





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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, TensorType

__A							:			Optional[int]         =			logging.get_logger(__name__)

__A							:			Any         =			{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'imagegpt'
     __magic_name__   =			['past_key_values']
     __magic_name__   =			{
         'hidden_size': 'n_embd',
         'max_position_embeddings': 'n_positions',
         'num_attention_heads': 'n_head',
         'num_hidden_layers': 'n_layer',
     }




     def __init__(    self				,     snake_case_=512 + 1				,     snake_case_=32 * 32				,     snake_case_=512				,     snake_case_=24				,     snake_case_=8				,     snake_case_=None				,     snake_case_="quick_gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=1E-5				,     snake_case_=0.02				,     snake_case_=True				,     snake_case_=True				,     snake_case_=False				,     snake_case_=False				,     snake_case_=False				,     **snake_case_				,     ):
         _A     =							vocab_size
         _A     =							n_positions
         _A     =							n_embd
         _A     =							n_layer
         _A     =							n_head
         _A     =							n_inner
         _A     =							activation_function
         _A     =							resid_pdrop
         _A     =							embd_pdrop
         _A     =							attn_pdrop
         _A     =							layer_norm_epsilon
         _A     =							initializer_range
         _A     =							scale_attn_weights
         _A     =							use_cache
         _A     =							scale_attn_by_inverse_layer_idx
         _A     =							reorder_and_upcast_attn
         _A     =							tie_word_embeddings

         super().__init__(tie_word_embeddings=snake_case_				,     **snake_case_							)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         return OrderedDict(
             [
                 ('input_ids', {0: 'batch', 1: 'sequence'}),
             ]							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = 1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = 3				,     snake_case_ = 32				,     snake_case_ = 32				,     ):
         _A     =							self._generate_dummy_images(snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							)
         _A     =							dict(preprocessor(images=snake_case_				,     return_tensors=snake_case_							)							)

         return inputs













from __future__ import annotations

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

snake_case__   :						Dict													=   TypeVar("""T""")


class     _a							(	Generic[T]     ):






  """simple docstring"""





  A_						       = 42  # Cache store of keys
  A_						       = 42  # References of the keys in cache
  A_						       = 10  # Maximum capacity of cache




  def __init__(  self		,		_UpperCAmelCase			)      ->			None:
     UpperCamelCase_						=  deque()
     UpperCamelCase_						=  set()
     if not n:
        UpperCamelCase_						=  sys.maxsize
     elif n < 0:
        raise ValueError('n should be an integer greater than 0.'			)
     else:
        UpperCamelCase_						=  n




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     if x not in self.key_reference:
        if len(self.dq_store			) == LRUCache._MAX_CAPACITY:
           UpperCamelCase_						=  self.dq_store.pop()
           self.key_reference.remove(_UpperCAmelCase			)
     else:
        self.dq_store.remove(_UpperCAmelCase			)

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




  def  _UpperCAmelCase  (  self			)      ->			None:
     for k in self.dq_store:
        print(_UpperCAmelCase			)




  def __repr__(  self			)      ->			str:
     return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store			)}"""


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 snake_case__   :						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]"





