




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

UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    """openai/imagegpt-small""": """""",
    """openai/imagegpt-medium""": """""",
    """openai/imagegpt-large""": """""",
}
class       a__   ( snake_case__				):
						_a							:   List[str] =   """imagegpt"""
						_a							:   List[str] =   ["""past_key_values"""]
						_a							:   int =   {
						    """hidden_size""": """n_embd""",
						    """max_position_embeddings""": """n_positions""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}






						def __init__(       self		,      _A=5_1_2 + 1		,      _A=3_2 * 3_2		,      _A=5_1_2		,      _A=2_4		,      _A=8		,      _A=None		,      _A="quick_gelu"		,      _A=0.1		,      _A=0.1		,      _A=0.1		,      _A=1E-5		,      _A=0.02		,      _A=True		,      _A=True		,      _A=False		,      _A=False		,      _A=False		,      **_A		,      ):




											"""simple docstring"""



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

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






class       a__   ( snake_case__				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = 1		,      _A = -1		,      _A = False		,      _A = None		,      _A = 3		,      _A = 3_2		,      _A = 3_2		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self._generate_dummy_images(_A		,      _A		,      _A		,      _A      )
											__lowerCAmelCase           =	dict(preprocessor(images=_A		,      return_tensors=_A      )      )

											return inputs










def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase       ,							__lowerCAmelCase           =	[], []
					while len(SCREAMING_SNAKE_CASE_					) > 1:
										__lowerCAmelCase       ,							__lowerCAmelCase           =	min(SCREAMING_SNAKE_CASE_					), max(SCREAMING_SNAKE_CASE_					)
										start.append(SCREAMING_SNAKE_CASE_					)
										end.append(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
					end.reverse()
					return start + collection + end


if __name__ == "__main__":
					UpperCamelCase__         =      input("""Enter numbers separated by a comma:\n""").strip()
					UpperCamelCase__         =      [int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")




