"""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

__A :     Optional[Any]				       =				logging.get_logger(__name__)

__A :     List[str]				       =				{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}






class 	__lowerCAmelCase					(      _UpperCamelCase):




    '''simple docstring'''






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





    def __init__(     self				:	Any					,				UpperCamelCase__				:	List[Any]=512 + 1					,				UpperCamelCase__				:	List[str]=32 * 32					,				UpperCamelCase__				:	List[str]=512					,				UpperCamelCase__				:	str=24					,				UpperCamelCase__				:	Optional[Any]=8					,				UpperCamelCase__				:	str=None					,				UpperCamelCase__				:	Union[str, Any]="quick_gelu"					,				UpperCamelCase__				:	int=0.1					,				UpperCamelCase__				:	str=0.1					,				UpperCamelCase__				:	Dict=0.1					,				UpperCamelCase__				:	Tuple=1E-5					,				UpperCamelCase__				:	Optional[Any]=0.02					,				UpperCamelCase__				:	List[str]=True					,				UpperCamelCase__				:	Optional[Any]=True					,				UpperCamelCase__				:	Optional[int]=False					,				UpperCamelCase__				:	Tuple=False					,				UpperCamelCase__				:	Optional[int]=False					,				**UpperCamelCase__				:	Any					,				):
          A__	:      Optional[int]      =vocab_size
          A__	:      List[Any]      =n_positions
          A__	:      Union[str, Any]      =n_embd
          A__	:      Optional[Any]      =n_layer
          A__	:      Optional[int]      =n_head
          A__	:      Union[str, Any]      =n_inner
          A__	:      Optional[Any]      =activation_function
          A__	:      Optional[int]      =resid_pdrop
          A__	:      Any      =embd_pdrop
          A__	:      Optional[Any]      =attn_pdrop
          A__	:      Optional[Any]      =layer_norm_epsilon
          A__	:      Optional[int]      =initializer_range
          A__	:      Any      =scale_attn_weights
          A__	:      Dict      =use_cache
          A__	:      Tuple      =scale_attn_by_inverse_layer_idx
          A__	:      Optional[Any]      =reorder_and_upcast_attn
          A__	:      Dict      =tie_word_embeddings

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






class 	__lowerCAmelCase					(      _UpperCamelCase):




    '''simple docstring'''






    @property
    def 					_UpperCAmelCase		(     self				:	Dict							):
          return OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "sequence"}),
              ]							)





    def 					_UpperCAmelCase		(     self				:	Dict					,				UpperCamelCase__				:	"FeatureExtractionMixin"					,				UpperCamelCase__				:	int = 1					,				UpperCamelCase__				:	int = -1					,				UpperCamelCase__				:	bool = False					,				UpperCamelCase__				:	Optional["TensorType"] = None					,				UpperCamelCase__				:	int = 3					,				UpperCamelCase__				:	int = 32					,				UpperCamelCase__				:	int = 32					,				):
          A__	:      str      =self._generate_dummy_images(__UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase							)
          A__	:      Optional[int]      =dict(preprocessor(images=__UpperCAmelCase					,				return_tensors=__UpperCAmelCase							)							)

          return inputs













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__							  =						{"processing_layoutxlm": ["LayoutXLMProcessor"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						["LayoutXLMTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						["LayoutXLMTokenizerFast"]

if TYPE_CHECKING:
     from .processing_layoutxlm import LayoutXLMProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm import LayoutXLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
     import sys

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


