
'''simple docstring'''





import numpy as np
from matplotlib import pyplot as plt





def 							snake_case				(		UpperCAmelCase						)->      Dict:







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


class 			UpperCamelCase__	:






 def __init__(      self	:Tuple						,					_A	:Optional[Any]						,					_A	:str=None						,					_A	:Tuple=None						,					_A	:List[Any]=False      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      units
       __A													=      None
       __A													=      None
       __A													=      activation
       if learning_rate is None:
             __A													=      0.3
       __A													=      learning_rate
       __A													=      is_input_layer






 def 						lowercase_    (      self	:Dict						,					_A	:Any      )					->      Tuple:







       '''simple docstring'''



       __A													=      np.asmatrix(np.random.normal(0						,					0.5						,					(self.units, back_units)      )      )
       __A													=      np.asmatrix(np.random.normal(0						,					0.5						,					self.units      )      ).T
       if self.activation is None:
             __A													=      sigmoid






 def 						lowercase_    (      self	:List[Any]      )					->      Tuple:







       '''simple docstring'''



       if self.activation == sigmoid:
             __A													=      np.dot(self.output						,					(1 - self.output).T      )
             __A													=      np.diag(np.diag(_A      )      )
       else:
             __A													=      1
       return gradient_activation






 def 						lowercase_    (      self	:int						,					_A	:Union[str, Any]      )					->      str:







       '''simple docstring'''



       __A													=      xdata
       if self.is_input_layer:
             # input layer
             __A													=      xdata
             __A													=      xdata
             return xdata
       else:
             __A													=      np.dot(self.weight						,					self.xdata      ) - self.bias
             __A													=      self.activation(self.wx_plus_b      )
             return self.output






 def 						lowercase_    (      self	:Tuple						,					_A	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.cal_gradient()  # i * i 维
       __A													=      np.asmatrix(np.dot(gradient.T						,					_A      )      )

       __A													=      np.asmatrix(self.xdata      )
       __A													=      -1
       __A													=      self.weight

       __A													=      np.dot(gradient.T						,					self._gradient_weight.T      )
       __A													=      gradient * self._gradient_bias
       __A													=      np.dot(_A						,					self._gradient_x      ).T
       # upgrade: the Negative gradient direction
       __A													=      self.weight - self.learn_rate * self.gradient_weight
       __A													=      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 			UpperCamelCase__	:






 def __init__(      self	:int      )					->      List[str]:







       '''simple docstring'''



       __A													=      []
       __A													=      []
       __A													=      plt.figure()
       __A													=      self.fig_loss.add_subplot(1						,					1						,					1      )






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:Tuple      )					->      Union[str, Any]:







       '''simple docstring'''



       self.layers.append(_A      )






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







       '''simple docstring'''



       for i, layer in enumerate(self.layers[:]      ):
             if i < 1:
                   __A													=      True
             else:
                   layer.initializer(self.layers[i - 1].units      )






 def 						lowercase_    (      self	:Tuple      )					->      List[str]:







       '''simple docstring'''



       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 						lowercase_    (      self	:Tuple						,					_A	:List[str]						,					_A	:Optional[int]						,					_A	:Union[str, Any]						,					_A	:str      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      train_round
       __A													=      accuracy

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

       __A													=      np.shape(_A      )
       for _ in range(_A      ):
             __A													=      0
             for row in range(x_shape[0]      ):
                   __A													=      np.asmatrix(xdata[row, :]      ).T
                   __A													=      np.asmatrix(ydata[row, :]      ).T

                   # forward propagation
                   for layer in self.layers:
                         __A													=      layer.forward_propagation(_xdata      )

                   __A						,   __A													=      self.cal_loss(_ydata						,					_xdata      )
                   __A													=      all_loss + loss

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

             __A													=      all_loss / x_shape[0]
             self.train_mse.append(_A      )

             self.plot_loss()

             if mse < self.accuracy:
                   print('----达到精度----'      )
                   return mse
       return None






 def 						lowercase_    (      self	:Dict						,					_A	:Tuple						,					_A	:str      )					->      Tuple:







       '''simple docstring'''



       __A													=      np.sum(np.power((ydata - ydata_)						,					2      )      )
       __A													=      2 * (ydata_ - ydata)
       # vector (shape is the same as _ydata.shape)
       return self.loss, self.loss_gradient






 def 						lowercase_    (      self	:Tuple      )					->      Dict:







