





'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase				:      int						      =logging.get_logger(__name__)

__lowerCAmelCase				:      Optional[int]						      ="▁"

__lowerCAmelCase				:      str						      ={"vocab_file": "sentencepiece.bpe.model"}

__lowerCAmelCase				:      List[Any]						      ={
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

__lowerCAmelCase				:      str						      ={
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
__lowerCAmelCase				:      Tuple						      =["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							VOCAB_FILES_NAMES
 __lowercase  						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase  						=							PRETRAINED_VOCAB_FILES_MAP
 __lowercase  						=							["""input_ids""", """attention_mask"""]

 __lowercase  						=							[]
 __lowercase  						=							[]
 def __init__(							self :Optional[Any]		,			lowercase_ :str		,			lowercase_ :Dict="<s>"		,			lowercase_ :Optional[int]="</s>"		,			lowercase_ :List[Any]="</s>"		,			lowercase_ :Dict="<s>"		,			lowercase_ :Dict="<unk>"		,			lowercase_ :Optional[int]="<pad>"		,			lowercase_ :Tuple="<mask>"		,			lowercase_ :Any=None		,			lowercase_ :Union[str, Any]=None		,			lowercase_ :Tuple=None		,			lowercase_ :Optional[Dict[str, Any]] = None		,			lowercase_ :Optional[Any]=None		,			lowercase_ :List[str]=False		,			**lowercase_ :List[str]		,			)->				Optional[int]:
     # Mask token behave like a normal word, i.e. include the space before it
     A__							=     AddedToken(lowercase_		,			lstrip=lowercase_		,			rstrip=lowercase_				) if isinstance(lowercase_		,			lowercase_				) else mask_token

     A__							=     {} if sp_model_kwargs is None else sp_model_kwargs
     A__							=     legacy_behaviour

     super().__init__(
         bos_token=lowercase_		,			eos_token=lowercase_		,			unk_token=lowercase_		,			sep_token=lowercase_		,			cls_token=lowercase_		,			pad_token=lowercase_		,			mask_token=lowercase_		,			tokenizer_file=lowercase_		,			src_lang=lowercase_		,			tgt_lang=lowercase_		,			additional_special_tokens=lowercase_		,			sp_model_kwargs=self.sp_model_kwargs		,			legacy_behaviour=lowercase_		,			**lowercase_		,			)

     A__							=     spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.Load(str(lowercase_				)				)
     A__							=     vocab_file
     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
     # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
     # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

     # Mimic fairseq token-to-id alignment for the first 4 token
     A__							=     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     A__							=     1

     A__							=     len(self.sp_model				)
     A__							=     {
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase_				)
     }
     A__							=     {v: k for k, v in self.lang_code_to_id.items()}
     A__							=     len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id				)
     A__							=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
     A__							=     list(self.lang_code_to_id.keys()				)

     if additional_special_tokens is not None:
         # Only add those special tokens if they are not already there.
         self._additional_special_tokens.extend(
             [t for t in additional_special_tokens if t not in self._additional_special_tokens]				)

     A__							=     src_lang if src_lang is not None else "eng_Latn"
     A__							=     self.lang_code_to_id[self._src_lang]
     A__							=     tgt_lang
     self.set_src_lang_special_tokens(self._src_lang				)
 def __getstate__(							self :Tuple				)->				int:
     A__							=     self.__dict__.copy()
     A__							=     None
     A__							=     self.sp_model.serialized_model_proto()
     return state
 def __setstate__(							self :Union[str, Any]		,			lowercase_ :str				)->				Dict:
     A__							=     d

     # for backward compatibility
     if not hasattr(self		,			"sp_model_kwargs"				):
         A__							=     {}

     A__							=     spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				str:
     return len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset + 1  # Plus 1 for the mask token
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     return self._src_lang
 @src_lang.setter
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :str				)->				None:
     A__							=     new_src_lang
     self.set_src_lang_special_tokens(self._src_lang				)
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[int]		,			lowercase_ :Optional[List[int]] = None		,			lowercase_ :bool = False				)->				List[int]:

