





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

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

class      lowerCAmelCase							(      snake_case_    ):

 '''simple docstring'''





 UpperCAmelCase__									=	"""vision-encoder-decoder"""
 UpperCAmelCase__									=	True
 def __init__(   self						:   List[Any]    ,  **UpperCAmelCase						:   Any			)    ->			Dict:
     super().__init__(**UpperCAmelCase			)
     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__					:   str      				=	kwargs.pop('encoder'			)
     lowerCamelCase__					:   Any      				=	encoder_config.pop('model_type'			)
     lowerCamelCase__					:   Tuple      				=	kwargs.pop('decoder'			)
     lowerCamelCase__					:   Dict      				=	decoder_config.pop('model_type'			)

     lowerCamelCase__					:   Union[str, Any]      				=	AutoConfig.for_model(UpperCAmelCase    ,  **UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	AutoConfig.for_model(UpperCAmelCase    ,  **UpperCAmelCase			)
     lowerCamelCase__					:   int      				=	True
 @classmethod
 def   A_			(   cls						:   int    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			List[str]:
     logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'			)
     lowerCamelCase__					:   List[str]      				=	True
     lowerCamelCase__					:   int      				=	True

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







 def   A_			(   self						:   List[str]			)    ->			Optional[int]:
     lowerCamelCase__					:   Optional[int]      				=	copy.deepcopy(self.__dict__			)
     lowerCamelCase__					:   Optional[Any]      				=	self.encoder.to_dict()
     lowerCamelCase__					:   Dict      				=	self.decoder.to_dict()
     lowerCamelCase__					:   Optional[Any]      				=	self.__class__.model_type
     return output


class      lowerCAmelCase							(      snake_case_    ):

 '''simple docstring'''





 UpperCAmelCase__									=	version.parse("""1.11"""    )
 @property
 def   A_			(   self						:   Optional[Any]			)    ->			List[str]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]			)
 @property
 def   A_			(   self						:   Optional[int]			)    ->			List[Any]:
     return 1e-4







 @property
 def   A_			(   self						:   List[str]			)    ->			int:
     return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}			)


class      lowerCAmelCase							(      snake_case_    ):

 '''simple docstring'''





 @property
 def   A_			(   self						:   int			)    ->			List[Any]:
     lowerCamelCase__					:   str      				=	OrderedDict()
     lowerCamelCase__					:   str      				=	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
     lowerCamelCase__					:   Optional[int]      				=	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
     lowerCamelCase__					:   Any      				=	{0: 'batch', 1: 'encoder_sequence'}

     return common_inputs







 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   List[Any] = -1    ,  UpperCAmelCase						:   Optional[Any] = -1    ,  UpperCAmelCase						:   Union[str, Any] = False    ,  UpperCAmelCase						:   List[str] = None    ,  )    ->			Tuple:
     import torch

     lowerCamelCase__					:   Any      				=	OrderedDict()

     lowerCamelCase__					:   List[str]      				=	super().generate_dummy_inputs(
         UpperCAmelCase    ,  batch_size=UpperCAmelCase    ,  seq_length=UpperCAmelCase    ,  is_pair=UpperCAmelCase    ,  framework=UpperCAmelCase			)

     lowerCamelCase__				,							lowerCamelCase__					:   Dict      				=	dummy_input['input_ids'].shape
     lowerCamelCase__					:   Tuple      				=	(batch, encoder_sequence, self._config.encoder_hidden_size)
     lowerCamelCase__					:   Optional[Any]      				=	dummy_input.pop('input_ids'			)
     lowerCamelCase__					:   List[str]      				=	dummy_input.pop('attention_mask'			)
     lowerCamelCase__					:   str      				=	torch.zeros(UpperCAmelCase			)

     return common_inputs


class      lowerCAmelCase							(      snake_case_    ):

 '''simple docstring'''





 @property
 def   A_			(   self						:   int			)    ->			Tuple:
     pass
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Dict			)    ->			List[str]:
     return VisionEncoderDecoderEncoderOnnxConfig(UpperCAmelCase			)







 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   str = "default"			)    ->			Tuple:
     lowerCamelCase__					:   int      				=	encoder_config.hidden_size
     return VisionEncoderDecoderDecoderOnnxConfig(UpperCAmelCase    ,  UpperCAmelCase			)












_UpperCAmelCase       : List[Any]			     =       {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
_UpperCAmelCase       : Dict			     =       {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  float:
    lowerCamelCase__					:   Dict      				=	from_type.lower().strip('s'				)
    lowerCamelCase__					:   Dict      				=	to_type.lower().strip('s'				)

    lowerCamelCase__					:   Dict      				=	UNIT_SYMBOL.get(_UpperCAmelCase		,			_UpperCAmelCase				)
    lowerCamelCase__					:   str      				=	UNIT_SYMBOL.get(_UpperCAmelCase		,			_UpperCAmelCase				)

    if from_sanitized not in METRIC_CONVERSION:
        lowerCamelCase__					:   List[Any]      				=	(
            F"""Invalid 'from_type' value: {from_type!r}.\n"""
            F"""Conversion abbreviations are: {", ".join(_UpperCAmelCase				)}"""
        )
        raise ValueError(_UpperCAmelCase				)
    if to_sanitized not in METRIC_CONVERSION:
        lowerCamelCase__					:   Optional[Any]      				=	(
            F"""Invalid 'to_type' value: {to_type!r}.\n"""
            F"""Conversion abbreviations are: {", ".join(_UpperCAmelCase				)}"""
        )
        raise ValueError(_UpperCAmelCase				)
    lowerCamelCase__					:   Any      				=	METRIC_CONVERSION[from_sanitized]
    lowerCamelCase__					:   Optional[int]      				=	METRIC_CONVERSION[to_sanitized]
    lowerCamelCase__					:   List[str]      				=	1

    if from_exponent > to_exponent:
        lowerCamelCase__					:   Dict      				=	from_exponent - to_exponent
    else:
        lowerCamelCase__					:   Dict      				=	-(to_exponent - from_exponent)

    return value * pow(10		,			_UpperCAmelCase				)


if __name__ == "__main__":
    from doctest import testmod

    testmod()