       '''simple docstring'''



       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 							snake_case				(		)->      List[str]:







      """simple docstring"""
      __A													=      np.random.randn(1_0   ,	1_0						)
      __A													=      np.asarray(
          [
              [0.8, 0.4],
              [0.4, 0.3],
              [0.34, 0.45],
              [0.67, 0.32],
              [0.88, 0.67],
              [0.78, 0.77],
              [0.55, 0.66],
              [0.55, 0.43],
              [0.54, 0.1],
              [0.1, 0.5],
          ]						)
      __A													=      BPNN()
      for i in (1_0, 2_0, 3_0, 2):
            model.add_layer(DenseLayer(UpperCAmelCase						)						)
      model.build()
      model.summary()
      model.train(xdata=UpperCAmelCase   ,	ydata=UpperCAmelCase   ,	train_round=1_0_0   ,	accuracy=0.01						)


if __name__ == "__main__":
 example()













'''simple docstring'''


from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel



@require_tf
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     MarianConfig
		__lowerCAmelCase								=     {}
		__lowerCAmelCase								=     '''gelu'''







		def __init__(self    :   Tuple   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict=13   ,   _lowerCAmelCase    :   Optional[int]=7   ,   _lowerCAmelCase    :   str=True   ,   _lowerCAmelCase    :   Optional[int]=False   ,   _lowerCAmelCase    :   Tuple=99   ,   _lowerCAmelCase    :   str=32   ,   _lowerCAmelCase    :   str=2   ,   _lowerCAmelCase    :   Union[str, Any]=4   ,   _lowerCAmelCase    :   Dict=37   ,   _lowerCAmelCase    :   int=0.1   ,   _lowerCAmelCase    :   List[Any]=0.1   ,   _lowerCAmelCase    :   Tuple=20   ,   _lowerCAmelCase    :   List[str]=2   ,   _lowerCAmelCase    :   Any=1   ,   _lowerCAmelCase    :   int=0   ,   ):
			A					=   parent
			A					=   batch_size
			A					=   seq_length
			A					=   is_training
			A					=   use_labels
			A					=   vocab_size
			A					=   hidden_size
			A					=   num_hidden_layers
			A					=   num_attention_heads
			A					=   intermediate_size

			A					=   hidden_dropout_prob
			A					=   attention_probs_dropout_prob
			A					=   max_position_embeddings
			A					=   eos_token_id
			A					=   pad_token_id
			A					=   bos_token_id







		def 							A  (self    :   int   ):
			A					=   ids_tensor([self.batch_size, self.seq_length - 1]   ,   self.vocab_size   )
			A					=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )   ,   1   )
			A					=   tf.concat([input_ids, eos_tensor]   ,   axis=1   )

			A					=   ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size   )

			A					=   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					=   prepare_marian_inputs_dict(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
			return config, inputs_dict







		def 							A  (self    :   List[Any]   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   Any   ):
			A					=   TFMarianModel(config=_lowerCAmelCase   ).get_decoder()
			A					=   inputs_dict["""input_ids"""]

			A					=   input_ids[:1, :]
			A					=   inputs_dict["""attention_mask"""][:1, :]
			A					=   inputs_dict["""head_mask"""]
			A					=   1

			# first forward pass
			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   ,   head_mask=_lowerCAmelCase   ,   use_cache=_lowerCAmelCase   )

			A	,     A					=   outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			A					=   ids_tensor((self.batch_size, 3)   ,   config.vocab_size   )
			A					=   tf.cast(ids_tensor((self.batch_size, 3)   ,   2   )   ,   tf.inta   )

			# append to next input_ids and
			A					=   tf.concat([input_ids, next_tokens]   ,   axis=-1   )
			A					=   tf.concat([attention_mask, next_attn_mask]   ,   axis=-1   )

			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   )[0]
			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   ,   past_key_values=_lowerCAmelCase   )[0]

			self.parent.assertEqual(next_tokens.shape[1]   ,   output_from_past.shape[1]   )

			# select random slice
			A					=   int(ids_tensor((1,)   ,   output_from_past.shape[-1]   )   )
			A					=   output_from_no_past[:, -3:, random_slice_idx]
			A					=   output_from_past[:, :, random_slice_idx]

			# test that outputs are equal for slice
			tf.debugging.assert_near(_lowerCAmelCase   ,   _lowerCAmelCase   ,   rtol=1e-3   )






def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      )   ->List[str]:



