




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


__a          =			R'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'

@add_start_docstrings(_a       )
class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''rag'''
    lowerCAmelCase        =				True





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=" / "       ,_SCREAMING_SNAKE_CASE=" // "       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=300       ,_SCREAMING_SNAKE_CASE=768       ,_SCREAMING_SNAKE_CASE=8       ,_SCREAMING_SNAKE_CASE="wiki_dpr"       ,_SCREAMING_SNAKE_CASE="train"       ,_SCREAMING_SNAKE_CASE="compressed"       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=0.0       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=None       ,**_SCREAMING_SNAKE_CASE       ,)			->		List[str]:
         super().__init__(
             bos_token_id=_SCREAMING_SNAKE_CASE       ,pad_token_id=_SCREAMING_SNAKE_CASE       ,eos_token_id=_SCREAMING_SNAKE_CASE       ,decoder_start_token_id=_SCREAMING_SNAKE_CASE       ,forced_eos_token_id=_SCREAMING_SNAKE_CASE       ,is_encoder_decoder=_SCREAMING_SNAKE_CASE       ,prefix=_SCREAMING_SNAKE_CASE       ,vocab_size=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         assert (
             "question_encoder" in kwargs and "generator" in kwargs
         ), "Config has to be initialized with question_encoder and generator config"
         UpperCAmelCase_      :							List[Any]       =      kwargs.pop('''question_encoder'''  )
         UpperCAmelCase_      :							Optional[int]       =      question_encoder_config.pop('''model_type'''  )
         UpperCAmelCase_      :							str       =      kwargs.pop('''generator'''  )
         UpperCAmelCase_      :							Any       =      decoder_config.pop('''model_type'''  )

         from ..auto.configuration_auto import AutoConfig

         UpperCAmelCase_      :							Union[str, Any]       =      AutoConfig.for_model(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      AutoConfig.for_model(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      reduce_loss
         UpperCAmelCase_      :							Tuple       =      label_smoothing
         UpperCAmelCase_      :							str       =      exclude_bos_score
         UpperCAmelCase_      :							Any       =      do_marginalize

         UpperCAmelCase_      :							List[Any]       =      title_sep
         UpperCAmelCase_      :							Union[str, Any]       =      doc_sep
         UpperCAmelCase_      :							int       =      n_docs
         UpperCAmelCase_      :							Optional[Any]       =      max_combined_length

         UpperCAmelCase_      :							List[str]       =      dataset
         UpperCAmelCase_      :							str       =      dataset_split
         UpperCAmelCase_      :							Optional[int]       =      index_name

         UpperCAmelCase_      :							List[Any]       =      retrieval_vector_size
         UpperCAmelCase_      :							Optional[int]       =      retrieval_batch_size
         UpperCAmelCase_      :							Optional[Any]       =      passages_path
         UpperCAmelCase_      :							str       =      index_path
         UpperCAmelCase_      :							Tuple       =      use_dummy_dataset

         UpperCAmelCase_      :							Optional[int]       =      output_retrieved

         UpperCAmelCase_      :							List[Any]       =      do_deduplication

         UpperCAmelCase_      :							Union[str, Any]       =      use_cache

         if self.forced_eos_token_id is None:
              UpperCAmelCase_      :							Optional[int]       =      getattr(self.generator       ,'''forced_eos_token_id'''       ,_SCREAMING_SNAKE_CASE  )





    @classmethod
    def 							a__				(				cls       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		PretrainedConfig:
         return cls(question_encoder=question_encoder_config.to_dict()       ,generator=generator_config.to_dict()       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Any       =      copy.deepcopy(self.__dict__  )
         UpperCAmelCase_      :							int       =      self.question_encoder.to_dict()
         UpperCAmelCase_      :							Union[str, Any]       =      self.generator.to_dict()
         UpperCAmelCase_      :							int       =      self.__class__.model_type
         return output





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

from packaging import version

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


if TYPE_CHECKING:
     from ...processing_utils import ProcessorMixin
     from ...utils import TensorType


