



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


snake_case_			:     int  	=  logging.get_logger(__name__)

snake_case_			:     Dict  	=  {
    '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 							lowercase__     (							lowercase       ):
	lowercase__      =      """gpt_neo"""
	lowercase__      =      ["""past_key_values"""]
	lowercase__      =      {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





	def __init__(			self :    Dict			,lowerCamelCase__ :    Optional[Any]=50257			,lowerCamelCase__ :    int=2048			,lowerCamelCase__ :    str=2048			,lowerCamelCase__ :    List[str]=24			,lowerCamelCase__ :    List[str]=[[["global", "local"], 12]]			,lowerCamelCase__ :    List[Any]=16			,lowerCamelCase__ :    str=None			,lowerCamelCase__ :    Tuple=256			,lowerCamelCase__ :    Union[str, Any]="gelu_new"			,lowerCamelCase__ :    Optional[Any]=0.0			,lowerCamelCase__ :    Dict=0.0			,lowerCamelCase__ :    Any=0.0			,lowerCamelCase__ :    str=0.1			,lowerCamelCase__ :    Dict=1E-5			,lowerCamelCase__ :    int=0.0_2			,lowerCamelCase__ :    List[Any]=True			,lowerCamelCase__ :    Optional[Any]=50256			,lowerCamelCase__ :    Optional[Any]=50256			,**lowerCamelCase__ :    Dict			,):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       vocab_size
							_UpperCamelCase :		Optional[int]			    =       max_position_embeddings
							_UpperCamelCase :		Tuple			    =       hidden_size
							_UpperCamelCase :		Any			    =       num_layers
							_UpperCamelCase :		Union[str, Any]			    =       num_heads
							_UpperCamelCase :		Optional[int]			    =       intermediate_size
							_UpperCamelCase :		int			    =       window_size
							_UpperCamelCase :		int			    =       activation_function
							_UpperCamelCase :		List[str]			    =       resid_dropout
							_UpperCamelCase :		Union[str, Any]			    =       embed_dropout
							_UpperCamelCase :		Optional[int]			    =       attention_dropout
							_UpperCamelCase :		Union[str, Any]			    =       classifier_dropout
							_UpperCamelCase :		Optional[int]			    =       layer_norm_epsilon
							_UpperCamelCase :		Tuple			    =       initializer_range
							_UpperCamelCase :		Optional[int]			    =       use_cache

							_UpperCamelCase :		Optional[Any]			    =       bos_token_id
							_UpperCamelCase :		int			    =       eos_token_id

							_UpperCamelCase :		int			    =       attention_types
							_UpperCamelCase :		Tuple			    =       self.expand_attention_types_params(lowerCamelCase__			)

							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=lowerCamelCase__			,eos_token_id=lowerCamelCase__			,**lowerCamelCase__			)





	@staticmethod
	def      UpperCamelCase_  (			lowerCamelCase__ :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       []
							for item in attention_types:
													for _ in range(item[1]			):
																			attentions.extend(item[0]			)
							return attentions






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						import torch

						_UpperCamelCase :		Tuple			    =       input.size()
						_UpperCamelCase :		Optional[Any]			    =       len(UpperCAmelCase_							)
						_UpperCamelCase :		Tuple			    =       shape[dimension]

						_UpperCamelCase :		Optional[int]			    =       torch.arange(0  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						_UpperCamelCase :		Optional[Any]			    =       torch.div(sizedim - size  ,  UpperCAmelCase_  ,  rounding_mode='floor'							) + 1
						_UpperCamelCase :		List[str]			    =       torch.arange(UpperCAmelCase_							) + low_indices[:min_length][:, None]

						_UpperCamelCase :		Optional[Any]			    =       [slice(UpperCAmelCase_							)] * rank
						_UpperCamelCase :		Tuple			    =       indices
						_UpperCamelCase :		int			    =       input[s]

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

						return sliced.permute(UpperCAmelCase_							)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						import torch

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






class 							lowercase__     (							lowercase       ):





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







							'''simple docstring'''
							_UpperCamelCase :		str			    =       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
							if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__			,direction='inputs'			)
													_UpperCamelCase :		List[Any]			    =       {0: 'batch', 1: 'past_sequence + sequence'}
							else:
													_UpperCamelCase :		Optional[Any]			    =       {0: 'batch', 1: 'sequence'}

							return common_inputs





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







							'''simple docstring'''
							return self._config.num_heads





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    PreTrainedTokenizer			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    bool = False			,lowerCamelCase__ :    Optional[TensorType] = None			,):







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

							_UpperCamelCase :		List[Any]			    =       common_inputs['attention_mask']
							if self.use_past:
													_UpperCamelCase :		Dict			    =       ordered_inputs['attention_mask'].dtype
													_UpperCamelCase :		Union[str, Any]			    =       torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__			,lowerCamelCase__			,dtype=lowerCamelCase__			)]			,dim=1			)

							return ordered_inputs





	@property
	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							return 13







'''simple docstring'''
from __future__ import annotations






def A__   (					UpperCAmelCase_							):
						if not nums:
												return 0
						_UpperCamelCase :		Any			    =       nums[0]
						_UpperCamelCase :		Optional[int]			    =       0
						for num in nums[1:]:
												_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       (
												    max_excluding + num,
												    max(UpperCAmelCase_  ,  UpperCAmelCase_							),
												)
						return max(UpperCAmelCase_  ,  UpperCAmelCase_							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


