





'''simple docstring'''


import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
       from ... import PreTrainedTokenizerBase, TensorType

_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				str								=       "vision-encoder-decoder"
 __lowerCamelCase    :				List[str]								=       True




 def __init__(						self      ,  **_lowerCAmelCase		)     ->				Union[str, Any]:
     super().__init__(**_lowerCAmelCase		)
     if "encoder" not in kwargs or "decoder" not in kwargs:
         raise ValueError(
             f'''A configuraton of type {self.model_type} cannot be instantiated because '''
             f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''		)

     _lowerCAmelCase						      =   kwargs.pop("encoder"		)
     _lowerCAmelCase						      =   encoder_config.pop("model_type"		)
     _lowerCAmelCase						      =   kwargs.pop("decoder"		)
     _lowerCAmelCase						      =   decoder_config.pop("model_type"		)

     _lowerCAmelCase						      =   AutoConfig.for_model(_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   AutoConfig.for_model(_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   True




 @classmethod
 def 				_snake_case (						cls      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				PretrainedConfig:
     logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
     _lowerCAmelCase						      =   True
     _lowerCAmelCase						      =   True

     return cls(encoder=encoder_config.to_dict()      ,  decoder=decoder_config.to_dict()      ,  **_lowerCAmelCase		)





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   copy.deepcopy(self.__dict__		)
     _lowerCAmelCase						      =   self.encoder.to_dict()
     _lowerCAmelCase						      =   self.decoder.to_dict()
     _lowerCAmelCase						      =   self.__class__.model_type
     return output







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				str								=       version.parse("1.11"		)




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]		)




 @property
 def 				_snake_case (						self		)     ->				float:
     return 1E-4





 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}		)







class  lowerCAmelCase_		(		__magic_name__		):




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase						      =   OrderedDict()
     _lowerCAmelCase						      =   {0: "batch", 1: "past_decoder_sequence + sequence"}
     _lowerCAmelCase						      =   {0: "batch", 1: "past_decoder_sequence + sequence"}
     _lowerCAmelCase						      =   {0: "batch", 1: "encoder_sequence"}

     return common_inputs





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     import torch

     _lowerCAmelCase						      =   OrderedDict()

     _lowerCAmelCase						      =   super().generate_dummy_inputs(
         _lowerCAmelCase      ,  batch_size=_lowerCAmelCase      ,  seq_length=_lowerCAmelCase      ,  is_pair=_lowerCAmelCase      ,  framework=_lowerCAmelCase		)

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   dummy_input["input_ids"].shape
     _lowerCAmelCase						      =   (batch, encoder_sequence, self._config.encoder_hidden_size)
     _lowerCAmelCase						      =   dummy_input.pop("input_ids"		)
     _lowerCAmelCase						      =   dummy_input.pop("attention_mask"		)
     _lowerCAmelCase						      =   torch.zeros(_lowerCAmelCase		)

     return common_inputs







class  lowerCAmelCase_		(		__magic_name__		):




 @property
 def 				_snake_case (						self		)     ->				None:
     pass




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				OnnxConfig:
     return VisionEncoderDecoderEncoderOnnxConfig(_lowerCAmelCase		)





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = "default"		)     ->				OnnxConfig:
     _lowerCAmelCase						      =   encoder_config.hidden_size
     return VisionEncoderDecoderDecoderOnnxConfig(_lowerCAmelCase      ,  _lowerCAmelCase		)









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[int]								=       "deit"




 def __init__(						self      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-12      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=16      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   encoder_stride







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       version.parse("1.11"		)




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]		)





 @property
 def 				_snake_case (						self		)     ->				float:
     return 1E-4


