

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			List[str]      		=       RobertaTokenizer
      _lowerCamelCase       :			Tuple      		=       RobertaTokenizerFast
      _lowerCamelCase       :			str      		=       True
      _lowerCamelCase       :			Tuple      		=       {'cls_token': '<s>'}





      def 		__A	(   self							:				Any	):
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            A_						  =				[
                "l",
                "o",
                "w",
                "e",
                "r",
                "s",
                "t",
                "i",
                "d",
                "n",
                "\u0120",
                "\u0120l",
                "\u0120n",
                "\u0120lo",
                "\u0120low",
                "er",
                "\u0120lowest",
                "\u0120newer",
                "\u0120wider",
                "<unk>",
            ]
            A_						  =				dict(zip(UpperCAmelCase						,			range(len(UpperCAmelCase	)	)	)	)
            A_						  =				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
            A_						  =				{"unk_token": "<unk>"}

            A_						  =				os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["vocab_file"]	)
            A_						  =				os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["merges_file"]	)
            with open(self.vocab_file						,			"w"						,			encoding="utf-8"	) as fp:
                  fp.write(json.dumps(UpperCAmelCase	) + "\n"	)
            with open(self.merges_file						,			"w"						,			encoding="utf-8"	) as fp:
                  fp.write("\n".join(UpperCAmelCase	)	)





      def 		__A	(   self							:				Any						,			**UpperCAmelCase							:				Union[str, Any]	):
            kwargs.update(self.special_tokens_map	)
            return self.tokenizer_class.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				List[Any]						,			**UpperCAmelCase							:				List[str]	):
            kwargs.update(self.special_tokens_map	)
            return RobertaTokenizerFast.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				Optional[int]	):
            A_						  =				"lower newer"
            A_						  =				"lower newer"
            return input_text, output_text





      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				self.tokenizer_class(self.vocab_file						,			self.merges_file						,			**self.special_tokens_map	)
            A_						  =				"lower newer"
            A_						  =				["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
            A_						  =				tokenizer.tokenize(UpperCAmelCase	)  # , add_prefix_space=True)
            self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokens + [tokenizer.unk_token]
            A_						  =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase	)						,			UpperCAmelCase	)





      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				self.get_tokenizer()

            self.assertListEqual(tokenizer.encode("Hello world!"						,			add_special_tokens=UpperCAmelCase	)						,			[0, 31414, 232, 328, 2]	)
            self.assertListEqual(
                tokenizer.encode("Hello world! cécé herlolip 418"						,			add_special_tokens=UpperCAmelCase	)						,			[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]						,			)





      @slow
      def 		__A	(   self							:				int	):
            A_						  =				self.tokenizer_class.from_pretrained("roberta-base"	)

            A_						  =				tokenizer.encode("sequence builders"						,			add_special_tokens=UpperCAmelCase	)
            A_						  =				tokenizer.encode("multi-sequence build"						,			add_special_tokens=UpperCAmelCase	)

            A_						  =				tokenizer.encode(
                "sequence builders"						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)
            A_						  =				tokenizer.encode(
                "sequence builders"						,			"multi-sequence build"						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)

            A_						  =				tokenizer.build_inputs_with_special_tokens(UpperCAmelCase	)
            A_						  =				tokenizer.build_inputs_with_special_tokens(UpperCAmelCase						,			UpperCAmelCase	)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode





      def 		__A	(   self							:				Tuple	):
            A_						  =				self.get_tokenizer()

            A_						  =				"Encode this sequence."
            A_						  =				tokenizer.byte_encoder[" ".encode("utf-8"	)[0]]

            # Testing encoder arguments
            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
            self.assertNotEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)

            tokenizer.add_special_tokens({"bos_token": "<s>"}	)
            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[1]	)[0]
            self.assertNotEqual(UpperCAmelCase						,			UpperCAmelCase	)

            # Testing spaces after special tokens
            A_						  =				"<mask>"
            tokenizer.add_special_tokens(
                {"mask_token": AddedToken(UpperCAmelCase						,			lstrip=UpperCAmelCase						,			rstrip=UpperCAmelCase	)}	)  # mask token has a left space
            A_						  =				tokenizer.convert_tokens_to_ids(UpperCAmelCase	)

            A_						  =				"Encode <mask> sequence"
            A_						  =				"Encode <mask>sequence"

            A_						  =				tokenizer.encode(UpperCAmelCase	)
            A_						  =				encoded.index(UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokenizer.encode(UpperCAmelCase	)
            A_						  =				encoded.index(UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
            self.assertNotEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Any	):
            pass





      def 		__A	(   self							:				Optional[int]	):
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                        A_						  =				self.rust_tokenizer_class.from_pretrained(UpperCAmelCase						,			**UpperCAmelCase	)
                        A_						  =				self.tokenizer_class.from_pretrained(UpperCAmelCase						,			**UpperCAmelCase	)
                        A_						  =				"A, <mask> AllenNLP sentence."
                        A_						  =				tokenizer_r.encode_plus(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			return_token_type_ids=UpperCAmelCase	)
                        A_						  =				tokenizer_p.encode_plus(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			return_token_type_ids=UpperCAmelCase	)

