
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

_lowerCamelCase	:    List[Any]          =      logging.get_logger(__name__)

_lowerCamelCase	:    Optional[Any]          =      {
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Union[str, Any]          = "imagegpt"
	_UpperCAmelCase	:		List[str]          = ["past_key_values"]
	_UpperCAmelCase	:		Optional[int]          = {
	    "hidden_size": "n_embd",
	    "max_position_embeddings": "n_positions",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}



	def __init__(  self     :   Tuple      ,  lowercase     :   Optional[int]=512 + 1      ,  lowercase     :   Optional[Any]=32 * 32      ,  lowercase     :   Union[str, Any]=512      ,  lowercase     :   Dict=24      ,  lowercase     :   int=8      ,  lowercase     :   List[str]=None      ,  lowercase     :   str="quick_gelu"      ,  lowercase     :   int=0.1      ,  lowercase     :   Any=0.1      ,  lowercase     :   List[Any]=0.1      ,  lowercase     :   Optional[int]=1E-5      ,  lowercase     :   Dict=0.02      ,  lowercase     :   Dict=True      ,  lowercase     :   Optional[int]=True      ,  lowercase     :   Tuple=False      ,  lowercase     :   Optional[Any]=False      ,  lowercase     :   Tuple=False      ,  **lowercase     :   Union[str, Any]      ,  ):


				'''simple docstring'''


				_snake_case          =      vocab_size
				_snake_case          =      n_positions
				_snake_case          =      n_embd
				_snake_case          =      n_layer
				_snake_case          =      n_head
				_snake_case          =      n_inner
				_snake_case          =      activation_function
				_snake_case          =      resid_pdrop
				_snake_case          =      embd_pdrop
				_snake_case          =      attn_pdrop
				_snake_case          =      layer_norm_epsilon
				_snake_case          =      initializer_range
				_snake_case          =      scale_attn_weights
				_snake_case          =      use_cache
				_snake_case          =      scale_attn_by_inverse_layer_idx
				_snake_case          =      reorder_and_upcast_attn
				_snake_case          =      tie_word_embeddings

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




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	@property
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				return OrderedDict(
				    [
				        ('input_ids', {0: 'batch', 1: 'sequence'}),
				    ]     )





	def 			A							(  self     :   List[str]      ,  lowercase     :   "FeatureExtractionMixin"      ,  lowercase     :   int = 1      ,  lowercase     :   int = -1      ,  lowercase     :   bool = False      ,  lowercase     :   Optional["TensorType"] = None      ,  lowercase     :   int = 3      ,  lowercase     :   int = 32      ,  lowercase     :   int = 32      ,  ):


				'''simple docstring'''


				_snake_case          =      self._generate_dummy_images(lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      dict(preprocessor(images=lowercase      ,  return_tensors=lowercase     )     )

				return inputs

import random




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	@staticmethod
	def 			A							(  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      [ord(lowercase     ) for i in text]
				_snake_case          =      []
				_snake_case          =      []
				for i in plain:
							_snake_case          =      random.randint(1      ,  300     )
							_snake_case          =      (i + k) * k
							cipher.append(lowercase     )
							key.append(lowercase     )
				return cipher, key





	@staticmethod
	def 			A							(  lowercase     :   list[int]      ,  lowercase     :   list[int]     ):


				'''simple docstring'''


				_snake_case          =      []
				for i in range(len(lowercase     )     ):
							_snake_case          =      int((cipher[i] - (key[i]) ** 2) / key[i]     )
							plain.append(chr(lowercase     )     )
				return "".join(lowercase     )


if __name__ == "__main__":
			_lowerCamelCase   ,					_lowerCamelCase	:    Optional[int]          =      Onepad().encrypt('''Hello''')
			print(c, k)
			print(Onepad().decrypt(c, k))