"""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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "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  UpperCamelCase	(    __lowercase						):
   lowercase							=						'gptj'
   lowercase							=						{
       'max_position_embeddings': 'n_positions',
       'hidden_size': 'n_embd',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }
   def __init__(    self  ,__UpperCamelCase=5_0400  ,__UpperCamelCase=2048  ,__UpperCamelCase=4096  ,__UpperCamelCase=28  ,__UpperCamelCase=16  ,__UpperCamelCase=64  ,__UpperCamelCase=None  ,__UpperCamelCase="gelu_new"  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=1e-5  ,__UpperCamelCase=0.02  ,__UpperCamelCase=True  ,__UpperCamelCase=5_0256  ,__UpperCamelCase=5_0256  ,__UpperCamelCase=False  ,**__UpperCamelCase  ,)			->   Union[str, Any]:


        '''simple docstring'''


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

        lowercase_			:       int									=			bos_token_id
        lowercase_			:       Tuple									=			eos_token_id

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







class  UpperCamelCase	(    __lowercase						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase = "default"  ,__UpperCamelCase = None  ,__UpperCamelCase = False  ,)			->   Dict:


        '''simple docstring'''


        super().__init__(__A  ,task=__A  ,patching_specs=__A  ,use_past=__A    )
        if not getattr(self._config  ,'pad_token_id'  ,__A    ):
             # TODO: how to do that better?
             lowercase_			:       List[str]									=			0
   @property
   def     _UpperCAmelCase     (    self    )			->   Mapping[str, Mapping[int, str]]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}    )
        if self.use_past:
             self.fill_with_past_key_values_(__A  ,direction='inputs'    )
             lowercase_			:       Optional[int]									=			{0: 'batch', 1: 'past_sequence + sequence'}
        else:
             lowercase_			:       int									=			{0: 'batch', 1: 'sequence'}

        return common_inputs
   @property
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return self._config.n_layer
   @property
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return self._config.n_head
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = -1  ,__UpperCamelCase = -1  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,)			->   Mapping[str, Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			super(__A  ,self    ).generate_dummy_inputs(
            __A  ,batch_size=__A  ,seq_length=__A  ,is_pair=__A  ,framework=__A    )

        # We need to order the input in the way they appears in the forward()
        lowercase_			:       Optional[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

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

        lowercase_			:       Union[str, Any]									=			common_inputs['attention_mask']
        if self.use_past:
             lowercase_			:       str									=			ordered_inputs['attention_mask'].dtype
             lowercase_			:       Optional[int]									=			torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(__A  ,__A  ,dtype=__A    )]  ,dim=1    )

        return ordered_inputs







   @property
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return 13


"""simple docstring"""







import numpy as np



def     lowercase__( __SCREAMING_SNAKE_CASE							:  np.array   ):
     return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
       import doctest

       doctest.testmod()

