
'''simple docstring'''




import math
import random







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__ = False						):




    '''simple docstring'''



    if deriv:
        return value * (1 - value)
    return 1 / (1 + math.exp(-value						))


# Initial Value
lowercase			:				Dict    		=			0.02







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Optional[Any]					=						float(2 * (random.randint(1		,				100						)) - 1						)

    for _ in range(snake_case__						):
        # Forward propagation
        A       :   int					=						sigmoid_function(INITIAL_VALUE * weight						)
        # How much did we miss?
        A       :   Union[str, Any]					=						(expected / 100) - layer_a
        # Error delta
        A       :   Tuple					=						layer_1_error * sigmoid_function(snake_case__		,				snake_case__						)
        # Update weight
        weight += INITIAL_VALUE * layer_1_delta

    return layer_a * 100


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowercase			:				Optional[Any]    		=			int(input('Expected value: '))
      lowercase			:				int    		=			int(input('Number of propagations: '))
      print(forward_propagation(expected, number_propagations))











import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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
       snake_case__					:							Tuple													=     'platform'

       import jax
       import jax.numpy as jnp

       from transformers import BlenderbotTokenizer
       from transformers.models.blenderbot.modeling_flax_blenderbot import (
           FlaxBlenderbotForConditionalGeneration,
           FlaxBlenderbotModel,
           shift_tokens_right,
       )



def _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   int=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Tuple=None					,				lowerCamelCase:   Union[str, Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				)	->						Union[str, Any]:







      '''simple docstring'''






      if attention_mask is None:
            __A          =				np.where(input_ids != config.pad_token_id					,				1					,				0 )
      if decoder_attention_mask is None:
            __A          =				np.where(decoder_input_ids != config.pad_token_id					,				1					,				0 )
      if head_mask is None:
            __A          =				np.ones((config.encoder_layers, config.encoder_attention_heads) )
      if decoder_head_mask is None:
            __A          =				np.ones((config.decoder_layers, config.decoder_attention_heads) )
      if cross_attn_head_mask is None:
            __A          =				np.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": attention_mask,
      }





class A_					:




   def __init__(self	:int    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Dict=13    ,   _UpperCamelCase	:Optional[Any]=7    ,   _UpperCamelCase	:str=True    ,   _UpperCamelCase	:Tuple=False    ,   _UpperCamelCase	:int=99    ,   _UpperCamelCase	:int=16    ,   _UpperCamelCase	:int=2    ,   _UpperCamelCase	:int=4    ,   _UpperCamelCase	:str=4    ,   _UpperCamelCase	:Dict="gelu"    ,   _UpperCamelCase	:int=0.1    ,   _UpperCamelCase	:Tuple=0.1    ,   _UpperCamelCase	:Union[str, Any]=32    ,   _UpperCamelCase	:Any=2    ,   _UpperCamelCase	:Union[str, Any]=1    ,   _UpperCamelCase	:Tuple=0    ,   _UpperCamelCase	:List[str]=0.0_2    ,   )->					str:
         __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_act
         __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
         __A          =				initializer_range




   def _lowerCAmelCase      (self	:Optional[int] )->					Union[str, Any]:
         __A          =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,   self.vocab_size )    ,   3    ,   self.vocab_size )
         __A          =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,   dtype=np.intaa ))    ,   -1 )

         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )

         __A          =				BlenderbotConfig(
             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_id=self.eos_token_id    ,   bos_token_id=self.bos_token_id    ,   pad_token_id=self.pad_token_id    ,   initializer_range=self.initializer_range    ,   use_cache=_UpperCamelCase    ,   )
         __A          =				prepare_blenderbot_inputs_dict(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Tuple:
         __A   ,						__A          =				self.prepare_config_and_inputs()
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Optional[int] )->					str:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

         __A          =				model.encode(inputs_dict['''input_ids'''] )

         __A   ,						__A          =				(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         __A          =				model.init_cache(decoder_input_ids.shape[0]    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,   dtype='''i4''' )

         __A          =				jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,   )
         __A          =				model.decode(
             decoder_input_ids[:, :-1]    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   past_key_values=_UpperCamelCase    ,   decoder_position_ids=_UpperCamelCase    ,   )

         __A          =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,   dtype='''i4''' )
         __A          =				model.decode(
             decoder_input_ids[:, -1:]    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   past_key_values=outputs_cache.past_key_values    ,   decoder_position_ids=_UpperCamelCase    ,   )

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase )

         __A          =				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 _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Any )->					Dict:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

         __A          =				model.encode(inputs_dict['''input_ids'''] )

         __A   ,						__A          =				(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         __A          =				jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
             ]    ,   axis=-1    ,   )

         __A          =				model.init_cache(decoder_input_ids.shape[0]    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,   )

         __A          =				model.decode(
             decoder_input_ids[:, :-1]    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   past_key_values=_UpperCamelCase    ,   decoder_position_ids=_UpperCamelCase    ,   )
         __A          =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,   dtype='''i4''' )
         __A          =				model.decode(
             decoder_input_ids[:, -1:]    ,   _UpperCamelCase    ,   past_key_values=outputs_cache.past_key_values    ,   decoder_attention_mask=_UpperCamelCase    ,   decoder_position_ids=_UpperCamelCase    ,   )

