
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_		:     List[str]        =     logging.get_logger(__name__)

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



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			"imagegpt"
   _UpperCAmelCase     	=			["past_key_values"]
   _UpperCAmelCase     	=			{
       "hidden_size": "n_embd",
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }

   def __init__(      self			:       Optional[int]							,   __lowerCAmelCase			:       List[str]=512 + 1							,   __lowerCAmelCase			:       Any=32 * 32							,   __lowerCAmelCase			:       Optional[Any]=512							,   __lowerCAmelCase			:       str=24							,   __lowerCAmelCase			:       Optional[Any]=8							,   __lowerCAmelCase			:       Dict=None							,   __lowerCAmelCase			:       str="quick_gelu"							,   __lowerCAmelCase			:       Optional[Any]=0.1							,   __lowerCAmelCase			:       Union[str, Any]=0.1							,   __lowerCAmelCase			:       List[Any]=0.1							,   __lowerCAmelCase			:       Any=1E-5							,   __lowerCAmelCase			:       Dict=0.0_2							,   __lowerCAmelCase			:       List[Any]=True							,   __lowerCAmelCase			:       str=True							,   __lowerCAmelCase			:       int=False							,   __lowerCAmelCase			:       int=False							,   __lowerCAmelCase			:       List[str]=False							,   **__lowerCAmelCase			:       List[str]							,   )       ->				Optional[Any]:


    """simple docstring"""






    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 		_lowercase		(       UpperCAmelCase__	):

   @property
   def 		A							(      self			:       int							)       ->				Optional[int]:


    """simple docstring"""






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




   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Optional[int] = 1							,   __lowerCAmelCase			:       str = -1							,   __lowerCAmelCase			:       Any = False							,   __lowerCAmelCase			:       int = None							,   __lowerCAmelCase			:       List[Any] = 3							,   __lowerCAmelCase			:       Optional[Any] = 32							,   __lowerCAmelCase			:       Union[str, Any] = 32							,   )       ->				List[Any]:


    """simple docstring"""






    a						=		self._generate_dummy_images(UpperCamelCase_							,   UpperCamelCase_							,   UpperCamelCase_							,   UpperCamelCase_							)
    a						=		dict(preprocessor(images=UpperCamelCase_							,   return_tensors=UpperCamelCase_							)							)

    return inputs






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_		:     Optional[int]        =     {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:     List[Any]        =     [
         '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''InstructBlipQFormerModel''',
         '''InstructBlipPreTrainedModel''',
         '''InstructBlipForConditionalGeneration''',
         '''InstructBlipVisionModel''',
     ]

if TYPE_CHECKING:
     from .configuration_instructblip import (
         INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         InstructBlipConfig,
         InstructBlipQFormerConfig,
         InstructBlipVisionConfig,
     )
     from .processing_instructblip import InstructBlipProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_instructblip import (
              INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              InstructBlipForConditionalGeneration,
              InstructBlipPreTrainedModel,
              InstructBlipQFormerModel,
              InstructBlipVisionModel,
          )

else:
     import sys

     A_		:     Tuple        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