     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowercase_		,			token_ids_a=lowercase_		,			already_has_special_tokens=lowercase_				)

     A__							=     [1] * len(self.prefix_tokens				)
     A__							=     [1] * len(self.suffix_tokens				)
     if token_ids_a is None:
         return prefix_ones + ([0] * len(lowercase_				)) + suffix_ones
     return prefix_ones + ([0] * len(lowercase_				)) + ([0] * len(lowercase_				)) + suffix_ones
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :List[int]		,			lowercase_ :Optional[List[int]] = None				)->				List[int]:
     if token_ids_a is None:
         return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :List[int]		,			lowercase_ :Optional[List[int]] = None				)->				List[int]:
     A__							=     [self.sep_token_id]
     A__							=     [self.cls_token_id]

     if token_ids_a is None:
         return len(cls + token_ids_a + sep				) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :List[Any]		,			lowercase_ :str		,			lowercase_ :Optional[str]		,			lowercase_ :Optional[str]		,			**lowercase_ :List[str]				)->				str:
     if src_lang is None or tgt_lang is None:
         raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"				)
     A__							=     src_lang
     A__							=     self(lowercase_		,			add_special_tokens=lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)
     A__							=     self.convert_tokens_to_ids(lowercase_				)
     A__							=     tgt_lang_id
     return inputs
 def 	UpperCAmelCase_				(							self :int				)->				str:
     A__							=     {self.convert_ids_to_tokens(lowercase_				): i for i in range(self.vocab_size				)}
     vocab.update(self.added_tokens_encoder				)
     return vocab
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :str				)->				List[str]:
     return self.sp_model.encode(lowercase_		,			out_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :str				)->				str:
     if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
     A__							=     self.sp_model.PieceToId(lowercase_				)

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :Optional[int]				)->				Any:
     if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset				)
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :str				)->				List[str]:
     A__							=     "".join(lowercase_				).replace(lowercase_		,			" "				).strip()
     return out_string
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :str		,			lowercase_ :Optional[str] = None				)->				Tuple[str]:
     if not os.path.isdir(lowercase_				):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
         return
     A__							=     os.path.join(
         lowercase_		,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

     if os.path.abspath(self.vocab_file				) != os.path.abspath(lowercase_				) and os.path.isfile(self.vocab_file				):
         copyfile(self.vocab_file		,			lowercase_				)
     elif not os.path.isfile(self.vocab_file				):
         with open(lowercase_		,			"wb"				) as fi:
             A__							=     self.sp_model.serialized_model_proto()
             fi.write(lowercase_				)

     return (out_vocab_file,)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :List[str]		,			lowercase_ :str = "eng_Latn"		,			lowercase_ :Optional[List[str]] = None		,			lowercase_ :str = "fra_Latn"		,			**lowercase_ :int		,			)->				BatchEncoding:
     A__							=     src_lang
     A__							=     tgt_lang
     return super().prepare_seqaseq_batch(lowercase_		,			lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				int:
     return self.set_src_lang_special_tokens(self.src_lang				)
 def 	UpperCAmelCase_				(							self :Any				)->				int:
     return self.set_tgt_lang_special_tokens(self.tgt_lang				)
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :Optional[Any]				)->				None:
     A__							=     self.lang_code_to_id[src_lang]
     if self.legacy_behaviour:
         A__							=     []
         A__							=     [self.eos_token_id, self.cur_lang_code]
     else:
         A__							=     [self.cur_lang_code]
         A__							=     [self.eos_token_id]



 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :str				)->				None:
     A__							=     self.lang_code_to_id[lang]
     if self.legacy_behaviour:
         A__							=     []
         A__							=     [self.eos_token_id, self.cur_lang_code]
     else:
         A__							=     [self.cur_lang_code]
         A__							=     [self.eos_token_id]










'''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



