

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

_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    logging.get_logger(__name__)

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






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


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






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

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






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  @property
  def    lowercase_	(							self		:	int      )	->							Mapping[str, Mapping[int, str]]:
       return OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
           ]      )







  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	"FeatureExtractionMixin"			,     __lowerCamelCase		:	int = 1			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	Optional["TensorType"] = None			,     __lowerCamelCase		:	int = 3			,     __lowerCamelCase		:	int = 32			,     __lowerCamelCase		:	int = 32			,     )	->							Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__					=					self._generate_dummy_images(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					dict(preprocessor(images=__lowerCamelCase			,     return_tensors=__lowerCamelCase      )      )

       return inputs





import random
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					a[left_index]
     SCREAMING_SNAKE_CASE__					=					left_index + 1
     for j in range(left_index + 1   ,	_A       ):
          if a[j] < pivot:
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i], a[j]
               i += 1
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i - 1], a[left_index]
     return i - 1
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     if left < right:
          SCREAMING_SNAKE_CASE__					=					random.randint(_A   ,	right - 1       )
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					(
              a[left],
              a[pivot],
          )  # switches the pivot with the left most bound
          SCREAMING_SNAKE_CASE__					=					partition(_A   ,	_A   ,	_A       )
          quick_sort_random(
              _A   ,	_A   ,	_A       )  # recursive quicksort to the left of the pivot point
          quick_sort_random(
              _A   ,	pivot_index + 1   ,	_A       )  # recursive quicksort to the right of the pivot point
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					input('''Enter numbers separated by a comma:\n'''       ).strip()
     SCREAMING_SNAKE_CASE__					=					[int(_A       ) for item in user_input.split(''','''       )]

     quick_sort_random(_A   ,	0   ,	len(_A       )       )

     print(_A       )


if __name__ == "__main__":
       main()


