






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

lowerCamelCase			:					List[Any]         =	logging.get_logger(__name__)

lowerCamelCase			:					int         =	{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""imagegpt"""
      snake_case_  =							["""past_key_values"""]
      snake_case_  =							{
          """hidden_size""": """n_embd""",
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }


      def __init__(	self					,						__UpperCamelCase=5_1_2 + 1					,						__UpperCamelCase=3_2 * 3_2					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=2_4					,						__UpperCamelCase=8					,						__UpperCamelCase=None					,						__UpperCamelCase="quick_gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=1e-5					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=False					,						__UpperCamelCase=False					,						__UpperCamelCase=False					,						**__UpperCamelCase					,						)->	Optional[int]:
           __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 							_UpperCamelCase			(a_			):


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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = 1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						__UpperCamelCase = 3					,						__UpperCamelCase = 3_2					,						__UpperCamelCase = 3_2					,						)->	Mapping[str, Any]:
           __lowerCAmelCase								=					self._generate_dummy_images(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)
           __lowerCAmelCase								=					dict(preprocessor(images=__UpperCamelCase					,						return_tensors=__UpperCamelCase	)	)

           return inputs














lowerCamelCase			:					str         =	'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase			:					Tuple         =	[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase			:					Dict         =	{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