__a          =			logging.get_logger(__name__)

__a          =			{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''layoutlmv3'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=50_265       ,_SCREAMING_SNAKE_CASE=768       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=3_072       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=1e-5       ,_SCREAMING_SNAKE_CASE=1       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=1_024       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=64       ,_SCREAMING_SNAKE_CASE=256       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=224       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         super().__init__(
             vocab_size=_SCREAMING_SNAKE_CASE       ,hidden_size=_SCREAMING_SNAKE_CASE       ,num_hidden_layers=_SCREAMING_SNAKE_CASE       ,num_attention_heads=_SCREAMING_SNAKE_CASE       ,intermediate_size=_SCREAMING_SNAKE_CASE       ,hidden_act=_SCREAMING_SNAKE_CASE       ,hidden_dropout_prob=_SCREAMING_SNAKE_CASE       ,attention_probs_dropout_prob=_SCREAMING_SNAKE_CASE       ,max_position_embeddings=_SCREAMING_SNAKE_CASE       ,type_vocab_size=_SCREAMING_SNAKE_CASE       ,initializer_range=_SCREAMING_SNAKE_CASE       ,layer_norm_eps=_SCREAMING_SNAKE_CASE       ,pad_token_id=_SCREAMING_SNAKE_CASE       ,bos_token_id=_SCREAMING_SNAKE_CASE       ,eos_token_id=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							Dict       =      max_ad_position_embeddings
         UpperCAmelCase_      :							Any       =      coordinate_size
         UpperCAmelCase_      :							Tuple       =      shape_size
         UpperCAmelCase_      :							Optional[int]       =      has_relative_attention_bias
         UpperCAmelCase_      :							Union[str, Any]       =      rel_pos_bins
         UpperCAmelCase_      :							Dict       =      max_rel_pos
         UpperCAmelCase_      :							Union[str, Any]       =      has_spatial_attention_bias
         UpperCAmelCase_      :							Any       =      rel_ad_pos_bins
         UpperCAmelCase_      :							Tuple       =      max_rel_ad_pos
         UpperCAmelCase_      :							List[str]       =      text_embed
         UpperCAmelCase_      :							int       =      visual_embed
         UpperCAmelCase_      :							int       =      input_size
         UpperCAmelCase_      :							Dict       =      num_channels
         UpperCAmelCase_      :							int       =      patch_size
         UpperCAmelCase_      :							Dict       =      classifier_dropout


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				version.parse('''1.12'''       )





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         # The order of inputs is different for question answering and sequence classification
         if self.task in ["question-answering", "sequence-classification"]:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]  )
         else:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
                  ]  )





    @property
    def 							a__				(				self  )			->		float:
         return 1e-5





    @property
    def 							a__				(				self  )			->		int:
         return 12





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = 3       ,_SCREAMING_SNAKE_CASE = 40       ,_SCREAMING_SNAKE_CASE = 40       ,)			->		Mapping[str, Any]:
         setattr(processor.image_processor       ,'''apply_ocr'''       ,_SCREAMING_SNAKE_CASE  )

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCAmelCase_      :							List[str]       =      compute_effective_axis_dimension(
             _SCREAMING_SNAKE_CASE       ,fixed_dimension=OnnxConfig.default_fixed_batch       ,num_token_to_add=0  )
         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         UpperCAmelCase_      :							Optional[Any]       =      processor.tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      compute_effective_axis_dimension(
             _SCREAMING_SNAKE_CASE       ,fixed_dimension=OnnxConfig.default_fixed_sequence       ,num_token_to_add=_SCREAMING_SNAKE_CASE  )
         # Generate dummy inputs according to compute batch and sequence
         UpperCAmelCase_      :							Optional[Any]       =      [[''' '''.join([processor.tokenizer.unk_token]  ) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         UpperCAmelCase_      :							Tuple       =      [[[48, 84, 73, 128]]] * batch_size

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
         UpperCAmelCase_      :							Union[str, Any]       =      self._generate_dummy_images(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      dict(
             processor(
                 _SCREAMING_SNAKE_CASE       ,text=_SCREAMING_SNAKE_CASE       ,boxes=_SCREAMING_SNAKE_CASE       ,return_tensors=_SCREAMING_SNAKE_CASE       ,)  )

         return inputs