


"""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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "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    lowerCamelCase__						(		_a    ):
      a :     Dict											=		"""gptj"""
      a :     Any											=		{
          """max_position_embeddings""": """n_positions""",
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(		self	:	List[Any]      ,       A_	:	Tuple=5_0_4_0_0      ,       A_	:	str=2_0_4_8      ,       A_	:	str=4_0_9_6      ,       A_	:	Dict=2_8      ,       A_	:	Union[str, Any]=1_6      ,       A_	:	List[str]=6_4      ,       A_	:	Optional[int]=None      ,       A_	:	Optional[Any]="gelu_new"      ,       A_	:	Optional[int]=0.0      ,       A_	:	str=0.0      ,       A_	:	str=0.0      ,       A_	:	Tuple=1e-5      ,       A_	:	List[str]=0.02      ,       A_	:	Any=True      ,       A_	:	int=5_0_2_5_6      ,       A_	:	Optional[Any]=5_0_2_5_6      ,       A_	:	Optional[int]=False      ,       **A_	:	List[Any]      ,       ):






       '''simple docstring'''


       __lowercase									=	vocab_size
       __lowercase									=	n_positions
       __lowercase									=	n_embd
       __lowercase									=	n_layer
       __lowercase									=	n_head
       __lowercase									=	n_inner
       __lowercase									=	rotary_dim
       __lowercase									=	activation_function
       __lowercase									=	resid_pdrop
       __lowercase									=	embd_pdrop
       __lowercase									=	attn_pdrop
       __lowercase									=	layer_norm_epsilon
       __lowercase									=	initializer_range
       __lowercase									=	use_cache

       __lowercase									=	bos_token_id
       __lowercase									=	eos_token_id

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






class    lowerCamelCase__						(		_a    ):
      def __init__(		self	:	Union[str, Any]      ,       A_	:	PretrainedConfig      ,       A_	:	str = "default"      ,       A_	:	List[PatchingSpec] = None      ,       A_	:	bool = False      ,       ):






       '''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									=	0
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
       if self.use_past:
        self.fill_with_past_key_values_(A_      ,       direction="""inputs"""							)
        __lowercase									=	{0: """batch""", 1: """past_sequence + sequence"""}
       else:
        __lowercase									=	{0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       return self._config.n_layer
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       return self._config.n_head
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	PreTrainedTokenizer      ,       A_	:	int = -1      ,       A_	:	int = -1      ,       A_	:	bool = False      ,       A_	:	Optional[TensorType] = None      ,       ):






       '''simple docstring'''


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

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

       return ordered_inputs






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       return 1_3




"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_      (				UpperCamelCase__	: list[float] ):






 """simple docstring"""







 if len(UpperCamelCase__ ) < 2:
  raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
 if any(i <= 0 for i in nums ):
  raise ValueError("""All values must be greater than 0""" )
 __lowercase									=	nums.copy()
 copy_nums.sort()
 return copy_nums[-1] < sum(copy_nums[:-1] )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
