




'''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	:						Optional[int]			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[Any]			=     {
    "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       (							lowerCAmelCase):
       _a 					=						'''gptj'''
       _a 					=						{
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }






       def __init__(   self:					List[str]       ,					_lowerCAmelCase:					Union[str, Any]=5_04_00       ,					_lowerCAmelCase:					Optional[int]=20_48       ,					_lowerCAmelCase:					Dict=40_96       ,					_lowerCAmelCase:					str=28       ,					_lowerCAmelCase:					Dict=16       ,					_lowerCAmelCase:					str=64       ,					_lowerCAmelCase:					Dict=None       ,					_lowerCAmelCase:					Optional[int]="gelu_new"       ,					_lowerCAmelCase:					Optional[int]=0.0       ,					_lowerCAmelCase:					List[str]=0.0       ,					_lowerCAmelCase:					str=0.0       ,					_lowerCAmelCase:					Any=1e-5       ,					_lowerCAmelCase:					str=0.02       ,					_lowerCAmelCase:					str=True       ,					_lowerCAmelCase:					Any=5_02_56       ,					_lowerCAmelCase:					Union[str, Any]=5_02_56       ,					_lowerCAmelCase:					Optional[int]=False       ,					**_lowerCAmelCase:					int       ,					):
         lowercase    :str      	=						vocab_size
         lowercase    :Union[str, Any]      	=						n_positions
         lowercase    :Optional[int]      	=						n_embd
         lowercase    :int      	=						n_layer
         lowercase    :Tuple      	=						n_head
         lowercase    :Optional[Any]      	=						n_inner
         lowercase    :List[Any]      	=						rotary_dim
         lowercase    :List[Any]      	=						activation_function
         lowercase    :Tuple      	=						resid_pdrop
         lowercase    :Optional[int]      	=						embd_pdrop
         lowercase    :Any      	=						attn_pdrop
         lowercase    :Optional[int]      	=						layer_norm_epsilon
         lowercase    :str      	=						initializer_range
         lowercase    :Optional[int]      	=						use_cache

         lowercase    :Dict      	=						bos_token_id
         lowercase    :List[Any]      	=						eos_token_id

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







class    __lowerCAmelCase       (							lowerCAmelCase):






       def __init__(   self:					Tuple       ,					_lowerCAmelCase:					PretrainedConfig       ,					_lowerCAmelCase:					str = "default"       ,					_lowerCAmelCase:					List[PatchingSpec] = None       ,					_lowerCAmelCase:					bool = False       ,					):
         super().__init__(_lowerCAmelCase       ,					task=_lowerCAmelCase       ,					patching_specs=_lowerCAmelCase       ,					use_past=_lowerCAmelCase				)
         if not getattr(self._config       ,					"pad_token_id"       ,					_lowerCAmelCase				):
           # TODO: how to do that better?
           lowercase    :int      	=						0






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Optional[int]      	=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
         if self.use_past:
           self.fill_with_past_key_values_(_lowerCAmelCase       ,					direction="inputs"				)
           lowercase    :Any      	=						{0: "batch", 1: "past_sequence + sequence"}
         else:
           lowercase    :Optional[int]      	=						{0: "batch", 1: "sequence"}

         return common_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         return self._config.n_layer






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         return self._config.n_head






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					PreTrainedTokenizer       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[TensorType] = None       ,					):
         lowercase    :Union[str, Any]      	=						super(_lowerCAmelCase       ,					self				).generate_dummy_inputs(
             _lowerCAmelCase       ,					batch_size=_lowerCAmelCase       ,					seq_length=_lowerCAmelCase       ,					is_pair=_lowerCAmelCase       ,					framework=_lowerCAmelCase				)

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

         lowercase    :List[Any]      	=						common_inputs["attention_mask"]
         if self.use_past:
           lowercase    :Tuple      	=						ordered_inputs["attention_mask"].dtype
           lowercase    :List[Any]      	=						torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(_lowerCAmelCase       ,					_lowerCAmelCase       ,					dtype=_lowerCAmelCase				)]       ,					dim=1				)

         return ordered_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         return 13









def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase = False   ):
  if n == 2:
    return True
  if not n % 2 or n < 2:
    return False
  if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
    return False
  if n > 3317044064679887385961981 and not allow_probable:
    raise ValueError(
        "Warning: upper bound of deterministic test is exceeded. "
        "Pass allow_probable=True to allow probabilistic test. "
        "A return value of True indicates a probable prime."   )
  # array bounds provided by analysis
  lowercase    :Optional[Any]      	=						[
      2047,
      1373653,
      25326001,
      3215031751,
      2152302898747,
      3474749660383,
      341550071728321,
      1,
      3825123056546413051,
      1,
      1,
      318665857834031151167461,
      3317044064679887385961981,
  ]

  lowercase    :List[Any]      	=						[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
  for idx, _p in enumerate(lowerCamelCase,			1   ):
    if n < _p:
      # then we have our last prime to check
      lowercase    :Tuple      	=						primes[:idx]
      break
  lowercase	,    lowercase    :str      	=						n - 1, 0
  # break up n -1 into a power of 2 (s) and
  # remaining odd component
  # essentially, solve for d * 2 ** s == n - 1
  while d % 2 == 0:
    d //= 2
    s += 1
  for prime in plist:
    lowercase    :Tuple      	=						False
    for r in range(lowerCamelCase   ):
      lowercase    :Any      	=						pow(lowerCamelCase,			d * 2**r,			lowerCamelCase   )
      # see article for analysis explanation for m
      if (r == 0 and m == 1) or ((m + 1) % n == 0):
        lowercase    :Any      	=						True
        # this loop will not determine compositeness
        break
    if pr:
      continue
    # if pr is False, then the above loop never evaluated to true,
    # and the n MUST be composite
    return False
  return True



def       UpperCAmelCase__  (     ):
  assert not miller_rabin(561   )
  assert miller_rabin(563   )
  # 2047

  assert not miller_rabin(838201   )
  assert miller_rabin(838207   )
  # 1_373_653

  assert not miller_rabin(17316001   )
  assert miller_rabin(17316017   )
  # 25_326_001

  assert not miller_rabin(3078386641   )
  assert miller_rabin(3078386653   )
  # 3_215_031_751

  assert not miller_rabin(1713045574801   )
  assert miller_rabin(1713045574819   )
  # 2_152_302_898_747

  assert not miller_rabin(2779799728307   )
  assert miller_rabin(2779799728327   )
  # 3_474_749_660_383

  assert not miller_rabin(113850023909441   )
  assert miller_rabin(113850023909527   )
  # 341_550_071_728_321

  assert not miller_rabin(1275041018848804351   )
  assert miller_rabin(1275041018848804391   )
  # 3_825_123_056_546_413_051

  assert not miller_rabin(79666464458507787791867   )
  assert miller_rabin(79666464458507787791951   )
  # 318_665_857_834_031_151_167_461

  assert not miller_rabin(552840677446647897660333   )
  assert miller_rabin(552840677446647897660359   )
  # 3_317_044_064_679_887_385_961_981
  # upper limit for probabilistic test


if __name__ == "__main__":
     test_miller_rabin()



