





"""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


__A											=						logging.get_logger(__name__)

__A											=						{
    """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			):
       """simple docstring"""




       __magic_name__	:Any						    =   """gptj"""
       __magic_name__	:Dict						    =   {
           """max_position_embeddings""": """n_positions""",
           """hidden_size""": """n_embd""",
           """num_attention_heads""": """n_head""",
           """num_hidden_layers""": """n_layer""",
       }






       def __init__(       self       ,					__UpperCAmelCase=5_0_4_0_0       ,					__UpperCAmelCase=2_0_4_8       ,					__UpperCAmelCase=4_0_9_6       ,					__UpperCAmelCase=2_8       ,					__UpperCAmelCase=1_6       ,					__UpperCAmelCase=6_4       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase="gelu_new"       ,					__UpperCAmelCase=0.0       ,					__UpperCAmelCase=0.0       ,					__UpperCAmelCase=0.0       ,					__UpperCAmelCase=1E-5       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=5_0_2_5_6       ,					__UpperCAmelCase=5_0_2_5_6       ,					__UpperCAmelCase=False       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

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

         lowerCAmelCase__     :Optional[int]											=				bos_token_id
         lowerCAmelCase__     :Tuple											=				eos_token_id

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






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = "default"       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					):



         '''simple docstring'''

         super().__init__(__UpperCAmelCase       ,					task=__UpperCAmelCase       ,					patching_specs=__UpperCAmelCase       ,					use_past=__UpperCAmelCase )
         if not getattr(self._config       ,					'pad_token_id'       ,					__UpperCAmelCase ):
           # TODO: how to do that better?
           lowerCAmelCase__     :Optional[int]											=				0






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
         if self.use_past:
           self.fill_with_past_key_values_(__UpperCAmelCase       ,					direction='inputs' )
           lowerCAmelCase__     :List[Any]											=				{0: 'batch', 1: 'past_sequence + sequence'}
         else:
           lowerCAmelCase__     :Optional[Any]											=				{0: 'batch', 1: 'sequence'}

         return common_inputs






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self._config.n_layer






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self._config.n_head






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				super(__UpperCAmelCase       ,					self ).generate_dummy_inputs(
             __UpperCAmelCase       ,					batch_size=__UpperCAmelCase       ,					seq_length=__UpperCAmelCase       ,					is_pair=__UpperCAmelCase       ,					framework=__UpperCAmelCase )

         # We need to order the input in the way they appears in the forward()
         lowerCAmelCase__     :int											=				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

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

         lowerCAmelCase__     :int											=				common_inputs['attention_mask']
         if self.use_past:
           lowerCAmelCase__     :int											=				ordered_inputs['attention_mask'].dtype
           lowerCAmelCase__     :Any											=				torch.cat(
               [ordered_inputs['attention_mask'], torch.ones(__UpperCAmelCase       ,					__UpperCAmelCase       ,					dtype=__UpperCAmelCase )]       ,					dim=1 )

         return ordered_inputs






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 1_3





'''simple docstring'''





from __future__ import annotations

_UpperCamelCase											=		10

def 			a_	(				_lowerCAmelCase			) -> list[int]:
  __lowerCamelCase			:			str       			=					1
  __lowerCamelCase			:			Union[str, Any]       			=					max(_lowerCAmelCase			)
  while placement <= max_digit:
    # declare and initialize empty buckets
    __lowerCamelCase			:			list[list]       			=					[[] for _ in range(_lowerCAmelCase			)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      __lowerCamelCase			:			List[str]       			=					int((i / placement) % RADIX			)
      buckets[tmp].append(_lowerCAmelCase			)
    # put each buckets' contents into list_of_ints
    __lowerCamelCase			:			Dict       			=					0
    for b in range(_lowerCAmelCase			):
      for i in buckets[b]:
        __lowerCamelCase			:			List[str]       			=					i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()






