





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_												=     logging.get_logger(__name__)

A_												=     {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""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	,  _lowerCAmelCase=512 + 1	,  _lowerCAmelCase=32 * 32	,  _lowerCAmelCase=512	,  _lowerCAmelCase=24	,  _lowerCAmelCase=8	,  _lowerCAmelCase=None	,  _lowerCAmelCase="quick_gelu"	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=1E-5	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=True	,  _lowerCAmelCase=True	,  _lowerCAmelCase=False	,  _lowerCAmelCase=False	,  _lowerCAmelCase=False	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       n_positions
										lowerCamelCase__       =       n_embd
										lowerCamelCase__       =       n_layer
										lowerCamelCase__       =       n_head
										lowerCamelCase__       =       n_inner
										lowerCamelCase__       =       activation_function
										lowerCamelCase__       =       resid_pdrop
										lowerCamelCase__       =       embd_pdrop
										lowerCamelCase__       =       attn_pdrop
										lowerCamelCase__       =       layer_norm_epsilon
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       scale_attn_weights
										lowerCamelCase__       =       use_cache
										lowerCamelCase__       =       scale_attn_by_inverse_layer_idx
										lowerCamelCase__       =       reorder_and_upcast_attn
										lowerCamelCase__       =       tie_word_embeddings

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

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@property
					def 					__magic_name__ ( self	):
										return OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "sequence"}),
										    ]	)






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = 1	,  _lowerCAmelCase = -1	,  _lowerCAmelCase = False	,  _lowerCAmelCase = None	,  _lowerCAmelCase = 3	,  _lowerCAmelCase = 32	,  _lowerCAmelCase = 32	,  ):
										lowerCamelCase__       =       self._generate_dummy_images(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       dict(preprocessor(images=_lowerCAmelCase	,  return_tensors=_lowerCAmelCase	)	)

										return inputs








def    __UpperCamelCase      (    a,     a,     a=False) ->Dict:

					if isinstance(a,     a) and isinstance(a,     a):
										lowerCamelCase__       =       len(set_a.intersection(a))

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
										else:
															lowerCamelCase__       =       len(set_a.union(a))

										return intersection / union

					if isinstance(a,     (list, tuple)) and isinstance(a,     (list, tuple)):
										lowerCamelCase__       =       [element for element in set_a if element in set_b]

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
															return len(a) / union
										else:
															lowerCamelCase__       =       set_a + [element for element in set_b if element not in set_a]
															return len(a) / len(a)

										return len(a) / len(a)
					return None


if __name__ == "__main__":
						A_												=     {"a", "b", "c", "d", "e"}
						A_												=     {"c", "d", "e", "f", "h", "i"}
						print(jaccard_similarity(set_a, set_b))

