





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase       :     Optional[int]   =     get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
    from transformers.models.mbart.modeling_mbart import shift_tokens_right

__lowerCamelCase       :     Optional[Any]   =     25_0004
__lowerCamelCase       :     Optional[Any]   =     25_0020



@require_sentencepiece
@require_tokenizers
class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[int]						    =   MBartTokenizer
      _UpperCAmelCase	:int						    =   MBartTokenizerFast
      _UpperCAmelCase	:Dict						    =   True
      _UpperCAmelCase	:Any						    =   True





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase	:					List[Any]       	=   MBartTokenizer(A_       ,							keep_accents=A_   )
       tokenizer.save_pretrained(self.tmpdirname   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   MBartTokenizer(A_       ,							keep_accents=A_   )

       UpperCamelCase	:					str       	=   tokenizer.tokenize("This is a test"   )
       self.assertListEqual(A_       ,							["▁This", "▁is", "▁a", "▁t", "est"]   )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(A_   )       ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,							)

       UpperCamelCase	:					Optional[Any]       	=   tokenizer.tokenize("I was born in 92000, and this is falsé."   )
       self.assertListEqual(
           A_       ,							[
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "9",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "é",
               ".",
           ]       ,							)
       UpperCamelCase	:					Any       	=   tokenizer.convert_tokens_to_ids(A_   )
       self.assertListEqual(
           A_       ,							[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ]       ,							)

       UpperCamelCase	:					str       	=   tokenizer.convert_ids_to_tokens(A_   )
       self.assertListEqual(
           A_       ,							[
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "<unk>",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "<unk>",
               ".",
           ]       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if not self.test_slow_tokenizer:
        # as we don't have a slow version, we can't compare the outputs between slow and fast versions
        return

       UpperCamelCase	:					Union[str, Any]       	=   (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
         UpperCamelCase	:					Any       	=   self.rust_tokenizer_class.from_pretrained(A_       ,							**A_   )
         UpperCamelCase	:					Any       	=   self.tokenizer_class.from_pretrained(A_       ,							**A_   )

         UpperCamelCase	:					Union[str, Any]       	=   tempfile.mkdtemp()

         UpperCamelCase	:					Any       	=   tokenizer_r.save_pretrained(A_   )
         UpperCamelCase	:					str       	=   tokenizer_p.save_pretrained(A_   )

         # Checks it save with the same files + the tokenizer.json file for the fast one
         self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )
         UpperCamelCase	:					Dict       	=   tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
         self.assertSequenceEqual(A_       ,							A_   )

         # Checks everything loads correctly in the same way
         UpperCamelCase	:					Dict       	=   tokenizer_r.from_pretrained(A_   )
         UpperCamelCase	:					List[str]       	=   tokenizer_p.from_pretrained(A_   )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
          self.assertTrue(hasattr(A_       ,							A_   )   )
          # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
          # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

         shutil.rmtree(A_   )

         # Save tokenizer rust, legacy_format=True
         UpperCamelCase	:					Any       	=   tempfile.mkdtemp()

         UpperCamelCase	:					List[str]       	=   tokenizer_r.save_pretrained(A_       ,							legacy_format=A_   )
         UpperCamelCase	:					Dict       	=   tokenizer_p.save_pretrained(A_   )

         # Checks it save with the same files
         self.assertSequenceEqual(A_       ,							A_   )

         # Checks everything loads correctly in the same way
         UpperCamelCase	:					Any       	=   tokenizer_r.from_pretrained(A_   )
         UpperCamelCase	:					Optional[Any]       	=   tokenizer_p.from_pretrained(A_   )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
          self.assertTrue(hasattr(A_       ,							A_   )   )

         shutil.rmtree(A_   )

         # Save tokenizer rust, legacy_format=False
         UpperCamelCase	:					str       	=   tempfile.mkdtemp()

         UpperCamelCase	:					int       	=   tokenizer_r.save_pretrained(A_       ,							legacy_format=A_   )
         UpperCamelCase	:					List[Any]       	=   tokenizer_p.save_pretrained(A_   )

