






import pickle

import numpy as np
from matplotlib import pyplot as plt

class 		_snake_case  :






			def __init__(       self		,					a		,					a		,					a		,					a		,					a		,					a=0.2		,					a=0.2)      ->       Any:
						SCREAMING_SNAKE_CASE						= bp_numa
						SCREAMING_SNAKE_CASE						= bp_numa
						SCREAMING_SNAKE_CASE						= bp_numa
						SCREAMING_SNAKE_CASE						= conva_get[:2]
						SCREAMING_SNAKE_CASE						= conva_get[2]
						SCREAMING_SNAKE_CASE						= size_pa
						SCREAMING_SNAKE_CASE						= rate_w
						SCREAMING_SNAKE_CASE						= rate_t
						SCREAMING_SNAKE_CASE						= [
						    np.mat(-1 * np.random.rand(self.conva[0]		,					self.conva[0]) + 0.5)
						    for i in range(self.conva[1])
						]
						SCREAMING_SNAKE_CASE						= np.mat(-1 * np.random.rand(self.num_bpa		,					self.num_bpa) + 0.5)
						SCREAMING_SNAKE_CASE						= np.mat(-1 * np.random.rand(self.num_bpa		,					self.num_bpa) + 0.5)
						SCREAMING_SNAKE_CASE						= -2 * np.random.rand(self.conva[1]) + 1
						SCREAMING_SNAKE_CASE						= -2 * np.random.rand(self.num_bpa) + 1
						SCREAMING_SNAKE_CASE						= -2 * np.random.rand(self.num_bpa) + 1






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= {
						    '''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(lowerCamelCase_		,					'wb') as f:
									pickle.dump(lowerCamelCase_		,					lowerCamelCase_)

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






			@classmethod
			def    SCREAMING_SNAKE_CASE__    (       cls		,					a)      ->       Tuple:




						with open(lowerCamelCase_		,					'rb') as f:
									SCREAMING_SNAKE_CASE						= pickle.load(lowerCamelCase_)  # noqa: S301

						SCREAMING_SNAKE_CASE						= model_dic.get('conv1')
						conv_get.append(model_dic.get('step_conv1'))
						SCREAMING_SNAKE_CASE						= model_dic.get('size_pooling1')
						SCREAMING_SNAKE_CASE						= model_dic.get('num_bp1')
						SCREAMING_SNAKE_CASE						= model_dic.get('num_bp2')
						SCREAMING_SNAKE_CASE						= model_dic.get('num_bp3')
						SCREAMING_SNAKE_CASE						= model_dic.get('rate_weight')
						SCREAMING_SNAKE_CASE						= model_dic.get('rate_thre')
						# create model instance
						SCREAMING_SNAKE_CASE						= CNN(lowerCamelCase_		,					lowerCamelCase_		,					lowerCamelCase_		,					lowerCamelCase_		,					lowerCamelCase_		,					lowerCamelCase_		,					lowerCamelCase_)
						# modify model parameter
						SCREAMING_SNAKE_CASE						= model_dic.get('w_conv1')
						SCREAMING_SNAKE_CASE						= model_dic.get('wkj')
						SCREAMING_SNAKE_CASE						= model_dic.get('vji')
						SCREAMING_SNAKE_CASE						= model_dic.get('thre_conv1')
						SCREAMING_SNAKE_CASE						= model_dic.get('thre_bp2')
						SCREAMING_SNAKE_CASE						= model_dic.get('thre_bp3')
						return conv_ins






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Optional[Any]:
						return 1 / (1 + np.exp(-1 * x))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						return round(lowerCamelCase_		,					3)






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

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a="average_pool")      ->       str:
						SCREAMING_SNAKE_CASE						= len(featuremaps[0])
						SCREAMING_SNAKE_CASE						= int(size_map / size_pooling)
						SCREAMING_SNAKE_CASE						= []
						for i_map in range(len(lowerCamelCase_)):
									SCREAMING_SNAKE_CASE						= featuremaps[i_map]
									SCREAMING_SNAKE_CASE						= []
									for i_focus in range(0		,					lowerCamelCase_		,					lowerCamelCase_):
												for j_focus in range(0		,					lowerCamelCase_		,					lowerCamelCase_):
															SCREAMING_SNAKE_CASE						= 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(lowerCamelCase_))
															elif pooling_type == "max_pooling":
																		# max pooling
																		map_pooled.append(np.max(lowerCamelCase_))
									SCREAMING_SNAKE_CASE						= np.asmatrix(lowerCamelCase_).reshape(lowerCamelCase_		,					lowerCamelCase_)
									featuremap_pooled.append(lowerCamelCase_)
						return featuremap_pooled






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= []
						for i in range(len(lowerCamelCase_)):
									SCREAMING_SNAKE_CASE						= np.shape(data[i])
									SCREAMING_SNAKE_CASE						= data[i].reshape(1		,					shapes[0] * shapes[1])
									SCREAMING_SNAKE_CASE						= data_listed.getA().tolist()[0]
									data_expanded.extend(lowerCamelCase_)
						SCREAMING_SNAKE_CASE						= np.asarray(lowerCamelCase_)
						return data_expanded






