





'''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   : Optional[Any]             =			logging.get_logger(__name__)

A   : 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 			lowerCamelCase					(						lowercase_  ):
     _SCREAMING_SNAKE_CASE							   =    '''gpt_neo'''
     _SCREAMING_SNAKE_CASE							   =    ['''past_key_values''']
     _SCREAMING_SNAKE_CASE							   =    {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


     def __init__(					self	:  List[str]       ,     __snake_case	:  List[str]=5_02_57       ,     __snake_case	:  List[Any]=20_48       ,     __snake_case	:  Union[str, Any]=20_48       ,     __snake_case	:  int=24       ,     __snake_case	:  Tuple=[[["global", "local"], 12]]       ,     __snake_case	:  Optional[Any]=16       ,     __snake_case	:  Union[str, Any]=None       ,     __snake_case	:  Optional[int]=2_56       ,     __snake_case	:  List[Any]="gelu_new"       ,     __snake_case	:  Optional[Any]=0.0       ,     __snake_case	:  List[str]=0.0       ,     __snake_case	:  Any=0.0       ,     __snake_case	:  Union[str, Any]=0.1       ,     __snake_case	:  Optional[Any]=1e-5       ,     __snake_case	:  Dict=0.02       ,     __snake_case	:  List[str]=True       ,     __snake_case	:  Dict=5_02_56       ,     __snake_case	:  List[Any]=5_02_56       ,     **__snake_case	:  Optional[Any]       ,     ):





           '''simple docstring'''





           _snake_case:   Optional[Any]  					=		vocab_size
           _snake_case:   Optional[int]  					=		max_position_embeddings
           _snake_case:   List[Any]  					=		hidden_size
           _snake_case:   Tuple  					=		num_layers
           _snake_case:   Optional[int]  					=		num_heads
           _snake_case:   Tuple  					=		intermediate_size
           _snake_case:   Any  					=		window_size
           _snake_case:   Any  					=		activation_function
           _snake_case:   Dict  					=		resid_dropout
           _snake_case:   Dict  					=		embed_dropout
           _snake_case:   Any  					=		attention_dropout
           _snake_case:   Dict  					=		classifier_dropout
           _snake_case:   Tuple  					=		layer_norm_epsilon
           _snake_case:   Optional[Any]  					=		initializer_range
           _snake_case:   List[Any]  					=		use_cache

           _snake_case:   List[str]  					=		bos_token_id
           _snake_case:   Optional[Any]  					=		eos_token_id

           _snake_case:   List[Any]  					=		attention_types
           _snake_case:   Dict  					=		self.expand_attention_types_params(__snake_case       )

           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=__snake_case       ,     eos_token_id=__snake_case       ,     **__snake_case       )




     @staticmethod
     def    SCREAMING_SNAKE_CASE_		(					__snake_case	:  List[Any]       ):





           '''simple docstring'''





           _snake_case:   Optional[int]  					=		[]
           for item in attention_types:
                 for _ in range(item[1]       ):
                       attentions.extend(item[0]       )
           return attentions





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->Dict:
      import torch

      _snake_case:   List[Any]  					=		input.size()
      _snake_case:   List[Any]  					=		len(UpperCamelCase__						)
      _snake_case:   str  					=		shape[dimension]

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

      _snake_case:   Tuple  					=		[slice(UpperCamelCase__						)] * rank
      _snake_case:   List[Any]  					=		indices
      _snake_case:   str  					=		input[s]

      _snake_case:   Optional[int]  					=		list(range(0				,  rank + 1						)						)
      perm.append(perm.pop(dimension + 1						)						)

      return sliced.permute(UpperCamelCase__						)





def       lowercase_      (		lowercase__				,  lowercase__						)   ->Optional[Any]:
      import torch

      _snake_case:   int  					=		torch.arange(1				,  UpperCamelCase__						)
      _snake_case:   Union[str, Any]  					=		torch.remainder(UpperCamelCase__				,  UpperCamelCase__						)
      _snake_case:   Any  					=		remainders == 0
      _snake_case:   Tuple  					=		candidates[divisor_indices]
      _snake_case:   Optional[Any]  					=		torch.max(UpperCamelCase__						)
      return largest_divisor, torch.div(UpperCamelCase__				,  UpperCamelCase__				,  rounding_mode='floor'						)







class 			lowerCamelCase					(						lowercase_  ):


     @property
     def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





           '''simple docstring'''





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

           return common_inputs


     @property
     def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





           '''simple docstring'''





           return self._config.num_heads


     def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Union[str, Any] = -1       ,     __snake_case	:  Dict = -1       ,     __snake_case	:  List[str] = False       ,     __snake_case	:  List[str] = None       ,     ):





           '''simple docstring'''





           _snake_case:   int  					=		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()
           _snake_case:   List[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

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

           _snake_case:   int  					=		common_inputs['attention_mask']
           if self.use_past:
                 _snake_case:   Dict  					=		ordered_inputs['attention_mask'].dtype
                 _snake_case:   Optional[int]  					=		torch.cat(
                     [ordered_inputs['attention_mask'], torch.ones(__snake_case       ,     __snake_case       ,     dtype=__snake_case       )]       ,     dim=1       )

           return ordered_inputs




     @property
     def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





           '''simple docstring'''





           return 13












'''simple docstring'''






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A   : str             =			TypeVar('T')







class 			lowerCamelCase					(						Generic[T]  ):
					_SCREAMING_SNAKE_CASE							   =    42  # Cache store of keys
					_SCREAMING_SNAKE_CASE							   =    42  # References of the keys in cache
					_SCREAMING_SNAKE_CASE							   =    10  # Maximum capacity of cache


					def __init__(					self	:  List[Any]       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		deque()
											_snake_case:   Union[str, Any]  					=		set()
											if not n:
																	_snake_case:   Optional[int]  					=		sys.maxsize
											elif n < 0:
																	raise ValueError('n should be an integer greater than 0.'       )
											else:
																	_snake_case:   Tuple  					=		n


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  T       ):





											'''simple docstring'''





											if x not in self.key_reference:
																	if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																							_snake_case:   int  					=		self.dq_store.pop()
																							self.key_reference.remove(__snake_case       )
											else:
																	self.dq_store.remove(__snake_case       )

											self.dq_store.appendleft(__snake_case       )
											self.key_reference.add(__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											for k in self.dq_store:
																	print(__snake_case       )




					def __repr__(					self	:  List[Any]       ):





											'''simple docstring'''





											return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}'''


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A   : LRUCache[str | int]             =			LRUCache(4)
		lru_cache.refer('A')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('A')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





