


'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__A		=logging.get_logger(__name__)

__A		={
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class 						_snake_case    (   a__		):
					lowerCAmelCase					:Any            =    '''gpt_neo'''
					lowerCAmelCase					:Optional[int]            =    ['''past_key_values''']
					lowerCAmelCase					:List[str]            =    {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




					def __init__(					self							,							_lowerCamelCase=5_0257							,							_lowerCamelCase=2048							,							_lowerCamelCase=2048							,							_lowerCamelCase=24							,							_lowerCamelCase=[[["global", "local"], 12]]							,							_lowerCamelCase=16							,							_lowerCamelCase=None							,							_lowerCamelCase=256							,							_lowerCamelCase="gelu_new"							,							_lowerCamelCase=0.0							,							_lowerCamelCase=0.0							,							_lowerCamelCase=0.0							,							_lowerCamelCase=0.1							,							_lowerCamelCase=1e-5							,							_lowerCamelCase=0.02							,							_lowerCamelCase=True							,							_lowerCamelCase=5_0256							,							_lowerCamelCase=5_0256							,							**_lowerCamelCase							,							):
								UpperCAmelCase__     :    Dict         =   vocab_size
								UpperCAmelCase__     :    int         =   max_position_embeddings
								UpperCAmelCase__     :    str         =   hidden_size
								UpperCAmelCase__     :    Tuple         =   num_layers
								UpperCAmelCase__     :    str         =   num_heads
								UpperCAmelCase__     :    Optional[Any]         =   intermediate_size
								UpperCAmelCase__     :    Dict         =   window_size
								UpperCAmelCase__     :    Any         =   activation_function
								UpperCAmelCase__     :    int         =   resid_dropout
								UpperCAmelCase__     :    List[str]         =   embed_dropout
								UpperCAmelCase__     :    int         =   attention_dropout
								UpperCAmelCase__     :    List[Any]         =   classifier_dropout
								UpperCAmelCase__     :    int         =   layer_norm_epsilon
								UpperCAmelCase__     :    str         =   initializer_range
								UpperCAmelCase__     :    List[str]         =   use_cache

								UpperCAmelCase__     :    str         =   bos_token_id
								UpperCAmelCase__     :    List[Any]         =   eos_token_id

								UpperCAmelCase__     :    Tuple         =   attention_types
								UpperCAmelCase__     :    List[str]         =   self.expand_attention_types_params(__UpperCAmelCase)

								if len(self.attention_layers) != self.num_layers:
											raise ValueError(
											    """Configuration for convolutional module is incorrect. """
											    """It is required that `len(config.attention_layers)` == `config.num_layers` """
											    f'''but is `len(config.attention_layers) = {len(self.attention_layers)}`, '''
											    f'''`config.num_layers = {self.num_layers}`. '''
											    """`config.attention_layers` is prepared using `config.attention_types`. """
											    """Please verify the value of `config.attention_types` argument.""")

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




					@staticmethod
					def  snake_case__  (					_lowerCamelCase):
								UpperCAmelCase__     :    List[str]         =   []
								for item in attention_types:
											for _ in range(item[1]):
														attentions.extend(item[0])
								return attentions







def 			_UpperCamelCase					(						UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__    ):
			import torch

			UpperCAmelCase__     :    Any         =   input.size()
			UpperCAmelCase__     :    Tuple         =   len(UpperCamelCase__    )
			UpperCAmelCase__     :    Optional[int]         =   shape[dimension]

			UpperCAmelCase__     :    str         =   torch.arange(0       ,					UpperCamelCase__       ,					UpperCamelCase__    )
			UpperCAmelCase__     :    str         =   torch.div(sizedim - size       ,					UpperCamelCase__       ,					rounding_mode="""floor"""    ) + 1
			UpperCAmelCase__     :    Optional[Any]         =   torch.arange(UpperCamelCase__    ) + low_indices[:min_length][:, None]

			UpperCAmelCase__     :    Dict         =   [slice(UpperCamelCase__    )] * rank
			UpperCAmelCase__     :    int         =   indices
			UpperCAmelCase__     :    Any         =   input[s]

			UpperCAmelCase__     :    int         =   list(range(0       ,					rank + 1    )    )
			perm.append(perm.pop(dimension + 1    )    )

			return sliced.permute(UpperCamelCase__    )







def 			_UpperCamelCase					(						UpperCamelCase__       ,					UpperCamelCase__    ):
			import torch

			UpperCAmelCase__     :    Optional[Any]         =   torch.arange(1       ,					UpperCamelCase__    )
			UpperCAmelCase__     :    str         =   torch.remainder(UpperCamelCase__       ,					UpperCamelCase__    )
			UpperCAmelCase__     :    Optional[int]         =   remainders == 0
			UpperCAmelCase__     :    List[str]         =   candidates[divisor_indices]
			UpperCAmelCase__     :    List[Any]         =   torch.max(UpperCamelCase__    )
			return largest_divisor, torch.div(UpperCamelCase__       ,					UpperCamelCase__       ,					rounding_mode="""floor"""    )


class 						_snake_case    (   a__		):




					@property
					def  snake_case__  (					self):
								UpperCAmelCase__     :    Dict         =   OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
								if self.use_past:
											self.fill_with_past_key_values_(__UpperCAmelCase							,							direction="""inputs""")
											UpperCAmelCase__     :    Optional[Any]         =   {0: """batch""", 1: """past_sequence + sequence"""}
								else:
											UpperCAmelCase__     :    Optional[Any]         =   {0: """batch""", 1: """sequence"""}

								return common_inputs




					@property
					def  snake_case__  (					self):
								return self._config.num_heads




					def  snake_case__  (					self							,							_lowerCamelCase							,							_lowerCamelCase = -1							,							_lowerCamelCase = -1							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__     :    Any         =   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()
								UpperCAmelCase__     :    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

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

								UpperCAmelCase__     :    Optional[int]         =   common_inputs["""attention_mask"""]
								if self.use_past:
											UpperCAmelCase__     :    Any         =   ordered_inputs["""attention_mask"""].dtype
											UpperCAmelCase__     :    str         =   torch.cat(
											    [ordered_inputs["""attention_mask"""], torch.ones(__UpperCAmelCase							,							__UpperCAmelCase							,							dtype=__UpperCAmelCase)]							,							dim=1)

								return ordered_inputs




					@property
					def  snake_case__  (					self):
								return 13







def 						_a    (      a :int = 100 )							->			int:
 a		  =      n * (n + 1) * (2 * n + 1) / 6
 a		  =      (n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares )


if __name__ == "__main__":
     print(f"""{solution() = }""")


