

'''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

lowerCamelCase			:				Optional[int] 			=					logging.get_logger(__name__)

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







class 							A__   (	A__  ):
  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				:     str							, _a				:     Any=512 + 1							, _a				:     int=32 * 32							, _a				:     str=512							, _a				:     Optional[Any]=24							, _a				:     Optional[int]=8							, _a				:     Union[str, Any]=None							, _a				:     List[str]="quick_gelu"							, _a				:     List[Any]=0.1							, _a				:     Any=0.1							, _a				:     Dict=0.1							, _a				:     Tuple=1e-5							, _a				:     Tuple=0.02							, _a				:     List[Any]=True							, _a				:     Any=True							, _a				:     Any=False							, _a				:     Tuple=False							, _a				:     str=False							, **_a				:     List[str]							, )     ->   List[Any]:





       '''simple docstring'''






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

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







class 							A__   (	A__  ):





  @property
  def 						A  (		self				:     Optional[Any]			)     ->   Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






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





  def 						A  (		self				:     Any							, _a				:     "FeatureExtractionMixin"							, _a				:     int = 1							, _a				:     int = -1							, _a				:     bool = False							, _a				:     Optional["TensorType"] = None							, _a				:     int = 3							, _a				:     int = 32							, _a				:     int = 32							, )     ->   Mapping[str, Any]:





       '''simple docstring'''






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

       return inputs



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase			:				List[str] 			=					{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Union[str, Any] 			=					[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				str 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