                        # token_type_ids should put 0 everywhere
                        self.assertEqual(sum(tokens_r["token_type_ids"]	)						,			sum(tokens_p["token_type_ids"]	)	)

                        # attention_mask should put 1 everywhere, so sum over length should be 1
                        self.assertEqual(
                            sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)						,			sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)						,			)

                        A_						  =				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
                        A_						  =				tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

                        # Rust correctly handles the space before the mask while python doesnt
                        self.assertSequenceEqual(tokens_p["input_ids"]						,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)
                        self.assertSequenceEqual(tokens_r["input_ids"]						,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

                        self.assertSequenceEqual(
                            UpperCAmelCase						,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
                        self.assertSequenceEqual(
                            UpperCAmelCase						,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)





      def 		__A	(   self							:				Union[str, Any]	):
            for trim_offsets, add_prefix_space in itertools.product([True, False]						,			repeat=2	):
                  A_						  =				self.rust_tokenizer_class.from_pretrained(
                      self.tmpdirname						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)

                  A_						  =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()	)
                  A_						  =				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()	)

                  self.assertEqual(pre_tokenizer_state["add_prefix_space"]						,			UpperCAmelCase	)

                  self.assertEqual(post_processor_state["add_prefix_space"]						,			UpperCAmelCase	)
                  self.assertEqual(post_processor_state["trim_offsets"]						,			UpperCAmelCase	)







      def 		__A	(   self							:				List[Any]	):
            # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
            # `trim_offsets`
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                        A_						  =				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                        A_						  =				f'''{text_of_1_token} {text_of_1_token}'''

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	) + 1, len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	) + 1, len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	), len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	), len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				f''' {text}'''

                        # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                        #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                        # )
                        # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                        # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                        # self.assertEqual(
                        #     encoding.offset_mapping[1],
                        #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                        # )

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(1, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	) + 1, 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	), 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	), 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)






'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt






class   UpperCAmelCase      :
 def __init__(							self :Dict		,			lowercase_ :Union[str, Any]		,			lowercase_ :Tuple		,			lowercase_ :List[Any]		,			lowercase_ :List[str]		,			lowercase_ :Dict		,			lowercase_ :Tuple=0.2		,			lowercase_ :List[str]=0.2				)->				Any:
     A__							=     bp_numa
     A__							=     bp_numa
     A__							=     bp_numa
     A__							=     conva_get[:2]
     A__							=     conva_get[2]
     A__							=     size_pa
     A__							=     rate_w
     A__							=     rate_t
     A__							=     [
         np.mat(-1 * np.random.rand(self.conva[0]		,			self.conva[0]				) + 0.5				)
         for i in range(self.conva[1]				)
     ]
     A__							=     np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa				) + 0.5				)
     A__							=     np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa				) + 0.5				)
     A__							=     -2 * np.random.rand(self.conva[1]				) + 1
     A__							=     -2 * np.random.rand(self.num_bpa				) + 1
     A__							=     -2 * np.random.rand(self.num_bpa				) + 1
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :Optional[Any]				)->				Optional[Any]:
     # save model dict with pickle
     A__							=     {
         "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 	UpperCAmelCase_				(							cls :Any		,			lowercase_ :List[Any]				)->				Optional[Any]:
     # read saved model
     with open(lowercase_		,			"rb"				) as f:
         A__							=     pickle.load(lowercase_				)  # noqa: S301

     A__							=     model_dic.get("conv1"				)
     conv_get.append(model_dic.get("step_conv1"				)				)
     A__							=     model_dic.get("size_pooling1"				)
     A__							=     model_dic.get("num_bp1"				)
     A__							=     model_dic.get("num_bp2"				)
     A__							=     model_dic.get("num_bp3"				)
     A__							=     model_dic.get("rate_weight"				)
     A__							=     model_dic.get("rate_thre"				)
     # create model instance
     A__							=     CNN(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_				)
     # modify model parameter
     A__							=     model_dic.get("w_conv1"				)
     A__							=     model_dic.get("wkj"				)
     A__							=     model_dic.get("vji"				)
     A__							=     model_dic.get("thre_conv1"				)
     A__							=     model_dic.get("thre_bp2"				)
     A__							=     model_dic.get("thre_bp3"				)
     return conv_ins
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :Optional[int]				)->				Optional[Any]:
     return 1 / (1 + np.exp(-1 * x				))
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :Optional[Any]				)->				Any:
     return round(lowercase_		,			3				)
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Tuple		,			lowercase_ :List[Any]		,			lowercase_ :Tuple		,			lowercase_ :Union[str, Any]		,			lowercase_ :Dict				)->				str:
     # convolution process
     A__							=     convs[0]
     A__							=     convs[1]
     A__							=     np.shape(lowercase_				)[0]
     # get the data slice of original image data, data_focus
     A__							=     []
     for i_focus in range(0		,			size_data - size_conv + 1		,			lowercase_				):
         for j_focus in range(0		,			size_data - size_conv + 1		,			lowercase_				):
             A__							=     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
     A__							=     []
     A__							=     int((size_data - size_conv) / conv_step + 1				)
     for i_map in range(lowercase_				):
         A__							=     []
         for i_focus in range(len(lowercase_				)				):
             A__							=     (
                 np.sum(np.multiply(data_focus[i_focus]		,			w_convs[i_map]				)				)
                 - thre_convs[i_map]
             )
             featuremap.append(self.sig(lowercase_				)				)
         A__							=     np.asmatrix(lowercase_				).reshape(
             lowercase_		,			lowercase_				)
         data_featuremap.append(lowercase_				)

