






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							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=    {
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}
class 				A_     (    SCREAMING_SNAKE_CASE__      ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      int			=       "imagegpt"
 _SCREAMING_SNAKE_CASE				:      str			=       ["past_key_values"]
 _SCREAMING_SNAKE_CASE				:      List[Any]			=       {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }


 def __init__(						self				,	_A=512 + 1				,	_A=32 * 32				,	_A=512				,	_A=24				,	_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				,	)			->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase					:   Any      	=					vocab_size
     _UpperCAmelCase					:   List[Any]      	=					n_positions
     _UpperCAmelCase					:   Optional[Any]      	=					n_embd
     _UpperCAmelCase					:   Dict      	=					n_layer
     _UpperCAmelCase					:   str      	=					n_head
     _UpperCAmelCase					:   Optional[Any]      	=					n_inner
     _UpperCAmelCase					:   Tuple      	=					activation_function
     _UpperCAmelCase					:   Union[str, Any]      	=					resid_pdrop
     _UpperCAmelCase					:   Optional[int]      	=					embd_pdrop
     _UpperCAmelCase					:   str      	=					attn_pdrop
     _UpperCAmelCase					:   Tuple      	=					layer_norm_epsilon
     _UpperCAmelCase					:   Any      	=					initializer_range
     _UpperCAmelCase					:   Dict      	=					scale_attn_weights
     _UpperCAmelCase					:   Union[str, Any]      	=					use_cache
     _UpperCAmelCase					:   Union[str, Any]      	=					scale_attn_by_inverse_layer_idx
     _UpperCAmelCase					:   List[str]      	=					reorder_and_upcast_attn
     _UpperCAmelCase					:   Tuple      	=					tie_word_embeddings

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



class 				A_     (    SCREAMING_SNAKE_CASE__      ):






 '''simple docstring'''




 @property
 def 			snake_case__					(						self)			->		Mapping[str, Mapping[int, str]]:

     """simple docstring"""



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


 def 			snake_case__					(						self				,	_A				,	_A = 1				,	_A = -1				,	_A = False				,	_A = None				,	_A = 3				,	_A = 32				,	_A = 32				,	)			->		Mapping[str, Any]:

     """simple docstring"""



     _UpperCAmelCase					:   Optional[Any]      	=					self._generate_dummy_images(A_				,	A_				,	A_				,	A_)
     _UpperCAmelCase					:   Union[str, Any]      	=					dict(preprocessor(images=A_				,	return_tensors=A_))

     return inputs






"""simple docstring"""




from collections.abc import Callable




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      float   			=  a
  _lowercase:      float   			=  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:
    _lowercase:      float   			=  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:
        _lowercase:      Union[str, Any]   			=  mid
      else:
        _lowercase:      Any   			=  mid
      _lowercase:      List[Any]   			=  start + (end - start) / 2.0
    return mid




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return x**3 - 2 * x - 5


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

  import doctest

  doctest.testmod()
