

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


a_     :    Union[str, Any]     		=   logging.get_logger(__name__)

a_     :    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   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __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[int]				,     snake_case__			:   Tuple=5_0_4_0_0				,     snake_case__			:   Tuple=2_0_4_8				,     snake_case__			:   Tuple=4_0_9_6				,     snake_case__			:   Dict=2_8				,     snake_case__			:   Dict=1_6				,     snake_case__			:   Tuple=6_4				,     snake_case__			:   int=None				,     snake_case__			:   str="gelu_new"				,     snake_case__			:   Union[str, Any]=0.0				,     snake_case__			:   Tuple=0.0				,     snake_case__			:   Union[str, Any]=0.0				,     snake_case__			:   Optional[int]=1E-5				,     snake_case__			:   Dict=0.02				,     snake_case__			:   Dict=True				,     snake_case__			:   Tuple=5_0_2_5_6				,     snake_case__			:   Tuple=5_0_2_5_6				,     snake_case__			:   Any=False				,     **snake_case__			:   List[str]				,     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      bos_token_id
     SCREAMING_SNAKE_CASE		    =      eos_token_id

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




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):

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

     """simple docstring"""






     super().__init__(snake_case__				,     task=snake_case__				,     patching_specs=snake_case__				,     use_past=snake_case__     )
     if not getattr(self._config				,     'pad_token_id'				,     snake_case__     ):
      # TODO: how to do that better?
      SCREAMING_SNAKE_CASE		    =      0

    @property
    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}     )
     if self.use_past:
      self.fill_with_past_key_values_(snake_case__				,     direction='inputs'     )
      SCREAMING_SNAKE_CASE		    =      {0: 'batch', 1: 'past_sequence + sequence'}
     else:
      SCREAMING_SNAKE_CASE		    =      {0: 'batch', 1: 'sequence'}

     return common_inputs

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

     """simple docstring"""






     return self._config.n_layer

    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     return self._config.n_head

    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   PreTrainedTokenizer				,     snake_case__			:   int = -1				,     snake_case__			:   int = -1				,     snake_case__			:   bool = False				,     snake_case__			:   Optional[TensorType] = None				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      super(snake_case__				,     self     ).generate_dummy_inputs(
         snake_case__				,     batch_size=snake_case__				,     seq_length=snake_case__				,     is_pair=snake_case__				,     framework=snake_case__     )

     # We need to order the input in the way they appears in the forward()
     SCREAMING_SNAKE_CASE		    =      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

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

     SCREAMING_SNAKE_CASE		    =      common_inputs['attention_mask']
     if self.use_past:
      SCREAMING_SNAKE_CASE		    =      ordered_inputs['attention_mask'].dtype
      SCREAMING_SNAKE_CASE		    =      torch.cat(
          [ordered_inputs['attention_mask'], torch.ones(snake_case__				,     snake_case__				,     dtype=snake_case__     )]				,     dim=1     )

     return ordered_inputs




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

     """simple docstring"""






     return 1_3




import math


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int						)		->				list[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      2
 SCREAMING_SNAKE_CASE		    =      int(math.sqrt(_UpperCamelCase						)						)  # Size of every segment
 SCREAMING_SNAKE_CASE		    =      [True] * (end + 1)
 SCREAMING_SNAKE_CASE		    =      []

 while start <= end:
  if temp[start] is True:
   in_prime.append(_UpperCamelCase						)
   for i in range(start * start		,				end + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False
  start += 1
 prime += in_prime

 SCREAMING_SNAKE_CASE		    =      end + 1
 SCREAMING_SNAKE_CASE		    =      min(2 * end		,				_UpperCamelCase						)

 while low <= n:
  SCREAMING_SNAKE_CASE		    =      [True] * (high - low + 1)
  for each in in_prime:
   SCREAMING_SNAKE_CASE		    =      math.floor(low / each						) * each
   if t < low:
    t += each

   for j in range(_UpperCamelCase		,				high + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False

  for j in range(len(_UpperCamelCase						)						):
   if temp[j] is True:
    prime.append(j + low						)

  SCREAMING_SNAKE_CASE		    =      high + 1
  SCREAMING_SNAKE_CASE		    =      min(high + end		,				_UpperCamelCase						)

 return prime




print(sieve(10**6))