     # expanding the data slice to One dimenssion
     A__							=     []
     for each_focus in data_focus:
         focusa_list.extend(self.Expand_Mat(lowercase_				)				)
     A__							=     np.asarray(lowercase_				)
     return focus_list, data_featuremap
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :Optional[int]		,			lowercase_ :Union[str, Any]		,			lowercase_ :Optional[Any]="average_pool"				)->				Dict:
     # pooling process
     A__							=     len(featuremaps[0]				)
     A__							=     int(size_map / size_pooling				)
     A__							=     []
     for i_map in range(len(lowercase_				)				):
         A__							=     featuremaps[i_map]
         A__							=     []
         for i_focus in range(0		,			lowercase_		,			lowercase_				):
             for j_focus in range(0		,			lowercase_		,			lowercase_				):
                 A__							=     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_				)				)
         A__							=     np.asmatrix(lowercase_				).reshape(lowercase_		,			lowercase_				)
         featuremap_pooled.append(lowercase_				)
     return featuremap_pooled
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :Any				)->				List[Any]:
     # expanding three dimension data to one dimension list
     A__							=     []
     for i in range(len(lowercase_				)				):
         A__							=     np.shape(data[i]				)
         A__							=     data[i].reshape(1		,			shapes[0] * shapes[1]				)
         A__							=     data_listed.getA().tolist()[0]
         data_expanded.extend(lowercase_				)
     A__							=     np.asarray(lowercase_				)
     return data_expanded
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Optional[Any]				)->				Dict:
     # expanding matrix to one dimension list
     A__							=     np.asarray(lowercase_				)
     A__							=     np.shape(lowercase_				)
     A__							=     data_mat.reshape(1		,			shapes[0] * shapes[1]				)
     return data_expanded
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :List[str]		,			lowercase_ :Any		,			lowercase_ :str		,			lowercase_ :Any		,			lowercase_ :Union[str, Any]				)->				Any:
     A__							=     []
     A__							=     0
     for i_map in range(lowercase_				):
         A__							=     np.ones((size_map, size_map)				)
         for i in range(0		,			lowercase_		,			lowercase_				):
             for j in range(0		,			lowercase_		,			lowercase_				):
                 A__							=     pd_pool[
                     i_pool
                 ]
                 A__							=     i_pool + 1
         A__							=     np.multiply(
             lowercase_		,			np.multiply(out_map[i_map]		,			(1 - out_map[i_map])				)				)
         pd_all.append(lowercase_				)
     return pd_all
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :Optional[Any]		,			lowercase_ :Optional[Any]		,			lowercase_ :Union[str, Any]		,			lowercase_ :List[Any]		,			lowercase_ :Union[str, Any]		,			lowercase_ :str=bool				)->				Tuple:
     # model traning
     print("----------------------Start Training-------------------------"				)
     print((" - - Shape: Train_Data  ", np.shape(lowercase_				))				)
     print((" - - Shape: Teach_Data  ", np.shape(lowercase_				))				)
     A__							=     0
     A__							=     []
     A__							=     1_00_00
     while rp < n_repeat and mse >= error_accuracy:
         A__							=     0
         print(F"-------------Learning Time {rp}--------------"				)
         for p in range(len(lowercase_				)				):
             # print('------------Learning Image: %d--------------'%p)
             A__							=     np.asmatrix(datas_train[p]				)
             A__							=     np.asarray(datas_teach[p]				)
             A__,      A__							=     self.convolute(
                 lowercase_		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
             A__							=     self.pooling(lowercase_		,			self.size_poolinga				)
             A__							=     np.shape(lowercase_				)
             A__							=     self._expand(lowercase_				)
             A__							=     data_bp_input

             A__							=     np.dot(lowercase_		,			self.vji.T				) - self.thre_bpa
             A__							=     self.sig(lowercase_				)
             A__							=     np.dot(lowercase_		,			self.wkj.T				) - self.thre_bpa
             A__							=     self.sig(lowercase_				)

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

             A__							=     pd_i_all / (self.size_poolinga * self.size_poolinga)
             A__							=     pd_conva_pooled.T.getA().tolist()
             A__							=     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]				):
                 A__							=     self._expand_mat(pd_conva_all[k_conv]				)
                 A__							=     self.rate_weight * np.dot(lowercase_		,			lowercase_				)

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

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

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

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



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

     return data_conveda, data_pooleda


if __name__ == "__main__":
  pass