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= np.asarray(lowerCamelCase_)
						SCREAMING_SNAKE_CASE						= np.shape(lowerCamelCase_)
						SCREAMING_SNAKE_CASE						= data_mat.reshape(1		,					shapes[0] * shapes[1])
						return data_expanded






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= []
						SCREAMING_SNAKE_CASE						= 0
						for i_map in range(lowerCamelCase_):
									SCREAMING_SNAKE_CASE						= np.ones((size_map, size_map))
									for i in range(0		,					lowerCamelCase_		,					lowerCamelCase_):
												for j in range(0		,					lowerCamelCase_		,					lowerCamelCase_):
															SCREAMING_SNAKE_CASE						= pd_pool[
															    i_pool
															]
															SCREAMING_SNAKE_CASE						= i_pool + 1
									SCREAMING_SNAKE_CASE						= np.multiply(
									    lowerCamelCase_		,					np.multiply(out_map[i_map]		,					(1 - out_map[i_map])))
									pd_all.append(lowerCamelCase_)
						return pd_all






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a=bool)      ->       str:
						print('----------------------Start Training-------------------------')
						print((' - - Shape: Train_Data  ', np.shape(lowerCamelCase_)))
						print((' - - Shape: Teach_Data  ', np.shape(lowerCamelCase_)))
						SCREAMING_SNAKE_CASE						= 0
						SCREAMING_SNAKE_CASE						= []
						SCREAMING_SNAKE_CASE						= 1_0000
						while rp < n_repeat and mse >= error_accuracy:
									SCREAMING_SNAKE_CASE						= 0
									print(f'''-------------Learning Time {rp}--------------''')
									for p in range(len(lowerCamelCase_)):
												# print('------------Learning Image: %d--------------'%p)
												SCREAMING_SNAKE_CASE						= np.asmatrix(datas_train[p])
												SCREAMING_SNAKE_CASE						= np.asarray(datas_teach[p])
												SCREAMING_SNAKE_CASE						= self.convolute(
												    lowerCamelCase_		,					self.conva		,					self.w_conva		,					self.thre_conva		,					conv_step=self.step_conva		,					)
												SCREAMING_SNAKE_CASE						= self.pooling(lowerCamelCase_		,					self.size_poolinga)
												SCREAMING_SNAKE_CASE						= np.shape(lowerCamelCase_)
												SCREAMING_SNAKE_CASE						= self._expand(lowerCamelCase_)
												SCREAMING_SNAKE_CASE						= data_bp_input

												SCREAMING_SNAKE_CASE						= np.dot(lowerCamelCase_		,					self.vji.T) - self.thre_bpa
												SCREAMING_SNAKE_CASE						= self.sig(lowerCamelCase_)
												SCREAMING_SNAKE_CASE						= np.dot(lowerCamelCase_		,					self.wkj.T) - self.thre_bpa
												SCREAMING_SNAKE_CASE						= self.sig(lowerCamelCase_)

												# --------------Model Leaning ------------------------
												# calculate error and gradient---------------
												SCREAMING_SNAKE_CASE						= np.multiply(
												    (data_teach - bp_outa)		,					np.multiply(lowerCamelCase_		,					(1 - bp_outa)))
												SCREAMING_SNAKE_CASE						= np.multiply(
												    np.dot(lowerCamelCase_		,					self.wkj)		,					np.multiply(lowerCamelCase_		,					(1 - bp_outa)))
												SCREAMING_SNAKE_CASE						= np.dot(lowerCamelCase_		,					self.vji)

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

															SCREAMING_SNAKE_CASE						= self.w_conva[k_conv] + delta_w.reshape(
															    (self.conva[0], self.conva[0]))

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

						def draw_error():
									SCREAMING_SNAKE_CASE						= [error_accuracy for i in range(int(n_repeat * 1.2))]
									plt.plot(lowerCamelCase_		,					'+-')
									plt.plot(lowerCamelCase_		,					'r--')
									plt.xlabel('Learning Times')
									plt.ylabel('All_mse')
									plt.grid(lowerCamelCase_		,					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		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= []
						print('-------------------Start Testing-------------------------')
						print((' - - Shape: Test_Data  ', np.shape(lowerCamelCase_)))
						for p in range(len(lowerCamelCase_)):
									SCREAMING_SNAKE_CASE						= np.asmatrix(datas_test[p])
									SCREAMING_SNAKE_CASE						= self.convolute(
									    lowerCamelCase_		,					self.conva		,					self.w_conva		,					self.thre_conva		,					conv_step=self.step_conva		,					)
									SCREAMING_SNAKE_CASE						= self.pooling(lowerCamelCase_		,					self.size_poolinga)
									SCREAMING_SNAKE_CASE						= self._expand(lowerCamelCase_)

