





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__       = logging.get_logger(__name__)

lowerCAmelCase__       = {
    "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 						__magic_name__    (       __lowercase    ):
 UpperCAmelCase							    =						'gptj'
 UpperCAmelCase							    =						{
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }


 def __init__(						self		: Optional[Any]			,						lowerCAmelCase__		: List[str]=5_0_4_0_0			,						lowerCAmelCase__		: Any=2_0_4_8			,						lowerCAmelCase__		: Dict=4_0_9_6			,						lowerCAmelCase__		: Union[str, Any]=2_8			,						lowerCAmelCase__		: Tuple=1_6			,						lowerCAmelCase__		: Dict=6_4			,						lowerCAmelCase__		: List[Any]=None			,						lowerCAmelCase__		: Any="gelu_new"			,						lowerCAmelCase__		: Union[str, Any]=0.0			,						lowerCAmelCase__		: Dict=0.0			,						lowerCAmelCase__		: int=0.0			,						lowerCAmelCase__		: Tuple=1e-5			,						lowerCAmelCase__		: str=0.02			,						lowerCAmelCase__		: int=True			,						lowerCAmelCase__		: str=5_0_2_5_6			,						lowerCAmelCase__		: Union[str, Any]=5_0_2_5_6			,						lowerCAmelCase__		: int=False			,						**lowerCAmelCase__		: Tuple			,						)    ->      Union[str, Any]:
   UpperCAmelCase					      =  vocab_size
   UpperCAmelCase					      =  n_positions
   UpperCAmelCase					      =  n_embd
   UpperCAmelCase					      =  n_layer
   UpperCAmelCase					      =  n_head
   UpperCAmelCase					      =  n_inner
   UpperCAmelCase					      =  rotary_dim
   UpperCAmelCase					      =  activation_function
   UpperCAmelCase					      =  resid_pdrop
   UpperCAmelCase					      =  embd_pdrop
   UpperCAmelCase					      =  attn_pdrop
   UpperCAmelCase					      =  layer_norm_epsilon
   UpperCAmelCase					      =  initializer_range
   UpperCAmelCase					      =  use_cache

   UpperCAmelCase					      =  bos_token_id
   UpperCAmelCase					      =  eos_token_id

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






class 						__magic_name__    (       __lowercase    ):


 def __init__(						self		: Tuple			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: Tuple = "default"			,						lowerCAmelCase__		: Any = None			,						lowerCAmelCase__		: Any = False			,						)    ->      Dict:
   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?
     UpperCAmelCase					      =  0


 @property
 def _UpperCamelCase	(						self		: Dict    )    ->      Mapping[str, Mapping[int, str]]:
   UpperCAmelCase					      =  OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
   if self.use_past:
     self.fill_with_past_key_values_(__A			,						direction="inputs"    )
     UpperCAmelCase					      =  {0: "batch", 1: "past_sequence + sequence"}
   else:
     UpperCAmelCase					      =  {0: "batch", 1: "sequence"}

   return common_inputs


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      int:
   return self._config.n_layer


 @property
 def _UpperCamelCase	(						self		: Dict    )    ->      int:
   return self._config.n_head


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: Dict = -1			,						lowerCAmelCase__		: Optional[int] = -1			,						lowerCAmelCase__		: List[str] = False			,						lowerCAmelCase__		: Tuple = None			,						)    ->      Mapping[str, Any]:
   UpperCAmelCase					      =  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()
   UpperCAmelCase					      =  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

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

   UpperCAmelCase					      =  common_inputs["attention_mask"]
   if self.use_past:
     UpperCAmelCase					      =  ordered_inputs["attention_mask"].dtype
     UpperCAmelCase					      =  torch.cat(
         [ordered_inputs["attention_mask"], torch.ones(__A			,						__A			,						dtype=__A    )]			,						dim=1    )

   return ordered_inputs


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      int:
   return 1_3






def      _lowerCAmelCase(	__A			):

  if not isinstance(__A   ,       __A			):
    raise TypeError("only integers accepted as input"			)
  else:
    UpperCAmelCase					      =  str(abs(__A			)			)
    UpperCAmelCase					      =  [list(__A			) for char in range(len(__A			)			)]
    for index in range(len(__A			)			):
      num_transpositions[index].pop(__A			)
    return max(
        int("".join(list(__A			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
      __import__("doctest").testmod()



