






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

a		:       Optional[Any]			     = logging.get_logger(__name__)

a		:       Union[str, Any]			     = {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Tuple							       =	'imagegpt'
      a  : Dict							       =	['past_key_values']
      a  : List[Any]							       =	{
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }
      def __init__(      self					:			int		,      __lowercase					:			Union[str, Any]=512 + 1		,      __lowercase					:			Optional[int]=32 * 32		,      __lowercase					:			Any=512		,      __lowercase					:			Dict=24		,      __lowercase					:			Optional[int]=8		,      __lowercase					:			List[str]=None		,      __lowercase					:			Tuple="quick_gelu"		,      __lowercase					:			Optional[Any]=0.1		,      __lowercase					:			Tuple=0.1		,      __lowercase					:			Optional[Any]=0.1		,      __lowercase					:			Optional[Any]=1e-5		,      __lowercase					:			Tuple=0.02		,      __lowercase					:			Tuple=True		,      __lowercase					:			List[str]=True		,      __lowercase					:			int=False		,      __lowercase					:			Dict=False		,      __lowercase					:			Optional[int]=False		,      **__lowercase					:			Optional[Any]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Any           =  n_positions
        __UpperCAmelCase							:	int           =  n_embd
        __UpperCAmelCase							:	Dict           =  n_layer
        __UpperCAmelCase							:	Dict           =  n_head
        __UpperCAmelCase							:	str           =  n_inner
        __UpperCAmelCase							:	Optional[Any]           =  activation_function
        __UpperCAmelCase							:	int           =  resid_pdrop
        __UpperCAmelCase							:	Tuple           =  embd_pdrop
        __UpperCAmelCase							:	Any           =  attn_pdrop
        __UpperCAmelCase							:	int           =  layer_norm_epsilon
        __UpperCAmelCase							:	List[str]           =  initializer_range
        __UpperCAmelCase							:	int           =  scale_attn_weights
        __UpperCAmelCase							:	List[str]           =  use_cache
        __UpperCAmelCase							:	int           =  scale_attn_by_inverse_layer_idx
        __UpperCAmelCase							:	int           =  reorder_and_upcast_attn
        __UpperCAmelCase							:	int           =  tie_word_embeddings

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


class     a    ( lowercase__   ):



      """simple docstring"""


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




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

        return inputs










from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a		:       Dict			     = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a		:       Any			     = typing.Union[np.floataa, int, float]  # noqa: UP007






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return np.sqrt(np.sum((np.asarray(__lowerCamelCase		) - np.asarray(__lowerCamelCase		)) ** 2		)		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return sum((va - va) ** 2 for va, va in zip(__lowerCamelCase  ,		__lowerCamelCase		)		) ** (1 / 2)


if __name__ == "__main__":






       def 				lowerCamelCase__      (    ):
         from timeit import timeit

         print("""Without Numpy"""		)
         print(
             timeit(
                 """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)
         print("""With Numpy"""		)
         print(
             timeit(
                 """euclidean_distance([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)







       benchmark()


