "encoder" not in kwargs or "decoder" not in kwargs:
                 raise ValueError(
                     f"""A configuraton of type {self.model_type} cannot be instantiated because """
                     f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )

          snake_case       :   int							=      kwargs.pop("encoder" )
          snake_case       :   Tuple							=      encoder_config.pop("model_type" )
          snake_case       :   int							=      kwargs.pop("decoder" )
          snake_case       :   Optional[Any]							=      decoder_config.pop("model_type" )

          snake_case       :   Tuple							=      AutoConfig.for_model(snake_case__    ,						**snake_case__ )
          snake_case       :   List[Any]							=      AutoConfig.for_model(snake_case__    ,						**snake_case__ )
          snake_case       :   List[Any]							=      True



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Optional[Any]    ,						snake_case__		:					PretrainedConfig    ,						snake_case__		:					PretrainedConfig    ,						**snake_case__		:					int )		->	PretrainedConfig:


          '''simple docstring'''


          logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
          snake_case       :   List[str]							=      True
          snake_case       :   Optional[Any]							=      True

          return cls(encoder=encoder_config.to_dict()    ,						decoder=decoder_config.to_dict()    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          snake_case       :   Any							=      copy.deepcopy(self.__dict__ )
          snake_case       :   List[Any]							=      self.encoder.to_dict()
          snake_case       :   Tuple							=      self.decoder.to_dict()
          snake_case       :   Union[str, Any]							=      self.__class__.model_type
          return output






class   UpperCAmelCase		(						A_ ):
   A__					:		str						     =     version.parse("1.11" )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ] )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	float:


          '''simple docstring'''


          return 1e-4



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}} )






class   UpperCAmelCase		(						A_ ):



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          snake_case       :   Dict							=      OrderedDict()
          snake_case       :   Optional[Any]							=      {0: "batch", 1: "past_decoder_sequence + sequence"}
          snake_case       :   Tuple							=      {0: "batch", 1: "past_decoder_sequence + sequence"}
          snake_case       :   List[Any]							=      {0: "batch", 1: "encoder_sequence"}

          return common_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					"PreTrainedTokenizerBase"    ,						snake_case__		:					int = -1    ,						snake_case__		:					int = -1    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional["TensorType"] = None    ,						)		->	Mapping[str, Any]:


          '''simple docstring'''


          import torch

          snake_case       :   str							=      OrderedDict()

          snake_case       :   Dict							=      super().generate_dummy_inputs(
              snake_case__    ,						batch_size=snake_case__    ,						seq_length=snake_case__    ,						is_pair=snake_case__    ,						framework=snake_case__ )

          snake_case				,					snake_case       :   Dict							=      dummy_input["input_ids"].shape
          snake_case       :   Dict							=      (batch, encoder_sequence, self._config.encoder_hidden_size)
          snake_case       :   List[str]							=      dummy_input.pop("input_ids" )
          snake_case       :   Optional[int]							=      dummy_input.pop("attention_mask" )
          snake_case       :   Union[str, Any]							=      torch.zeros(snake_case__ )

          return common_inputs






class   UpperCAmelCase		(						A_ ):



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	None:


          '''simple docstring'''


          pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					PretrainedConfig )		->	OnnxConfig:


          '''simple docstring'''


          return VisionEncoderDecoderEncoderOnnxConfig(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					PretrainedConfig    ,						snake_case__		:					PretrainedConfig    ,						snake_case__		:					str = "default" )		->	OnnxConfig:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      encoder_config.hidden_size
          return VisionEncoderDecoderDecoderOnnxConfig(snake_case__    ,						snake_case__ )








"""simple docstring"""


import pickle

import numpy as np
from matplotlib import pyplot as plt





class     snake_case	:

				def __init__(	self							:				int     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				Union[str, Any]=0.2     ,  UpperCamelCase__							:				Any=0.2)->   Optional[int]:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				int)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			{
						    "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(UpperCamelCase__     ,  "wb") as f:
								pickle.dump(UpperCamelCase__     ,  UpperCamelCase__)

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

				@classmethod
				def 							lowercase_     (	cls							:				Dict     ,  UpperCamelCase__							:				Union[str, Any])->   List[Any]:



						'''simple docstring'''


						with open(UpperCamelCase__     ,  "rb") as f:
								__lowerCAmelCase:       Dict          =			pickle.load(UpperCamelCase__)  # noqa: S301

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

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				List[Any])->   List[Any]:



						'''simple docstring'''


						return 1 / (1 + np.exp(-1 * x))

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				List[Any])->   Optional[Any]:



						'''simple docstring'''


						return round(UpperCamelCase__     ,  3)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				int)->   Dict:



						'''simple docstring'''


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

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

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Optional[Any]="average_pool")->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			len(featuremaps[0])
						__lowerCAmelCase:       List[Any]          =			int(size_map / size_pooling)
						__lowerCAmelCase:       int          =			[]
						for i_map in range(len(UpperCamelCase__)):
								__lowerCAmelCase:       str          =			featuremaps[i_map]
								__lowerCAmelCase:       List[Any]          =			[]
								for i_focus in range(0     ,  UpperCamelCase__     ,  UpperCamelCase__):
										for j_focus in range(0     ,  UpperCamelCase__     ,  UpperCamelCase__):
												__lowerCAmelCase:       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(UpperCamelCase__))
												elif pooling_type == "max_pooling":
														# max pooling
														map_pooled.append(np.max(UpperCamelCase__))
								__lowerCAmelCase:       Optional[int]          =			np.asmatrix(UpperCamelCase__).reshape(UpperCamelCase__     ,  UpperCamelCase__)
								featuremap_pooled.append(UpperCamelCase__)
						return featuremap_pooled

