




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_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	int    		=   'vision-encoder-decoder'
      UpperCAmelCase__      :	Union[str, Any]    		=   True
      def __init__(				self: List[str]    ,       **UpperCamelCase_: List[Any]   ):
             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   =						kwargs.pop("""encoder"""   )
             __lowerCamelCase   =						encoder_config.pop("""model_type"""   )
             __lowerCamelCase   =						kwargs.pop("""decoder"""   )
             __lowerCamelCase   =						decoder_config.pop("""model_type"""   )

             __lowerCamelCase   =						AutoConfig.for_model(UpperCamelCase_    ,       **UpperCamelCase_   )
             __lowerCamelCase   =						AutoConfig.for_model(UpperCamelCase_    ,       **UpperCamelCase_   )
             __lowerCamelCase   =						True
      @classmethod
      def 			lowerCAmelCase__						(				cls: Tuple    ,       UpperCamelCase_: PretrainedConfig    ,       UpperCamelCase_: PretrainedConfig    ,       **UpperCamelCase_: Any   ):
             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()    ,       **UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						copy.deepcopy(self.__dict__   )
             __lowerCamelCase   =						self.encoder.to_dict()
             __lowerCamelCase   =						self.decoder.to_dict()
             __lowerCamelCase   =						self.__class__.model_type
             return output





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   version.parse('1.11')
      @property
      def 			lowerCAmelCase__						(				self: Any   ):
             return OrderedDict(
                 [
                     ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                 ]   )
      @property
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             return 1E-4




      @property
      def 			lowerCAmelCase__						(				self: Any   ):
             return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}   )





class     lowerCamelCase__(					__lowerCamelCase):
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             __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 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: "PreTrainedTokenizerBase"    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional["TensorType"] = None    ,       ):
             import torch

             __lowerCamelCase   =						OrderedDict()

             __lowerCamelCase   =						super().generate_dummy_inputs(
                 UpperCamelCase_    ,       batch_size=UpperCamelCase_    ,       seq_length=UpperCamelCase_    ,       is_pair=UpperCamelCase_    ,       framework=UpperCamelCase_   )

             __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(UpperCamelCase_   )

             return common_inputs





class     lowerCamelCase__(					__lowerCamelCase):
      @property
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             pass
      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: PretrainedConfig   ):
             return VisionEncoderDecoderEncoderOnnxConfig(UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: PretrainedConfig    ,       UpperCamelCase_: PretrainedConfig    ,       UpperCamelCase_: str = "default"   ):
             __lowerCamelCase   =						encoder_config.hidden_size
             return VisionEncoderDecoderDecoderOnnxConfig(UpperCamelCase_    ,       UpperCamelCase_   )







import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__:
      def __init__(				self: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						question_encoder
             __lowerCamelCase   =						generator
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[Any]   ):
             if os.path.isfile(UpperCamelCase_   ):
                    raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """question_encoder_tokenizer"""   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """generator_tokenizer"""   )
             self.question_encoder.save_pretrained(UpperCamelCase_   )
             self.generator.save_pretrained(UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: List[Any]    ,       UpperCamelCase_: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             # dynamically import AutoTokenizer
             from ..auto.tokenization_auto import AutoTokenizer

             __lowerCamelCase   =						kwargs.pop("""config"""    ,       UpperCamelCase_   )

             if config is None:
                    __lowerCamelCase   =						RagConfig.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.question_encoder    ,       subfolder="""question_encoder_tokenizer"""   )
             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.generator    ,       subfolder="""generator_tokenizer"""   )
             return cls(question_encoder=UpperCamelCase_    ,       generator=UpperCamelCase_   )
      def __call__(				self: Tuple    ,       *UpperCamelCase_: int    ,       **UpperCamelCase_: int   ):
             return self.current_tokenizer(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: List[Any]    ,       **UpperCamelCase_: List[Any]   ):
             return self.generator.batch_decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Union[str, Any]   ):
             return self.generator.decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.generator




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[List[str]] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: str = "longest"    ,       UpperCamelCase_: str = None    ,       UpperCamelCase_: bool = True    ,       **UpperCamelCase_: int    ,       ):
             warnings.warn(
                 """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                 """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                 """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                 """details"""    ,       UpperCamelCase_    ,       )
             if max_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             if tgt_texts is None:
                    return model_inputs
             # Process tgt_texts
             if max_target_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 text_target=UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             __lowerCamelCase   =						labels["""input_ids"""]
             return model_inputs

