




'''simple docstring'''


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase_      :  List[Any]				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  Optional[int]				     =  {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Any   							=					'''gptj'''
      __magic_name__  :       Optional[int]   							=					{
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }






      def __init__( self   :  str						,						lowercase__   :  int=5_0400						,						lowercase__   :  Tuple=2048						,						lowercase__   :  List[str]=4096						,						lowercase__   :  List[str]=28						,						lowercase__   :  Optional[Any]=16						,						lowercase__   :  Optional[int]=64						,						lowercase__   :  List[str]=None						,						lowercase__   :  Any="gelu_new"						,						lowercase__   :  List[str]=0.0						,						lowercase__   :  Optional[int]=0.0						,						lowercase__   :  List[str]=0.0						,						lowercase__   :  List[Any]=1e-5						,						lowercase__   :  Tuple=0.02						,						lowercase__   :  List[str]=True						,						lowercase__   :  Any=5_0256						,						lowercase__   :  Optional[Any]=5_0256						,						lowercase__   :  Optional[int]=False						,						**lowercase__   :  Tuple						,						):


           '''simple docstring'''

           a_     :							List[Any]     						=  vocab_size
           a_     :							Tuple     						=  n_positions
           a_     :							str     						=  n_embd
           a_     :							Optional[Any]     						=  n_layer
           a_     :							List[str]     						=  n_head
           a_     :							List[str]     						=  n_inner
           a_     :							str     						=  rotary_dim
           a_     :							Optional[int]     						=  activation_function
           a_     :							Optional[Any]     						=  resid_pdrop
           a_     :							Dict     						=  embd_pdrop
           a_     :							str     						=  attn_pdrop
           a_     :							Union[str, Any]     						=  layer_norm_epsilon
           a_     :							Optional[int]     						=  initializer_range
           a_     :							List[str]     						=  use_cache

           a_     :							Tuple     						=  bos_token_id
           a_     :							Union[str, Any]     						=  eos_token_id

           super().__init__(
               bos_token_id=lowercase__						,						eos_token_id=lowercase__						,						tie_word_embeddings=lowercase__						,						**lowercase__       )







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def __init__( self   :  Dict						,						lowercase__   :  PretrainedConfig						,						lowercase__   :  str = "default"						,						lowercase__   :  List[PatchingSpec] = None						,						lowercase__   :  bool = False						,						):


           '''simple docstring'''

           super().__init__(lowercase__						,						task=lowercase__						,						patching_specs=lowercase__						,						use_past=lowercase__       )
           if not getattr(self._config						,						"""pad_token_id"""						,						lowercase__       ):
                # TODO: how to do that better?
                a_     :							Any     						=  0






      @property
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}       )
           if self.use_past:
                self.fill_with_past_key_values_(lowercase__						,						direction="""inputs"""       )
                a_     :							List[str]     						=  {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                a_     :							List[str]     						=  {0: """batch""", 1: """sequence"""}

           return common_inputs






      @property
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           return self._config.n_layer






      @property
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           return self._config.n_head






      def 							lowercase_						( self   :  int						,						lowercase__   :  PreTrainedTokenizer						,						lowercase__   :  int = -1						,						lowercase__   :  int = -1						,						lowercase__   :  bool = False						,						lowercase__   :  Optional[TensorType] = None						,						):


           '''simple docstring'''

           a_     :							Any     						=  super(lowercase__						,						self       ).generate_dummy_inputs(
               lowercase__						,						batch_size=lowercase__						,						seq_length=lowercase__						,						is_pair=lowercase__						,						framework=lowercase__       )

           # We need to order the input in the way they appears in the forward()
           a_     :							Union[str, Any]     						=  OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}       )

           # Need to add the past_keys
           if self.use_past:
                if not is_torch_available():
                     raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
                else:
                     import torch

                     a_						,     a_     :							List[str]     						=  common_inputs["""input_ids"""].shape
                     # Not using the same length for past_key_values
                     a_     :							Any     						=  seqlen + 2
                     a_     :							Tuple     						=  (
                         batch,
                         self.num_attention_heads,
                         past_key_values_length,
                         self._config.hidden_size // self.num_attention_heads,
                     )
                     a_     :							Optional[Any]     						=  [
                         (torch.zeros(lowercase__       ), torch.zeros(lowercase__       )) for _ in range(self.num_layers       )
                     ]

           a_     :							List[Any]     						=  common_inputs["""attention_mask"""]
           if self.use_past:
                a_     :							List[Any]     						=  ordered_inputs["""attention_mask"""].dtype
                a_     :							List[str]     						=  torch.cat(
                    [ordered_inputs["""attention_mask"""], torch.ones(lowercase__						,						lowercase__						,						dtype=lowercase__       )]						,						dim=1       )

           return ordered_inputs






      @property
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           return 13






'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


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


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  layer_1_error * sigmoid_function(UpperCamelCase__ ,    UpperCamelCase__				)
          # Update weight
          weight += INITIAL_VALUE * layer_1_delta

     return layer_a * 100


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase_      :  str				     =  int(input('Expected value: '))
 lowerCAmelCase_      :  str				     =  int(input('Number of propagations: '))
 print(forward_propagation(expected, number_propagations))
