



'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
	from PIL import Image


class 	a__     (				_lowercase	):
	__magic_name__		:    Tuple	    =     "naver-clova-ix/donut-base-finetuned-docvqa"
	__magic_name__		:    List[Any]	    =     (
	    "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
	    "should be the document containing the information, as well as a `question` that is the question about the "
	    "document. It returns a text that contains the answer to the question."
	)
	__magic_name__		:    Optional[Any]	    =     "document_qa"
	__magic_name__		:    int	    =     AutoProcessor
	__magic_name__		:    str	    =     VisionEncoderDecoderModel

	__magic_name__		:    int	    =     ["image", "text"]
	__magic_name__		:    List[str]	    =     ["text"]


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

					"""simple docstring"""

					if not is_vision_available():
									raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.'''      )

					super().__init__(*__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : "Image",					__UpperCAmelCase : str      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
					SCREAMING_SNAKE_CASE  :						Dict      					= task_prompt.replace('''{user_input}''',					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pre_processor.tokenizer(
					    __UpperCAmelCase,					add_special_tokens=__UpperCAmelCase,					return_tensors='''pt'''      ).input_ids
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.pre_processor(__UpperCAmelCase,					return_tensors='''pt'''      ).pixel_values

					return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Dict      )     ->			List[str]:

					"""simple docstring"""

					return self.model.generate(
					    inputs['''pixel_values'''].to(self.device      ),					decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device      ),					max_length=self.model.decoder.config.max_position_embeddings,					early_stopping=__UpperCAmelCase,					pad_token_id=self.pre_processor.tokenizer.pad_token_id,					eos_token_id=self.pre_processor.tokenizer.eos_token_id,					use_cache=__UpperCAmelCase,					num_beams=1,					bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]],					return_dict_in_generate=__UpperCAmelCase,					).sequences






	def 				lowercase__  (self : int,					__UpperCAmelCase : Union[str, Any]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.pre_processor.batch_decode(__UpperCAmelCase      )[0]
					SCREAMING_SNAKE_CASE  :						Dict      					= sequence.replace(self.pre_processor.tokenizer.eos_token,					''''''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= sequence.replace(self.pre_processor.tokenizer.pad_token,					''''''      )
					SCREAMING_SNAKE_CASE  :						int      					= re.sub(R'''<.*?>''',					'''''',					__UpperCAmelCase,					count=1      ).strip()  # remove first task start token
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.pre_processor.tokenajson(__UpperCAmelCase      )

					return sequence["answer"]









'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt


class 	a__     :


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

					"""simple docstring"""

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


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= {
					    '''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 : str,					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

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

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


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Dict      )     ->			Optional[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : List[str]      )     ->			str:

					"""simple docstring"""

					return round(__UpperCAmelCase,					3      )


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Any,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= convs[0]
					SCREAMING_SNAKE_CASE  :						int      					= convs[1]
					SCREAMING_SNAKE_CASE  :						List[str]      					= np.shape(__UpperCAmelCase      )[0]
					# get the data slice of original image data, data_focus
					SCREAMING_SNAKE_CASE  :						Union[str, 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      ):
													SCREAMING_SNAKE_CASE  :						Optional[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
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= int((size_data - size_conv) / conv_step + 1      )
					for i_map in range(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
									for i_focus in range(len(__UpperCAmelCase      )      ):
													SCREAMING_SNAKE_CASE  :						Any      					= (
													    np.sum(np.multiply(data_focus[i_focus],					w_convs[i_map]      )      )
													    - thre_convs[i_map]
													)
													featuremap.append(self.sig(__UpperCAmelCase      )      )
									SCREAMING_SNAKE_CASE  :						Dict      					= np.asmatrix(__UpperCAmelCase      ).reshape(
									    __UpperCAmelCase,					__UpperCAmelCase      )
									data_featuremap.append(__UpperCAmelCase      )

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


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]="average_pool"      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= len(featuremaps[0]      )
					SCREAMING_SNAKE_CASE  :						str      					= int(size_map / size_pooling      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= []
					for i_map in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						str      					= featuremaps[i_map]
									SCREAMING_SNAKE_CASE  :						Any      					= []
									for i_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
													for j_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						str      					= 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      )      )
									SCREAMING_SNAKE_CASE  :						Tuple      					= np.asmatrix(__UpperCAmelCase      ).reshape(__UpperCAmelCase,					__UpperCAmelCase      )
									featuremap_pooled.append(__UpperCAmelCase      )
					return featuremap_pooled


	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			Optional[int]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= np.asarray(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= np.shape(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= data_mat.reshape(1,					shapes[0] * shapes[1]      )
					return data_expanded


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[int]=bool      )     ->			List[Any]:

					"""simple docstring"""

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

													SCREAMING_SNAKE_CASE  :						Tuple      					= np.dot(__UpperCAmelCase,					self.vji.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						List[Any]      					= self.sig(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.dot(__UpperCAmelCase,					self.wkj.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.sig(__UpperCAmelCase      )

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

													SCREAMING_SNAKE_CASE  :						Dict      					= pd_i_all / (self.size_poolinga * self.size_poolinga)
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pd_conva_pooled.T.getA().tolist()
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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]      ):
																	SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self._expand_mat(pd_conva_all[k_conv]      )
																	SCREAMING_SNAKE_CASE  :						Any      					= self.rate_weight * np.dot(__UpperCAmelCase,					__UpperCAmelCase      )

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

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

					def draw_error():
									SCREAMING_SNAKE_CASE  :						Tuple      					= [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 : List[str],					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					print('''-------------------Start Testing-------------------------'''      )
					print((''' - - Shape: Test_Data  ''', np.shape(__UpperCAmelCase      ))      )
					for p in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(datas_test[p]      )
									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= self.convolute(
									    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
									SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )
									SCREAMING_SNAKE_CASE  :						str      					= self._expand(__UpperCAmelCase      )

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






	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

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

					return data_conveda, data_pooleda


if __name__ == "__main__":
	pass




