'''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


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							'gptj'
		lowercase   :      Tuple       =							{
		    'max_position_embeddings': 'n_positions',
		    'hidden_size': 'n_embd',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}






		def __init__(   self		,						__A=5_0400		,						__A=2048		,						__A=4096		,						__A=28		,						__A=16		,						__A=64		,						__A=None		,						__A="gelu_new"		,						__A=0.0		,						__A=0.0		,						__A=0.0		,						__A=1E-5		,						__A=0.02		,						__A=True		,						__A=5_0256		,						__A=5_0256		,						__A=False		,						**__A		,						) ->     Union[str, Any]:
								_lowerCAmelCase										=vocab_size
								_lowerCAmelCase										=n_positions
								_lowerCAmelCase										=n_embd
								_lowerCAmelCase										=n_layer
								_lowerCAmelCase										=n_head
								_lowerCAmelCase										=n_inner
								_lowerCAmelCase										=rotary_dim
								_lowerCAmelCase										=activation_function
								_lowerCAmelCase										=resid_pdrop
								_lowerCAmelCase										=embd_pdrop
								_lowerCAmelCase										=attn_pdrop
								_lowerCAmelCase										=layer_norm_epsilon
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=use_cache

								_lowerCAmelCase										=bos_token_id
								_lowerCAmelCase										=eos_token_id

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

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A = "default"		,						__A = None		,						__A = 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?
														_lowerCAmelCase										=0






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

								return common_inputs






		@property
		def 		UpperCamelCase__       (   self				) ->     int:
								return self._config.n_layer






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






		def 		UpperCamelCase__       (   self		,						__A		,						__A = -1		,						__A = -1		,						__A = False		,						__A = None		,						) ->     Mapping[str, Any]:
								_lowerCAmelCase										=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()
								_lowerCAmelCase										=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										=common_inputs['input_ids'].shape
																				# Not using the same length for past_key_values
																				_lowerCAmelCase										=seqlen + 2
																				_lowerCAmelCase										=(
																				    batch,
																				    self.num_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // self.num_attention_heads,
																				)
																				_lowerCAmelCase										=[
																				    (torch.zeros(__A				), torch.zeros(__A				)) for _ in range(self.num_layers				)
																				]

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

								return ordered_inputs






		@property
		def 		UpperCamelCase__       (   self				) ->     int:
								return 13






'''simple docstring'''

from __future__ import annotations

lowercase_     		= 10



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=1
						_lowerCAmelCase										=max(a__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												_lowerCAmelCase										=[[] for _ in range(a__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		_lowerCAmelCase										=int((i / placement) % RADIX      )
																		buckets[tmp].append(a__      )
												# put each buckets' contents into list_of_ints
												_lowerCAmelCase										=0
												for b in range(a__      ):
																		for i in buckets[b]:
																								_lowerCAmelCase										=i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
							import doctest

							doctest.testmod()





