


'''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

UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}
class 						a__					(					__a   ):





			'''simple docstring'''

			A						:				List[str]					      =			'''imagegpt'''
			A						:				Optional[Any]					      =			['''past_key_values''']
			A						:				Tuple					      =			{
			    '''hidden_size''': '''n_embd''',
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}
			def __init__(  self							:			Dict			,    lowerCAmelCase_							:			List[str]=512 + 1			,    lowerCAmelCase_							:			List[str]=32 * 32			,    lowerCAmelCase_							:			Any=512			,    lowerCAmelCase_							:			int=24			,    lowerCAmelCase_							:			str=8			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			List[Any]="quick_gelu"			,    lowerCAmelCase_							:			List[Any]=0.1			,    lowerCAmelCase_							:			List[Any]=0.1			,    lowerCAmelCase_							:			Any=0.1			,    lowerCAmelCase_							:			Optional[Any]=1E-5			,    lowerCAmelCase_							:			List[Any]=0.02			,    lowerCAmelCase_							:			Any=True			,    lowerCAmelCase_							:			int=True			,    lowerCAmelCase_							:			Optional[int]=False			,    lowerCAmelCase_							:			Dict=False			,    lowerCAmelCase_							:			List[str]=False			,    **lowerCAmelCase_							:			Dict			,    )      ->		Tuple:
						__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__					(					__a   ):





			'''simple docstring'''

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

			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			List[str] = 1			,    lowerCAmelCase_							:			str = -1			,    lowerCAmelCase_							:			List[str] = False			,    lowerCAmelCase_							:			Optional[int] = None			,    lowerCAmelCase_							:			int = 3			,    lowerCAmelCase_							:			Union[str, Any] = 32			,    lowerCAmelCase_							:			Dict = 32			,    )      ->		Mapping[str, Any]:
						__A=	self._generate_dummy_images(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase	)
						__A=	dict(preprocessor(images=_UpperCamelCase			,    return_tensors=_UpperCamelCase	)	)

						return inputs





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       _lowerCAmelCase   (      __a			):
				_lowercase     						='''imagegpt'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(   self							,							_UpperCamelCase=512 + 1							,							_UpperCamelCase=32 * 32							,							_UpperCamelCase=512							,							_UpperCamelCase=24							,							_UpperCamelCase=8							,							_UpperCamelCase=None							,							_UpperCamelCase="quick_gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> Tuple:
							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=_UpperCamelCase							,							**_UpperCamelCase	)





class       _lowerCAmelCase   (      __a			):




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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = 1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							_UpperCamelCase = 3							,							_UpperCamelCase = 32							,							_UpperCamelCase = 32							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      self._generate_dummy_images(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      dict(preprocessor(images=_UpperCamelCase							,							return_tensors=_UpperCamelCase	)	)

							return inputs