									SCREAMING_SNAKE_CASE						= data_bp_input
									SCREAMING_SNAKE_CASE						= bp_outa * self.vji.T - self.thre_bpa
									SCREAMING_SNAKE_CASE						= self.sig(lowerCamelCase_)
									SCREAMING_SNAKE_CASE						= bp_outa * self.wkj.T - self.thre_bpa
									SCREAMING_SNAKE_CASE						= self.sig(lowerCamelCase_)
									produce_out.extend(bp_outa.getA().tolist())
						SCREAMING_SNAKE_CASE						= [list(map(self.do_round		,					lowerCamelCase_)) for each in produce_out]
						return np.asarray(lowerCamelCase_)







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= np.asmatrix(lowerCamelCase_)
						SCREAMING_SNAKE_CASE						= self.convolute(
						    lowerCamelCase_		,					self.conva		,					self.w_conva		,					self.thre_conva		,					conv_step=self.step_conva		,					)
						SCREAMING_SNAKE_CASE						= self.pooling(lowerCamelCase_		,					self.size_poolinga)

						return data_conveda, data_pooleda


if __name__ == "__main__":
				pass












import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


a_		:						List[str]             =    R'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'

@add_start_docstrings(A__					)
class 		_snake_case  (  A__					):
			_lowercase		:    Optional[int]			     =							'''rag'''
			_lowercase		:    Optional[int]			     =							True






			def __init__(       self		,					a=None		,					a=True		,					a=None		,					a=None		,					a=None		,					a=None		,					a=None		,					a=" / "		,					a=" // "		,					a=5		,					a=300		,					a=768		,					a=8		,					a="wiki_dpr"		,					a="train"		,					a="compressed"		,					a=None		,					a=None		,					a=False		,					a=False		,					a=0.0		,					a=True		,					a=False		,					a=False		,					a=False		,					a=True		,					a=None		,					**a		,					)      ->       Optional[Any]:
						super().__init__(
						    bos_token_id=a		,					pad_token_id=a		,					eos_token_id=a		,					decoder_start_token_id=a		,					forced_eos_token_id=a		,					is_encoder_decoder=a		,					prefix=a		,					vocab_size=a		,					**a		,					)
						assert (
						    "question_encoder" in kwargs and "generator" in kwargs
						), "Config has to be initialized with question_encoder and generator config"
						SCREAMING_SNAKE_CASE						= kwargs.pop('question_encoder')
						SCREAMING_SNAKE_CASE						= question_encoder_config.pop('model_type')
						SCREAMING_SNAKE_CASE						= kwargs.pop('generator')
						SCREAMING_SNAKE_CASE						= decoder_config.pop('model_type')

						from ..auto.configuration_auto import AutoConfig

						SCREAMING_SNAKE_CASE						= AutoConfig.for_model(a		,					**a)
						SCREAMING_SNAKE_CASE						= AutoConfig.for_model(a		,					**a)

						SCREAMING_SNAKE_CASE						= reduce_loss
						SCREAMING_SNAKE_CASE						= label_smoothing
						SCREAMING_SNAKE_CASE						= exclude_bos_score
						SCREAMING_SNAKE_CASE						= do_marginalize

						SCREAMING_SNAKE_CASE						= title_sep
						SCREAMING_SNAKE_CASE						= doc_sep
						SCREAMING_SNAKE_CASE						= n_docs
						SCREAMING_SNAKE_CASE						= max_combined_length

						SCREAMING_SNAKE_CASE						= dataset
						SCREAMING_SNAKE_CASE						= dataset_split
						SCREAMING_SNAKE_CASE						= index_name

						SCREAMING_SNAKE_CASE						= retrieval_vector_size
						SCREAMING_SNAKE_CASE						= retrieval_batch_size
						SCREAMING_SNAKE_CASE						= passages_path
						SCREAMING_SNAKE_CASE						= index_path
						SCREAMING_SNAKE_CASE						= use_dummy_dataset

						SCREAMING_SNAKE_CASE						= output_retrieved

						SCREAMING_SNAKE_CASE						= do_deduplication

						SCREAMING_SNAKE_CASE						= use_cache

						if self.forced_eos_token_id is None:
									SCREAMING_SNAKE_CASE						= getattr(self.generator		,					'forced_eos_token_id'		,					a)






			@classmethod
			def    SCREAMING_SNAKE_CASE__    (       cls		,					a		,					a		,					**a)      ->       PretrainedConfig:
						return cls(question_encoder=question_encoder_config.to_dict()		,					generator=generator_config.to_dict()		,					**a)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= copy.deepcopy(self.__dict__)
						SCREAMING_SNAKE_CASE						= self.question_encoder.to_dict()
						SCREAMING_SNAKE_CASE						= self.generator.to_dict()
						SCREAMING_SNAKE_CASE						= self.__class__.model_type
						return output




