





"""simple docstring"""




import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    lowercase				:	Optional[int]      						=  """platform"""
    import jax
    import jax.numpy as jnp
    import numpy as np

    from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel





@require_flax
class 			A__   :




   """simple docstring"""



   __A       :    List[Any]	  =  PegasusConfig
   __A       :    Any	  =  {}
   __A       :    Optional[Any]	  =  "gelu"

   def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=False ,  lowercase=99 ,  lowercase=32 ,  lowercase=5 ,  lowercase=4 ,  lowercase=37 ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=20 ,  lowercase=2 ,  lowercase=1 ,  lowercase=0 ,  )      ->						Optional[int]:







          '''simple docstring'''

          a__						:     List[str]			=    parent
          a__						:     Tuple			=    batch_size
          a__						:     Optional[int]			=    seq_length
          a__						:     Dict			=    is_training
          a__						:     Dict			=    use_labels
          a__						:     Optional[Any]			=    vocab_size
          a__						:     Optional[Any]			=    hidden_size
          a__						:     List[str]			=    num_hidden_layers
          a__						:     Any			=    num_attention_heads
          a__						:     List[str]			=    intermediate_size

          a__						:     Optional[Any]			=    hidden_dropout_prob
          a__						:     Any			=    attention_probs_dropout_prob
          a__						:     Optional[int]			=    max_position_embeddings
          a__						:     Optional[Any]			=    eos_token_id
          a__						:     str			=    pad_token_id
          a__						:     Optional[int]			=    bos_token_id

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          a__						:     List[str]			=    ids_tensor([self.batch_size, self.seq_length - 1] ,  self.vocab_size).clip(3 ,  self.vocab_size)
          a__						:     Optional[Any]			=    np.expand_dims(np.array([self.eos_token_id] * self.batch_size) ,  1)
          a__						:     List[str]			=    np.concatenate([input_ids, eos_tensor] ,  axis=1)

          a__						:     List[str]			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

          a__						:     List[str]			=    self.config_cls(
              vocab_size=self.vocab_size ,  d_model=self.hidden_size ,  encoder_layers=self.num_hidden_layers ,  decoder_layers=self.num_hidden_layers ,  encoder_attention_heads=self.num_attention_heads ,  decoder_attention_heads=self.num_attention_heads ,  encoder_ffn_dim=self.intermediate_size ,  decoder_ffn_dim=self.intermediate_size ,  dropout=self.hidden_dropout_prob ,  attention_dropout=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  eos_token_ids=[2] ,  bos_token_id=self.bos_token_id ,  pad_token_id=self.pad_token_id ,  decoder_start_token_id=self.pad_token_id ,  **self.config_updates ,  )
          a__						:     List[str]			=    prepare_pegasus_inputs_dict(lowercase ,  lowercase ,  lowercase)
          return config, inputs_dict

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						Dict:







          '''simple docstring'''

          a__						:     Any			=    20
          a__						:     int			=    model_class_name(lowercase)

          a__						:     List[Any]			=    model.encode(inputs_dict['input_ids'])

          a__			,							a__						:     Optional[Any]			=    (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          a__						:     Optional[int]			=    model.init_cache(decoder_input_ids.shape[0] ,  lowercase ,  lowercase)
          a__						:     List[Any]			=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,  dtype='i4')

          a__						:     int			=    jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )
          a__						:     Any			=    model.decode(
              decoder_input_ids[:, :-1] ,  lowercase ,  decoder_attention_mask=lowercase ,  past_key_values=lowercase ,  decoder_position_ids=lowercase ,  )

          a__						:     Union[str, Any]			=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='i4')
          a__						:     Dict			=    model.decode(
              decoder_input_ids[:, -1:] ,  lowercase ,  decoder_attention_mask=lowercase ,  past_key_values=outputs_cache.past_key_values ,  decoder_position_ids=lowercase ,  )

          a__						:     str			=    model.decode(lowercase ,  lowercase)

          a__						:     int			=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
          self.parent.assertTrue(diff < 1e-3 ,  msg=F'Max diff is {diff}')

