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			:						Any				  =		logging.get_logger(__name__)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """vision-encoder-decoder"""
  _SCREAMING_SNAKE_CASE     					=      True

  def __init__(		self			:   Tuple  ,				**__SCREAMING_SNAKE_CASE			:   Any							):
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   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}"""							)

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

   __a							=	AutoConfig.for_model(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
   __a							=	AutoConfig.for_model(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
   __a							=	True

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   PretrainedConfig  ,				__SCREAMING_SNAKE_CASE			:   PretrainedConfig  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"							)
   __a							=	True
   __a							=	True

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

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	copy.deepcopy(self.__dict__							)
   __a							=	self.encoder.to_dict()
   __a							=	self.decoder.to_dict()
   __a							=	self.__class__.model_type
   return output
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      version.parse("""1.11"""			)

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

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   return 1E-4

  @property
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}							)
class  A_			(							a_			):

  @property
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	OrderedDict()
   __a							=	{0: "batch", 1: "past_decoder_sequence + sequence"}
   __a							=	{0: "batch", 1: "past_decoder_sequence + sequence"}
   __a							=	{0: "batch", 1: "encoder_sequence"}

   return common_inputs

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   "PreTrainedTokenizerBase"  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				__SCREAMING_SNAKE_CASE			:   Optional["TensorType"] = None  ,				):
   import torch

   __a							=	OrderedDict()

   __a							=	super().generate_dummy_inputs(
       __SCREAMING_SNAKE_CASE  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				seq_length=__SCREAMING_SNAKE_CASE  ,				is_pair=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)

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

   return common_inputs
class  A_			(							a_			):

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   pass

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   PretrainedConfig							):
   return VisionEncoderDecoderEncoderOnnxConfig(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   PretrainedConfig  ,				__SCREAMING_SNAKE_CASE			:   PretrainedConfig  ,				__SCREAMING_SNAKE_CASE			:   str = "default"							):
   __a							=	encoder_config.hidden_size
   return VisionEncoderDecoderDecoderOnnxConfig(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