         # Checks it saved the tokenizer.json file
         self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )

         # Checks everything loads correctly in the same way
         UpperCamelCase	:					Union[str, Any]       	=   tokenizer_r.from_pretrained(A_   )
         UpperCamelCase	:					Dict       	=   tokenizer_p.from_pretrained(A_   )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
          self.assertTrue(hasattr(A_       ,							A_   )   )

         shutil.rmtree(A_   )



@require_torch
@require_sentencepiece
@require_tokenizers
class 					A__   (       unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   'facebook/mbart-large-en-ro'
      _UpperCAmelCase	:Optional[Any]						    =   [
          ' UN Chief Says There Is No Military Solution in Syria',
          ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
      ]
      _UpperCAmelCase	:int						    =   [
          'Şeful ONU declară că nu există o soluţie militară în Siria',
          'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
          ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
          ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
      ]
      _UpperCAmelCase	:Dict						    =   [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]





      @classmethod
      def        __UpperCamelCase( cls   ):
       '''simple docstring'''




       UpperCamelCase	:					MBartTokenizer       	=   MBartTokenizer.from_pretrained(
           cls.checkpoint_name       ,							src_lang="en_XX"       ,							tgt_lang="ro_RO"   )
       UpperCamelCase	:					Tuple       	=   1
       return cls





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]       ,							25_0001   )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]       ,							25_0004   )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]       ,							25_0020   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertIn(A_       ,							self.tokenizer.all_special_ids   )
       UpperCamelCase	:					int       	=   [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
       UpperCamelCase	:					Optional[Any]       	=   self.tokenizer.decode(A_       ,							skip_special_tokens=A_   )
       UpperCamelCase	:					str       	=   self.tokenizer.decode(generated_ids[1:]       ,							skip_special_tokens=A_   )
       self.assertEqual(A_       ,							A_   )
       self.assertNotIn(self.tokenizer.eos_token       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   ["this is gunna be a long sentence " * 20]
       assert isinstance(src_text[0]       ,							A_   )
       UpperCamelCase	:					Union[str, Any]       	=   10
       UpperCamelCase	:					List[Any]       	=   self.tokenizer(A_       ,							max_length=A_       ,							truncation=A_   ).input_ids[0]
       self.assertEqual(ids[-2]       ,							2   )
       self.assertEqual(ids[-1]       ,							A_   )
       self.assertEqual(len(A_   )       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   )       ,							[25_0026, 25_0001]   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   tempfile.mkdtemp()
       UpperCamelCase	:					List[str]       	=   self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(A_   )
       UpperCamelCase	:					List[Any]       	=   MBartTokenizer.from_pretrained(A_   )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,							A_   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.tokenizer(self.src_text       ,							text_target=self.tgt_text       ,							padding=A_       ,							return_tensors="pt"   )
       UpperCamelCase	:					int       	=   shift_tokens_right(batch["labels"]       ,							self.tokenizer.pad_token_id   )

       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
       assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
       assert batch.decoder_input_ids[1][-1] == 2
       assert batch.labels[1][-2:].tolist() == [2, RO_CODE]





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   self.tokenizer(
           self.src_text       ,							text_target=self.tgt_text       ,							padding=A_       ,							truncation=A_       ,							max_length=len(self.expected_src_tokens   )       ,							return_tensors="pt"       ,							)

       UpperCamelCase	:					List[str]       	=   shift_tokens_right(batch["labels"]       ,							self.tokenizer.pad_token_id   )

       self.assertIsInstance(A_       ,							A_   )

       self.assertEqual((2, 14)       ,							batch.input_ids.shape   )
       self.assertEqual((2, 14)       ,							batch.attention_mask.shape   )
       UpperCamelCase	:					Union[str, Any]       	=   batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens       ,							A_   )
       self.assertEqual(2       ,							batch.decoder_input_ids[0, -1]   )  # EOS
       # Test that special tokens are reset
       self.assertEqual(self.tokenizer.prefix_tokens       ,							[]   )
       self.assertEqual(self.tokenizer.suffix_tokens       ,							[self.tokenizer.eos_token_id, EN_CODE]   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.tokenizer(self.src_text       ,							padding=A_       ,							truncation=A_       ,							max_length=3       ,							return_tensors="pt"   )
       UpperCamelCase	:					str       	=   self.tokenizer(
           text_target=self.tgt_text       ,							padding=A_       ,							truncation=A_       ,							max_length=10       ,							return_tensors="pt"   )
       UpperCamelCase	:					Optional[int]       	=   targets["input_ids"]
       UpperCamelCase	:					Any       	=   shift_tokens_right(A_       ,							self.tokenizer.pad_token_id   )

