
import numpy as np
from matplotlib import pyplot as plt





def    _a (						lowerCamelCase							):
 return 1 / (1 + np.exp(-1 * x							))





class 							A__		:
      def __init__(				self   ,  __magic_name__   ,  __magic_name__=None   ,  __magic_name__=None   ,  __magic_name__=False				):
       lowerCamelCase		:     int          =				units
       lowerCamelCase		:     Union[str, Any]          =				None
       lowerCamelCase		:     Dict          =				None
       lowerCamelCase		:     int          =				activation
       if learning_rate is None:
        lowerCamelCase		:     int          =				0.3
       lowerCamelCase		:     str          =				learning_rate
       lowerCamelCase		:     Dict          =				is_input_layer
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     str          =				np.asmatrix(np.random.normal(0   ,  0.5   ,  (self.units, back_units)				)				)
       lowerCamelCase		:     str          =				np.asmatrix(np.random.normal(0   ,  0.5   ,  self.units				)				).T
       if self.activation is None:
        lowerCamelCase		:     str          =				sigmoid
      def 						UpperCamelCase__							(				self				):
       # activation function may be sigmoid or linear
       if self.activation == sigmoid:
        lowerCamelCase		:     str          =				np.dot(self.output   ,  (1 - self.output).T				)
        lowerCamelCase		:     List[Any]          =				np.diag(np.diag(__magic_name__				)				)
       else:
        lowerCamelCase		:     Union[str, Any]          =				1
       return gradient_activation
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     List[str]          =				xdata
       if self.is_input_layer:
        # input layer
        lowerCamelCase		:     Dict          =				xdata
        lowerCamelCase		:     Tuple          =				xdata
        return xdata
       else:
        lowerCamelCase		:     Dict          =				np.dot(self.weight   ,  self.xdata				) - self.bias
        lowerCamelCase		:     Dict          =				self.activation(self.wx_plus_b				)
        return self.output






      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     str          =				self.cal_gradient()  # i * i 维
       lowerCamelCase		:     List[str]          =				np.asmatrix(np.dot(gradient.T   ,  __magic_name__				)				)

       lowerCamelCase		:     List[str]          =				np.asmatrix(self.xdata				)
       lowerCamelCase		:     Union[str, Any]          =				-1
       lowerCamelCase		:     List[Any]          =				self.weight

       lowerCamelCase		:     Union[str, Any]          =				np.dot(gradient.T   ,  self._gradient_weight.T				)
       lowerCamelCase		:     Union[str, Any]          =				gradient * self._gradient_bias
       lowerCamelCase		:     List[str]          =				np.dot(__magic_name__   ,  self._gradient_x				).T
       # upgrade: the Negative gradient direction
       lowerCamelCase		:     Any          =				self.weight - self.learn_rate * self.gradient_weight
       lowerCamelCase		:     Optional[int]          =				self.bias - self.learn_rate * self.gradient_bias.T
       # updates the weights and bias according to learning rate (0.3 if undefined)
       return self.gradient





class 							A__		:
      def __init__(				self				):
       lowerCamelCase		:     Dict          =				[]
       lowerCamelCase		:     List[Any]          =				[]
       lowerCamelCase		:     Tuple          =				plt.figure()
       lowerCamelCase		:     Optional[Any]          =				self.fig_loss.add_subplot(1   ,  1   ,  1				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       self.layers.append(__magic_name__				)
      def 						UpperCamelCase__							(				self				):
       for i, layer in enumerate(self.layers[:]				):
        if i < 1:
         lowerCamelCase		:     Dict          =				True
        else:
         layer.initializer(self.layers[i - 1].units				)
      def 						UpperCamelCase__							(				self				):
       for i, layer in enumerate(self.layers[:]				):
        print(F'''------- layer {i} -------'''				)
        print("""weight.shape """   ,  np.shape(layer.weight				)				)
        print("""bias.shape """   ,  np.shape(layer.bias				)				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Any          =				train_round
       lowerCamelCase		:     List[str]          =				accuracy

       self.ax_loss.hlines(self.accuracy   ,  0   ,  self.train_round * 1.1				)

       lowerCamelCase		:     Union[str, Any]          =				np.shape(__magic_name__				)
       for _ in range(__magic_name__				):
        lowerCamelCase		:     int          =				0
        for row in range(x_shape[0]				):
         lowerCamelCase		:     Any          =				np.asmatrix(xdata[row, :]				).T
         lowerCamelCase		:     Optional[Any]          =				np.asmatrix(ydata[row, :]				).T