	"""simple docstring"""







	if attention_mask is None:
		A					=   tf.cast(tf.math.not_equal(UpperCAmelCase							,      config.pad_token_id     )							,      tf.inta     )
	if decoder_attention_mask is None:
		A					=   tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape							,      dtype=tf.inta     ),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,      config.pad_token_id     )							,      tf.inta     ),
		    ]							,      axis=-1							,      )
	if head_mask is None:
		A					=   tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
	if decoder_head_mask is None:
		A					=   tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
	if cross_attn_head_mask is None:
		A					=   tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}



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







		'''simple docstring'''







		__lowerCAmelCase								=     (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
		__lowerCAmelCase								=     (TFMarianMTModel,) if is_tf_available() else ()
		__lowerCAmelCase								=     (
		    {
		        '''conversational''': TFMarianMTModel,
		        '''feature-extraction''': TFMarianModel,
		        '''summarization''': TFMarianMTModel,
		        '''text2text-generation''': TFMarianMTModel,
		        '''translation''': TFMarianMTModel,
		    }
		    if is_tf_available()
		    else {}
		)
		__lowerCAmelCase								=     True
		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     False







		def 							A  (self    :   Tuple   ):
			A					=   TFMarianModelTester(self   )
			A					=   ConfigTester(self   ,   config_class=_lowerCAmelCase   )







		def 							A  (self    :   List[str]   ):
			self.config_tester.run_common_tests()







		def 							A  (self    :   Any   ):
			A					=   self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase   )






@require_tf
class 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     10_00  # show more chars for failing integration tests







		@classmethod
		def 							A  (cls    :   Union[str, Any]   ):
			A					=   F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
			return cls







		@cached_property
		def 							A  (self    :   str   ):
			return AutoTokenizer.from_pretrained(self.model_name   )







		@property
		def 							A  (self    :   Optional[int]   ):
			return self.tokenizer.eos_token_id







		@cached_property
		def 							A  (self    :   Optional[Any]   ):
			warnings.simplefilter("""error"""   )
			A					=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
			assert isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   model.config
			self.assertListEqual(c.bad_words_ids   ,   [[c.pad_token_id]]   )
			self.assertEqual(c.max_length   ,   512   )
			self.assertEqual(c.decoder_start_token_id   ,   c.pad_token_id   )
			return model







		def 							A  (self    :   Optional[int]   ,   **_lowerCAmelCase    :   Any   ):
			A					=   self.translate_src_text(**_lowerCAmelCase   )
			self.assertListEqual(self.expected_text   ,   _lowerCAmelCase   )







		def 							A  (self    :   Union[str, Any]   ,   **_lowerCAmelCase    :   Union[str, Any]   ):
			A					=   self.tokenizer(self.src_text   ,   **_lowerCAmelCase   ,   padding=_lowerCAmelCase   ,   return_tensors="""tf"""   )
			A					=   self.model.generate(
			    model_inputs.input_ids   ,   attention_mask=model_inputs.attention_mask   ,   num_beams=2   ,   max_length=128   )
			A					=   self.tokenizer.batch_decode(generated_ids.numpy()   ,   skip_special_tokens=_lowerCAmelCase   )
			return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''mt'''
		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
		__lowerCAmelCase								=     ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   str   ):
			self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     '''zh'''
		__lowerCAmelCase								=     ['''My name is Wolfgang and I live in Berlin''']
		__lowerCAmelCase								=     ['''我叫沃尔夫冈 我住在柏林''']







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Any   ):
			self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     '''ROMANCE'''
		__lowerCAmelCase								=     [
		    '''>>fr<< Don\'t spend so much time watching TV.''',
		    '''>>pt<< Your message has been sent.''',
		    '''>>es<< He\'s two years older than me.''',
		]
		__lowerCAmelCase								=     [
		    '''Ne passez pas autant de temps à regarder la télé.''',
		    '''A sua mensagem foi enviada.''',
		    '''Es dos años más viejo que yo.''',
		]







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Union[str, Any]   ):
			self._assert_generated_batch_equal_expected()







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Union[str, Any]   ):
			A					=   TranslationPipeline(self.model   ,   self.tokenizer   ,   framework="""tf"""   )
			A					=   pipeline(self.src_text   )
			self.assertEqual(self.expected_text   ,   [x["""translation_text"""] for x in output]   )