       self.assertEqual(batch.input_ids.shape[1]       ,							3   )
       self.assertEqual(batch.decoder_input_ids.shape[1]       ,							10   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.tokenizer._build_translation_inputs(
           "A test"       ,							return_tensors="pt"       ,							src_lang="en_XX"       ,							tgt_lang="ar_AR"   )

       self.assertEqual(
           nested_simplify(A_   )       ,							{
               # A, test, EOS, en_XX
               "input_ids": [[62, 3034, 2, 25_0004]],
               "attention_mask": [[1, 1, 1, 1]],
               # ar_AR
               "forced_bos_token_id": 25_0001,
           }       ,							)







import pickle

import numpy as np
from matplotlib import pyplot as plt



class 					A__   :





      def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_=0.2       ,							A_=0.2   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   {
           "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(A_       ,							"wb"   ) as f:
        pickle.dump(A_       ,							A_   )

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





      @classmethod
      def        __UpperCamelCase( cls       ,							A_   ):
       '''simple docstring'''




       with open(A_       ,							"rb"   ) as f:
        UpperCamelCase	:					Optional[Any]       	=   pickle.load(A_   )  # noqa: S301

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return round(A_       ,							3   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




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

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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_="average_pool"   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   np.asarray(A_   )
       UpperCamelCase	:					List[Any]       	=   np.shape(A_   )
       UpperCamelCase	:					Any       	=   data_mat.reshape(1       ,							shapes[0] * shapes[1]   )
       return data_expanded





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_=bool   ):
       '''simple docstring'''




       print("----------------------Start Training-------------------------"   )
       print((" - - Shape: Train_Data  ", np.shape(A_   ))   )
       print((" - - Shape: Teach_Data  ", np.shape(A_   ))   )
       UpperCamelCase	:					List[str]       	=   0
       UpperCamelCase	:					Union[str, Any]       	=   []
       UpperCamelCase	:					int       	=   1_0000
       while rp < n_repeat and mse >= error_accuracy:
        UpperCamelCase	:					Tuple       	=   0
        print(F"""-------------Learning Time {rp}--------------"""   )
        for p in range(len(A_   )   ):
         # print('------------Learning Image: %d--------------'%p)
         UpperCamelCase	:					Any       	=   np.asmatrix(datas_train[p]   )
         UpperCamelCase	:					List[str]       	=   np.asarray(datas_teach[p]   )
         UpperCamelCase	,    UpperCamelCase	:					Dict       	=   self.convolute(
             A_       ,							self.conva       ,							self.w_conva       ,							self.thre_conva       ,							conv_step=self.step_conva       ,							)
         UpperCamelCase	:					Tuple       	=   self.pooling(A_       ,							self.size_poolinga   )
         UpperCamelCase	:					int       	=   np.shape(A_   )
         UpperCamelCase	:					List[str]       	=   self._expand(A_   )
         UpperCamelCase	:					Optional[int]       	=   data_bp_input

         UpperCamelCase	:					str       	=   np.dot(A_       ,							self.vji.T   ) - self.thre_bpa
         UpperCamelCase	:					Optional[int]       	=   self.sig(A_   )
         UpperCamelCase	:					List[Any]       	=   np.dot(A_       ,							self.wkj.T   ) - self.thre_bpa
         UpperCamelCase	:					Dict       	=   self.sig(A_   )

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

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

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

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

       def draw_error():
        UpperCamelCase	:					Tuple       	=   [error_accuracy for i in range(int(n_repeat * 1.2   )   )]
        plt.plot(A_       ,							"+-"   )
        plt.plot(A_       ,							"r--"   )
        plt.xlabel("Learning Times"   )
        plt.ylabel("All_mse"   )
        plt.grid(A_       ,							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       ,							A_   ):
       '''simple docstring'''




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

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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

       return data_conveda, data_pooleda


if __name__ == "__main__":
    pass