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase )

         __A          =				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}""" )





@require_flax
class A_					(			unittest.TestCase   ):
   lowerCAmelCase__        = 99




   def _lowerCAmelCase      (self	:Dict )->					int:
         __A          =				np.array(
             [
                 [71, 82, 18, 33, 46, 91, 2],
                 [68, 34, 26, 58, 30, 82, 2],
                 [5, 97, 17, 39, 94, 40, 2],
                 [76, 83, 94, 25, 70, 78, 2],
                 [87, 59, 41, 35, 48, 66, 2],
                 [55, 13, 16, 58, 5, 2, 1],  # note padding
                 [64, 27, 31, 51, 12, 75, 2],
                 [52, 64, 86, 17, 83, 39, 2],
                 [48, 61, 9, 24, 71, 82, 2],
                 [26, 1, 60, 48, 22, 13, 2],
                 [21, 5, 62, 28, 14, 76, 2],
                 [45, 98, 37, 86, 59, 48, 2],
                 [70, 70, 50, 9, 28, 0, 2],
             ]    ,   dtype=np.intaa    ,   )

         __A          =				input_ids.shape[0]
         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=24    ,   encoder_layers=2    ,   decoder_layers=2    ,   encoder_attention_heads=2    ,   decoder_attention_heads=2    ,   encoder_ffn_dim=32    ,   decoder_ffn_dim=32    ,   max_position_embeddings=48    ,   eos_token_id=2    ,   pad_token_id=1    ,   bos_token_id=0    ,   )
         return config, input_ids, batch_size




   def _lowerCAmelCase      (self	:Any )->					List[Any]:
         __A   ,						__A   ,						__A          =				self._get_config_and_data()
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				lm_model(input_ids=_UpperCamelCase )
         __A          =				(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:int )->					Dict:
         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=14    ,   encoder_layers=2    ,   decoder_layers=2    ,   encoder_attention_heads=2    ,   decoder_attention_heads=2    ,   encoder_ffn_dim=8    ,   decoder_ffn_dim=8    ,   max_position_embeddings=48    ,   )
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,   dtype=np.intaa )
         __A          =				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,   dtype=np.intaa )
         __A          =				lm_model(input_ids=_UpperCamelCase    ,   decoder_input_ids=_UpperCamelCase )
         __A          =				(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )






   def _lowerCAmelCase      (self	:Tuple )->					Tuple:
         __A          =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,   dtype=np.intaa )
         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )
         __A          =				np.equal(_UpperCamelCase    ,   1 ).astype(np.floataa ).sum()
         __A          =				np.equal(_UpperCamelCase    ,   1 ).astype(np.floataa ).sum()
         self.assertEqual(shifted.shape    ,   input_ids.shape )
         self.assertEqual(_UpperCamelCase    ,   n_pad_before - 1 )
         self.assertTrue(np.equal(shifted[:, 0]    ,   2 ).all() )





@require_flax
class A_					(			_lowerCamelCase ,      unittest.TestCase ,      _lowerCamelCase   ):
   lowerCAmelCase__        = True
   lowerCAmelCase__        = (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   lowerCAmelCase__        = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




   def _lowerCAmelCase      (self	:List[str] )->					Optional[int]:
         __A          =				FlaxBlenderbotModelTester(self )




   def _lowerCAmelCase      (self	:List[str] )->					List[str]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Union[str, Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				self._prepare_for_class(_UpperCamelCase    ,   _UpperCamelCase )
                     __A          =				model_class(_UpperCamelCase )

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

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				encode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				encode_jitted(**_UpperCamelCase ).to_tuple()

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




   def _lowerCAmelCase      (self	:List[str] )->					List[Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				model_class(_UpperCamelCase )
                     __A          =				model.encode(inputs_dict['''input_ids''']    ,   inputs_dict['''attention_mask'''] )

                     __A          =				{
                         '''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(_UpperCamelCase	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Dict ):
                           return model.decode(
                               decoder_input_ids=_UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   encoder_outputs=_UpperCamelCase    ,   )

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				decode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				decode_jitted(**_UpperCamelCase ).to_tuple()

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




   @slow
   def _lowerCAmelCase      (self	:int )->					Optional[int]:
         for model_class_name in self.all_model_classes:
               __A          =				model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __A          =				np.ones((1, 1) ) * model.config.eos_token_id
               __A          =				model(_UpperCamelCase )
               self.assertIsNotNone(_UpperCamelCase )






   @unittest.skipUnless(jax_device != '''cpu'''    ,   '''3B test too slow on CPU.''' )
   @slow
   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         __A          =				{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
         __A          =				{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

         __A          =				FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''    ,   from_pt=_UpperCamelCase )
         __A          =				BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )

         __A          =				['''Sam''']
         __A          =				tokenizer(_UpperCamelCase    ,   return_tensors='''jax''' )

         __A          =				model.generate(**_UpperCamelCase    ,   **_UpperCamelCase )
         __A          =				'''Sam is a great name. It means "sun" in Gaelic.'''

         __A          =				tokenizer.batch_decode(_UpperCamelCase    ,   **_UpperCamelCase )
         assert generated_txt[0].strip() == tgt_text