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Optional[int]			=    20
          a__						:     Any			=    model_class_name(lowercase)

          a__						:     Optional[int]			=    model.encode(inputs_dict['input_ids'])

          a__			,							a__						:     Tuple			=    (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          a__						:     Optional[int]			=    jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
              ] ,  axis=-1 ,  )

          a__						:     Tuple			=    model.init_cache(decoder_input_ids.shape[0] ,  lowercase ,  lowercase)
          a__						:     Any			=    jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )

          a__						:     int			=    model.decode(
              decoder_input_ids[:, :-1] ,  lowercase ,  decoder_attention_mask=lowercase ,  past_key_values=lowercase ,  decoder_position_ids=lowercase ,  )
          a__						:     List[Any]			=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='i4')
          a__						:     Tuple			=    model.decode(
              decoder_input_ids[:, -1:] ,  lowercase ,  past_key_values=outputs_cache.past_key_values ,  decoder_attention_mask=lowercase ,  decoder_position_ids=lowercase ,  )

          a__						:     List[Any]			=    model.decode(lowercase ,  lowercase ,  decoder_attention_mask=lowercase)

          a__						:     Optional[int]			=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
          self.parent.assertTrue(diff < 1e-3 ,  msg=F'Max diff is {diff}')



def A_     (		A__		,				A__		,				A__		,				A__=None		,				A__=None		,				)  ->		List[str]:






       if attention_mask is None:
              a__						:     Union[str, Any]			=    np.not_equal(A__		,				config.pad_token_id    ).astype(np.inta    )
       if decoder_attention_mask is None:
              a__						:     Tuple			=    np.concatenate(
                  [
                      np.ones(decoder_input_ids[:, :1].shape		,				dtype=np.inta    ),
                      np.not_equal(decoder_input_ids[:, 1:]		,				config.pad_token_id    ).astype(np.inta    ),
                  ]		,				axis=-1		,				)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }





@require_flax
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    int	  =  (
       (
           FlaxPegasusForConditionalGeneration,
           FlaxPegasusModel,
       )
       if is_flax_available()
       else ()
   )
   __A       :    Any	  =  (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
   __A       :    List[str]	  =  True
   __A       :    Union[str, Any]	  =  False
   __A       :    str	  =  False
   __A       :    List[str]	  =  False

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    FlaxPegasusModelTester(self)
          a__						:     List[str]			=    ConfigTester(self ,  config_class=lowercase)

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__			,							a__						:     Optional[Any]			=    self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(lowercase ,  lowercase ,  lowercase)

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__			,							a__						:     Dict			=    self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(lowercase ,  lowercase ,  lowercase)

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__			,							a__						:     List[str]			=    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__):
                        a__						:     List[Any]			=    self._prepare_for_class(lowercase ,  lowercase)
                        a__						:     Optional[Any]			=    model_class(lowercase)

                        @jax.jit
                        def encode_jitted(lowercase ,  lowercase=None ,  **lowercase):
                               return model.encode(input_ids=lowercase ,  attention_mask=lowercase)

                        with self.subTest('JIT Enabled'):
                               a__						:     Any			=    encode_jitted(**lowercase).to_tuple()

                        with self.subTest('JIT Disabled'):
                               with jax.disable_jit():
                                      a__						:     int			=    encode_jitted(**lowercase).to_tuple()

                        self.assertEqual(len(lowercase) ,  len(lowercase))
                        for jitted_output, output in zip(lowercase ,  lowercase):
                               self.assertEqual(jitted_output.shape ,  output.shape)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__			,							a__						:     Dict			=    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__):
                        a__						:     str			=    model_class(lowercase)
                        a__						:     int			=    model.encode(inputs_dict['input_ids'] ,  inputs_dict['attention_mask'])

                        a__						:     Union[str, Any]			=    {
                            'decoder_input_ids': inputs_dict['decoder_input_ids'],
                            'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                            'encoder_outputs': encoder_outputs,
                        }

                        @jax.jit
                        def decode_jitted(lowercase ,  lowercase ,  lowercase):
                               return model.decode(
                                   decoder_input_ids=lowercase ,  decoder_attention_mask=lowercase ,  encoder_outputs=lowercase ,  )