         # forward propagation
         for layer in self.layers:
          lowerCamelCase		:     Optional[Any]          =				layer.forward_propagation(_xdata				)

         lowerCamelCase      ,   lowerCamelCase		:     Any          =				self.cal_loss(_ydata   ,  _xdata				)
         lowerCamelCase		:     Optional[Any]          =				all_loss + loss

         # back propagation: the input_layer does not upgrade
         for layer in self.layers[:0:-1]:
          lowerCamelCase		:     str          =				layer.back_propagation(__magic_name__				)

        lowerCamelCase		:     Dict          =				all_loss / x_shape[0]
        self.train_mse.append(__magic_name__				)

        self.plot_loss()

        if mse < self.accuracy:
         print("""----达到精度----"""				)
         return mse
       return None
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				np.sum(np.power((ydata - ydata_)   ,  2				)				)
       lowerCamelCase		:     str          =				2 * (ydata_ - ydata)
       # vector (shape is the same as _ydata.shape)
       return self.loss, self.loss_gradient






      def 						UpperCamelCase__							(				self				):
       if self.ax_loss.lines:
        self.ax_loss.lines.remove(self.ax_loss.lines[0]				)
       self.ax_loss.plot(self.train_mse   ,  """r-"""				)
       plt.ion()
       plt.xlabel("""step"""				)
       plt.ylabel("""loss"""				)
       plt.show()
       plt.pause(0.1				)





def    _a (						):
 lowerCamelCase		:     List[str]          =				np.random.randn(10,     10							)
 lowerCamelCase		:     List[str]          =				np.asarray(
     [
         [0.8, 0.4],
         [0.4, 0.3],
         [0.3_4, 0.4_5],
         [0.6_7, 0.3_2],
         [0.8_8, 0.6_7],
         [0.7_8, 0.7_7],
         [0.5_5, 0.6_6],
         [0.5_5, 0.4_3],
         [0.5_4, 0.1],
         [0.1, 0.5],
     ]							)
 lowerCamelCase		:     Any          =				BPNN()
 for i in (10, 20, 30, 2):
  model.add_layer(DenseLayer(lowerCamelCase							)							)
 model.build()
 model.summary()
 model.train(xdata=lowerCamelCase,     ydata=lowerCamelCase,     train_round=100,     accuracy=0.0_1							)


if __name__ == "__main__":
      example()






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   Tuple   	=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _UpperCAmelCase    :   Optional[Any]   	=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if isinstance(model.config   ,  __magic_name__				):
        lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				list(tokenizer.lang_code_to_id.keys()				)[:2]
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang=__magic_name__   ,  tgt_lang=__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__				)
       return translator, ["Some string", "Some other text"]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				translator("""Some string"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Dict          =				translator(["""Some string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Any          =				translator(["""Some string""", """other string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}, {"""translation_text""": ANY(__magic_name__				)}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Tuple          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     List[Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )






      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )





class 							A__		(     unittest.TestCase):
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       # We don't provide a default for this pair
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation_cn_to_ar"""				)

       # but we do for this one
       lowerCamelCase		:     Optional[Any]          =				pipeline(task="""translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
       lowerCamelCase		:     List[Any]          =				MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

       lowerCamelCase		:     Any          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(__magic_name__				):
        translator("""This is a test"""				)

       lowerCamelCase		:     Optional[int]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)

       lowerCamelCase		:     Optional[Any]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""hi_IN"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """यह एक परीक्षण है"""}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       lowerCamelCase		:     str          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				"""patrickvonplaten/t5-tiny-random"""
       lowerCamelCase		:     Tuple          =				pipeline(task="""translation_cn_to_ar"""   ,  model=__magic_name__				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """cn"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """ar"""				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				"""patrickvonplaten/t5-tiny-random"""
       with pytest.warns(__magic_name__   ,  match=r""".*translation_en_to_de.*"""				):
        lowerCamelCase		:     Optional[int]          =				pipeline(task="""translation"""   ,  model=__magic_name__				)
       self.assertEqual(translator.task   ,  """translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)






      @require_torch
      def 						UpperCamelCase__							(				self				):
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation"""				)




