



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

UpperCamelCase__ =						logging.get_logger(__name__)

UpperCamelCase__ =						{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}
class 			_lowerCAmelCase   ( __SCREAMING_SNAKE_CASE			):






			"""simple docstring"""





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





			def __init__(				self	:       str			,     lowercase	:       int=512 + 1			,     lowercase	:       List[str]=32 * 32			,     lowercase	:       int=512			,     lowercase	:       Optional[int]=24			,     lowercase	:       List[str]=8			,     lowercase	:       Dict=None			,     lowercase	:       Optional[Any]="quick_gelu"			,     lowercase	:       str=0.1			,     lowercase	:       Optional[Any]=0.1			,     lowercase	:       List[str]=0.1			,     lowercase	:       str=1E-5			,     lowercase	:       Union[str, Any]=0.02			,     lowercase	:       int=True			,     lowercase	:       Optional[int]=True			,     lowercase	:       Optional[Any]=False			,     lowercase	:       List[Any]=False			,     lowercase	:       str=False			,     **lowercase	:       Tuple			,     )						->					str:
									"""simple docstring"""







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

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





class 			_lowerCAmelCase   ( __SCREAMING_SNAKE_CASE			):






			"""simple docstring"""





			@property
			def 		snake_case__			(				self	:       List[str]   )						->					str:
									"""simple docstring"""







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







			def 		snake_case__			(				self	:       int			,     lowercase	:       "FeatureExtractionMixin"			,     lowercase	:       int = 1			,     lowercase	:       int = -1			,     lowercase	:       bool = False			,     lowercase	:       Optional["TensorType"] = None			,     lowercase	:       int = 3			,     lowercase	:       int = 32			,     lowercase	:       int = 32			,     )						->					Any:
									"""simple docstring"""







									__lowercase					=				self._generate_dummy_images(_a			,     _a			,     _a			,     _a   )
									__lowercase					=				dict(preprocessor(images=_a			,     return_tensors=_a   )   )

									return inputs










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ =						{
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "LukeForEntityClassification",
			    "LukeForEntityPairClassification",
			    "LukeForEntitySpanClassification",
			    "LukeForMultipleChoice",
			    "LukeForQuestionAnswering",
			    "LukeForSequenceClassification",
			    "LukeForTokenClassification",
			    "LukeForMaskedLM",
			    "LukeModel",
			    "LukePreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			UpperCamelCase__ =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





