

"""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"""



from collections.abc import Callable





def  _snake_case     (    UpperCAmelCase_  :					Callable[[float], float]					,   UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):
     A__										=  a
     A__										=  b
     if function(UpperCAmelCase_			) == 0:  # one of the a or b is a root for the function
          return a
     elif function(UpperCAmelCase_			) == 0:
          return b
     elif (
         function(UpperCAmelCase_			) * function(UpperCAmelCase_			) > 0
     ):  # if none of these are root and they are both positive or negative,
          # then this algorithm can't find the root
          raise ValueError("""could not find root in given interval."""			)
     else:
          A__										=  start + (end - start) / 2.0
          while abs(start - mid			) > 10**-7:  # until precisely equals to 10^-7
               if function(UpperCAmelCase_			) == 0:
                    return mid
               elif function(UpperCAmelCase_			) * function(UpperCAmelCase_			) < 0:
                    A__										=  mid
               else:
                    A__										=  mid
               A__										=  start + (end - start) / 2.0
          return mid





def  _snake_case     (    UpperCAmelCase_  :					float			):
     return x**3 - 2 * x - 5


if __name__ == "__main__":
 print(bisection(f, 1, 1_0_0_0))

 import doctest

 doctest.testmod()