                        with self.subTest('JIT Enabled'):
                               a__						:     Any			=    decode_jitted(**lowercase).to_tuple()

                        with self.subTest('JIT Disabled'):
                               with jax.disable_jit():
                                      a__						:     Union[str, Any]			=    decode_jitted(**lowercase).to_tuple()

                        self.assertEqual(len(lowercase) ,  len(lowercase))
                        for jitted_output, output in zip(lowercase ,  lowercase):
                               self.assertEqual(jitted_output.shape ,  output.shape)

   @slow
   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          for model_class_name in self.all_model_classes:
                 a__						:     List[str]			=    model_class_name.from_pretrained('google/pegasus-large' ,  from_pt=lowercase)
                 a__						:     List[Any]			=    np.ones((1, 1))
                 a__						:     List[str]			=    model(lowercase)
                 self.assertIsNotNone(lowercase)

   @slow
   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     List[Any]			=    FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum')
          a__						:     int			=    PegasusTokenizer.from_pretrained('google/pegasus-xsum')

          a__						:     List[str]			=    [
              ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
              ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
          ]

          a__						:     Tuple			=    [
              'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
              'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
          ]

          a__						:     int			=    tokenizer(lowercase ,  return_tensors='np' ,  truncation=lowercase ,  max_length=512 ,  padding=lowercase)
          a__						:     List[str]			=    model.generate(**lowercase ,  num_beams=2).sequences
          a__						:     Union[str, Any]			=    tokenizer.batch_decode(lowercase ,  skip_special_tokens=lowercase)
          assert tgt_text == decoded









import pickle

import numpy as np
from matplotlib import pyplot as plt





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase=0.2 ,  lowercase=0.2)      ->						Any:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    {
              '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 							__lowercase		( cls ,  lowercase)      ->						Any:







          '''simple docstring'''

          with open(lowercase ,  'rb') as f:
                 a__						:     Any			=    pickle.load(lowercase)  # noqa: S301

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

   def 							__lowercase		( self ,  lowercase)      ->						Any:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          return round(lowercase ,  3)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

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

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

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase="average_pool")      ->						str:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						Dict:







          '''simple docstring'''

          a__						:     Dict			=    np.asarray(lowercase)
          a__						:     Optional[int]			=    np.shape(lowercase)
          a__						:     Any			=    data_mat.reshape(1 ,  shapes[0] * shapes[1])
          return data_expanded

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase=bool)      ->						str:







          '''simple docstring'''

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

                        a__						:     Tuple			=    np.dot(lowercase ,  self.vji.T) - self.thre_bpa
                        a__						:     Any			=    self.sig(lowercase)
                        a__						:     Any			=    np.dot(lowercase ,  self.wkj.T) - self.thre_bpa
                        a__						:     Any			=    self.sig(lowercase)

                        # --------------Model Leaning ------------------------
                        # calculate error and gradient---------------
                        a__						:     Any			=    np.multiply(
                            (data_teach - bp_outa) ,  np.multiply(lowercase ,  (1 - bp_outa)))
                        a__						:     Optional[Any]			=    np.multiply(
                            np.dot(lowercase ,  self.wkj) ,  np.multiply(lowercase ,  (1 - bp_outa)))
                        a__						:     Tuple			=    np.dot(lowercase ,  self.vji)

                        a__						:     Union[str, Any]			=    pd_i_all / (self.size_poolinga * self.size_poolinga)
                        a__						:     List[str]			=    pd_conva_pooled.T.getA().tolist()
                        a__						:     str			=    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__						:     Optional[int]			=    self._expand_mat(pd_conva_all[k_conv])
                               a__						:     int			=    self.rate_weight * np.dot(lowercase ,  lowercase)

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

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

          def draw_error():
                 a__						:     int			=    [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 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

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

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

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     str			=    np.asmatrix(lowercase)
          a__			,							a__						:     str			=    self.convolute(
              lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
          a__						:     List[str]			=    self.pooling(lowercase ,  self.size_poolinga)

          return data_conveda, data_pooleda


if __name__ == "__main__":
    pass


