






"""simple docstring"""
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         =							logging.get_logger(__name__)

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



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				str    =				"""imagegpt"""
	_A       :				Dict    =				["""past_key_values"""]
	_A       :				Dict    =				{
	    """hidden_size""": """n_embd""",
	    """max_position_embeddings""": """n_positions""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(self     ,     lowerCAmelCase_=512 + 1     ,     lowerCAmelCase_=32 * 32     ,     lowerCAmelCase_=512     ,     lowerCAmelCase_=24     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_="quick_gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=1e-5     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False     ,     **lowerCAmelCase_     ,     ):
		A_		:						Optional[int]	    =   vocab_size
		A_		:						str	    =   n_positions
		A_		:						int	    =   n_embd
		A_		:						Any	    =   n_layer
		A_		:						Union[str, Any]	    =   n_head
		A_		:						Optional[Any]	    =   n_inner
		A_		:						List[Any]	    =   activation_function
		A_		:						Optional[Any]	    =   resid_pdrop
		A_		:						str	    =   embd_pdrop
		A_		:						Optional[int]	    =   attn_pdrop
		A_		:						Optional[int]	    =   layer_norm_epsilon
		A_		:						List[Any]	    =   initializer_range
		A_		:						Tuple	    =   scale_attn_weights
		A_		:						Optional[Any]	    =   use_cache
		A_		:						Union[str, Any]	    =   scale_attn_by_inverse_layer_idx
		A_		:						Union[str, Any]	    =   reorder_and_upcast_attn
		A_		:						List[Any]	    =   tie_word_embeddings

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






class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




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





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = 1     ,     lowerCAmelCase_ = -1     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 3     ,     lowerCAmelCase_ = 32     ,     lowerCAmelCase_ = 32     ,     ):
		A_		:						Dict	    =   self._generate_dummy_images(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						List[Any]	    =   dict(preprocessor(images=lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_							)							)

		return inputs









"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   prime_factors(snake_case__					)
	if is_square_free(snake_case__					):
		return -1 if len(snake_case__					) % 2 else 1
	return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()

