

"""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

SCREAMING_SNAKE_CASE_					:	int							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Any							  =						{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



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

    def __init__(     self: Tuple    ,						UpperCamelCase: Any=5_12 + 1    ,						UpperCamelCase: str=32 * 32    ,						UpperCamelCase: Optional[Any]=5_12    ,						UpperCamelCase: str=24    ,						UpperCamelCase: List[str]=8    ,						UpperCamelCase: str=None    ,						UpperCamelCase: Dict="quick_gelu"    ,						UpperCamelCase: Optional[Any]=0.1    ,						UpperCamelCase: Optional[Any]=0.1    ,						UpperCamelCase: Optional[Any]=0.1    ,						UpperCamelCase: Union[str, Any]=1e-5    ,						UpperCamelCase: Dict=0.02    ,						UpperCamelCase: Optional[Any]=True    ,						UpperCamelCase: Any=True    ,						UpperCamelCase: Tuple=False    ,						UpperCamelCase: Any=False    ,						UpperCamelCase: Dict=False    ,						**UpperCamelCase: List[Any]    ,						):






         """simple docstring"""


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

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







class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


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





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






         """simple docstring"""


         A__										=  self._generate_dummy_images(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )
         A__										=  dict(preprocessor(images=UpperCamelCase    ,						return_tensors=UpperCamelCase       )       )

         return inputs









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					Optional[Any]			):
     A__										=  [0 for i in range(r + 1			)]
     # nc0 = 1
     A__										=  1
     for i in range(1					,   n + 1			):
          # to compute current row from previous row.
          A__										=  min(UpperCAmelCase_					,   UpperCAmelCase_			)
          while j > 0:
               c[j] += c[j - 1]
               j -= 1
     return c[r]

print(binomial_coefficient(n=1_0, r=5))