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str)->   int:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			[]
						for i in range(len(UpperCamelCase__)):
								__lowerCAmelCase:       Union[str, Any]          =			np.shape(data[i])
								__lowerCAmelCase:       int          =			data[i].reshape(1     ,  shapes[0] * shapes[1])
								__lowerCAmelCase:       Dict          =			data_listed.getA().tolist()[0]
								data_expanded.extend(UpperCamelCase__)
						__lowerCAmelCase:       Any          =			np.asarray(UpperCamelCase__)
						return data_expanded

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			np.asarray(UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			np.shape(UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			data_mat.reshape(1     ,  shapes[0] * shapes[1])
						return data_expanded

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Dict)->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[]
						__lowerCAmelCase:       Any          =			0
						for i_map in range(UpperCamelCase__):
								__lowerCAmelCase:       Optional[Any]          =			np.ones((size_map, size_map))
								for i in range(0     ,  UpperCamelCase__     ,  UpperCamelCase__):
										for j in range(0     ,  UpperCamelCase__     ,  UpperCamelCase__):
												__lowerCAmelCase:       Optional[Any]          =			pd_pool[
												    i_pool
												]
												__lowerCAmelCase:       str          =			i_pool + 1
								__lowerCAmelCase:       Dict          =			np.multiply(
								    UpperCamelCase__     ,  np.multiply(out_map[i_map]     ,  (1 - out_map[i_map])))
								pd_all.append(UpperCamelCase__)
						return pd_all

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				int     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				str=bool)->   List[str]:



						'''simple docstring'''


						print("----------------------Start Training-------------------------")
						print((" - - Shape: Train_Data  ", np.shape(UpperCamelCase__)))
						print((" - - Shape: Teach_Data  ", np.shape(UpperCamelCase__)))
						__lowerCAmelCase:       str          =			0
						__lowerCAmelCase:       Optional[int]          =			[]
						__lowerCAmelCase:       List[Any]          =			1_0_0_0_0
						while rp < n_repeat and mse >= error_accuracy:
								__lowerCAmelCase:       Optional[Any]          =			0
								print(f"-------------Learning Time {rp}--------------")
								for p in range(len(UpperCamelCase__)):
										# print('------------Learning Image: %d--------------'%p)
										__lowerCAmelCase:       Dict          =			np.asmatrix(datas_train[p])
										__lowerCAmelCase:       Dict          =			np.asarray(datas_teach[p])
										__lowerCAmelCase      , __lowerCAmelCase:       int          =			self.convolute(
										    UpperCamelCase__     ,  self.conva     ,  self.w_conva     ,  self.thre_conva     ,  conv_step=self.step_conva     ,  )
										__lowerCAmelCase:       Any          =			self.pooling(UpperCamelCase__     ,  self.size_poolinga)
										__lowerCAmelCase:       Optional[Any]          =			np.shape(UpperCamelCase__)
										__lowerCAmelCase:       str          =			self._expand(UpperCamelCase__)
										__lowerCAmelCase:       str          =			data_bp_input

										__lowerCAmelCase:       int          =			np.dot(UpperCamelCase__     ,  self.vji.T) - self.thre_bpa
										__lowerCAmelCase:       int          =			self.sig(UpperCamelCase__)
										__lowerCAmelCase:       Optional[Any]          =			np.dot(UpperCamelCase__     ,  self.wkj.T) - self.thre_bpa
										__lowerCAmelCase:       str          =			self.sig(UpperCamelCase__)

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

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

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

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

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

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

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				Tuple)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			[]
						print("-------------------Start Testing-------------------------")
						print((" - - Shape: Test_Data  ", np.shape(UpperCamelCase__)))
						for p in range(len(UpperCamelCase__)):
								__lowerCAmelCase:       Dict          =			np.asmatrix(datas_test[p])
								__lowerCAmelCase      , __lowerCAmelCase:       Optional[int]          =			self.convolute(
								    UpperCamelCase__     ,  self.conva     ,  self.w_conva     ,  self.thre_conva     ,  conv_step=self.step_conva     ,  )
								__lowerCAmelCase:       Tuple          =			self.pooling(UpperCamelCase__     ,  self.size_poolinga)
								__lowerCAmelCase:       List[str]          =			self._expand(UpperCamelCase__)

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




				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				Any)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			np.asmatrix(UpperCamelCase__)
						__lowerCAmelCase      , __lowerCAmelCase:       Optional[Any]          =			self.convolute(
						    UpperCamelCase__     ,  self.conva     ,  self.w_conva     ,  self.thre_conva     ,  conv_step=self.step_conva     ,  )
						__lowerCAmelCase:       Any          =			self.pooling(UpperCamelCase__     ,  self.size_poolinga)

						return data_conveda, data_pooleda


if __name__ == "__main__":
			pass


