
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

lowerCamelCase_  :	str 				=					logging.get_logger(__name__)

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	"""vision-encoder-decoder"""
			__UpperCamelCase :  List[Any]    =	True

			def __init__(    self	:    Tuple  ,		**snake_case_	:    Dict ):
							super().__init__(**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}''' )

							UpperCamelCase_:							Dict		=  kwargs.pop("""encoder""" )
							UpperCamelCase_:							Optional[int]		=  encoder_config.pop("""model_type""" )
							UpperCamelCase_:							Dict		=  kwargs.pop("""decoder""" )
							UpperCamelCase_:							int		=  decoder_config.pop("""model_type""" )

							UpperCamelCase_:							List[str]		=  AutoConfig.for_model(snake_case_  ,		**snake_case_ )
							UpperCamelCase_:							str		=  AutoConfig.for_model(snake_case_  ,		**snake_case_ )
							UpperCamelCase_:							Tuple		=  True

			@classmethod
			def  lowerCAmelCase__		(    cls	:    int  ,		snake_case_	:    PretrainedConfig  ,		snake_case_	:    PretrainedConfig  ,		**snake_case_	:    List[Any] ):
							logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
							UpperCamelCase_:							str		=  True
							UpperCamelCase_:							Dict		=  True

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




			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							List[str]		=  copy.deepcopy(self.__dict__ )
							UpperCamelCase_:							Dict		=  self.encoder.to_dict()
							UpperCamelCase_:							Dict		=  self.decoder.to_dict()
							UpperCamelCase_:							Optional[int]		=  self.__class__.model_type
							return output




class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Any    =	version.parse("""1.11"""					)

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

			@property
			def  lowerCAmelCase__		(    self	:    Dict ):
							return 1e-4




			@property
			def  lowerCAmelCase__		(    self	:    List[str] ):
							return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )




class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			@property
			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							List[Any]		=  OrderedDict()
							UpperCamelCase_:							Tuple		=  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
							UpperCamelCase_:							List[str]		=  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
							UpperCamelCase_:							Optional[Any]		=  {0: """batch""", 1: """encoder_sequence"""}

							return common_inputs




			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    "PreTrainedTokenizerBase"  ,		snake_case_	:    int = -1  ,		snake_case_	:    int = -1  ,		snake_case_	:    bool = False  ,		snake_case_	:    Optional["TensorType"] = None  ,		):
							import torch

							UpperCamelCase_:							List[Any]		=  OrderedDict()

							UpperCamelCase_:							Tuple		=  super().generate_dummy_inputs(
							    snake_case_  ,		batch_size=snake_case_  ,		seq_length=snake_case_  ,		is_pair=snake_case_  ,		framework=snake_case_ )

							UpperCamelCase_:							int		=  dummy_input["""input_ids"""].shape
							UpperCamelCase_:							List[Any]		=  (batch, encoder_sequence, self._config.encoder_hidden_size)
							UpperCamelCase_:							List[Any]		=  dummy_input.pop("""input_ids""" )
							UpperCamelCase_:							Any		=  dummy_input.pop("""attention_mask""" )
							UpperCamelCase_:							Tuple		=  torch.zeros(snake_case_ )

							return common_inputs




class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			@property
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							pass

			def  lowerCAmelCase__		(    self	:    List[str]  ,		snake_case_	:    PretrainedConfig ):
							return VisionEncoderDecoderEncoderOnnxConfig(snake_case_ )




			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    PretrainedConfig  ,		snake_case_	:    PretrainedConfig  ,		snake_case_	:    str = "default" ):
							UpperCamelCase_:							Union[str, Any]		=  encoder_config.hidden_size
							return VisionEncoderDecoderDecoderOnnxConfig(snake_case_  ,		snake_case_ )








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase_  :	Dict 				=					{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_byta import ByTaTokenizer
else:
							import sys

							lowerCamelCase_  :	Any 				=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






