
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:    Tuple 							=			logging.get_logger(__name__)

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

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     'imagegpt'
	SCREAMING_SNAKE_CASE__    				=     ['past_key_values']
	SCREAMING_SNAKE_CASE__    				=     {
	    'hidden_size': 'n_embd',
	    'max_position_embeddings': 'n_positions',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}




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


		'''simple docstring'''







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

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


class 						__UpperCamelCase    (					lowercase						):




	@property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







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




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


		'''simple docstring'''







		UpperCAmelCase_       				=			self._generate_dummy_images(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			dict(preprocessor(images=lowerCAmelCase		,	return_tensors=lowerCAmelCase							)							)

		return inputs


def       __lowerCAmelCase      (						A    ):
	if len(A    ) <= 1:
		return lst

	UpperCAmelCase_       				=			1

	while i < len(A    ):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			UpperCAmelCase_				,   UpperCAmelCase_       				=			lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				UpperCAmelCase_       				=			1

	return lst


if __name__ == "__main__":
				_a:    List[Any] 							=			input("""Enter numbers separated by a comma:\n""").strip()
				_a:    Optional[int] 							=			[int(item) for item in user_input.split(""",""")]
				print(gnome_sort(unsorted))