





import copy

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


_lowerCamelCase   				=			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(UpperCamelCase__    )
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"rag"
							lowerCamelCase_           =					True


							def __init__(							self    :Tuple							,				__A    :Optional[int]=None							,				__A    :Optional[Any]=True							,				__A    :List[str]=None							,				__A    :List[Any]=None							,				__A    :List[Any]=None							,				__A    :Tuple=None							,				__A    :List[Any]=None							,				__A    :Optional[int]=" / "							,				__A    :Optional[Any]=" // "							,				__A    :Tuple=5							,				__A    :int=300							,				__A    :Dict=768							,				__A    :str=8							,				__A    :int="wiki_dpr"							,				__A    :Optional[Any]="train"							,				__A    :Any="compressed"							,				__A    :str=None							,				__A    :str=None							,				__A    :List[Any]=False							,				__A    :Tuple=False							,				__A    :int=0.0							,				__A    :Any=True							,				__A    :Any=False							,				__A    :int=False							,				__A    :int=False							,				__A    :Union[str, Any]=True							,				__A    :str=None							,				**__A    :Dict							,				)   ->   Dict:



									"""simple docstring"""



									super().__init__(
									    bos_token_id=__A							,				pad_token_id=__A							,				eos_token_id=__A							,				decoder_start_token_id=__A							,				forced_eos_token_id=__A							,				is_encoder_decoder=__A							,				prefix=__A							,				vocab_size=__A							,				**__A							,				)
									assert (
									    "question_encoder" in kwargs and "generator" in kwargs
									), "Config has to be initialized with question_encoder and generator config"
									SCREAMING_SNAKE_CASE__			=      kwargs.pop("""question_encoder"""		)
									SCREAMING_SNAKE_CASE__			=      question_encoder_config.pop("""model_type"""		)
									SCREAMING_SNAKE_CASE__			=      kwargs.pop("""generator"""		)
									SCREAMING_SNAKE_CASE__			=      decoder_config.pop("""model_type"""		)

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE__			=      AutoConfig.for_model(__A							,				**__A		)
									SCREAMING_SNAKE_CASE__			=      AutoConfig.for_model(__A							,				**__A		)

									SCREAMING_SNAKE_CASE__			=      reduce_loss
									SCREAMING_SNAKE_CASE__			=      label_smoothing
									SCREAMING_SNAKE_CASE__			=      exclude_bos_score
									SCREAMING_SNAKE_CASE__			=      do_marginalize

									SCREAMING_SNAKE_CASE__			=      title_sep
									SCREAMING_SNAKE_CASE__			=      doc_sep
									SCREAMING_SNAKE_CASE__			=      n_docs
									SCREAMING_SNAKE_CASE__			=      max_combined_length

									SCREAMING_SNAKE_CASE__			=      dataset
									SCREAMING_SNAKE_CASE__			=      dataset_split
									SCREAMING_SNAKE_CASE__			=      index_name

									SCREAMING_SNAKE_CASE__			=      retrieval_vector_size
									SCREAMING_SNAKE_CASE__			=      retrieval_batch_size
									SCREAMING_SNAKE_CASE__			=      passages_path
									SCREAMING_SNAKE_CASE__			=      index_path
									SCREAMING_SNAKE_CASE__			=      use_dummy_dataset

									SCREAMING_SNAKE_CASE__			=      output_retrieved

									SCREAMING_SNAKE_CASE__			=      do_deduplication

									SCREAMING_SNAKE_CASE__			=      use_cache

									if self.forced_eos_token_id is None:
											SCREAMING_SNAKE_CASE__			=      getattr(self.generator							,				"""forced_eos_token_id"""							,				__A		)


							@classmethod
							def      _snake_case  (							cls    :Union[str, Any]							,				__A    :PretrainedConfig							,				__A    :PretrainedConfig							,				**__A    :List[Any]		)   ->   PretrainedConfig:



									"""simple docstring"""



									return cls(question_encoder=question_encoder_config.to_dict()							,				generator=generator_config.to_dict()							,				**__A		)


							def      _snake_case  (							self    :Union[str, Any]		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=      self.question_encoder.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.generator.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.__class__.model_type
									return output






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct_text_model"
							lowerCamelCase_           =					["past_key_values"]
							lowerCamelCase_           =					{
							    "hidden_size": "hidden_size",
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(							self    :Union[str, Any]							,				__A    :Any=5_0244							,				__A    :Optional[Any]=768							,				__A    :Tuple=64							,				__A    :List[str]=2048							,				__A    :int=12							,				__A    :str=12							,				__A    :Any=32							,				__A    :Tuple=128							,				__A    :int=0.1							,				__A    :str=1E-6							,				__A    :Optional[Any]=1.0							,				__A    :Union[str, Any]="gelu_new"							,				__A    :Any=0							,				__A    :List[str]=False							,				__A    :Optional[Any]=0							,				__A    :int=1							,				__A    :Optional[int]=False							,				__A    :Optional[Any]=True							,				**__A    :List[Any]							,				)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      d_kv
									SCREAMING_SNAKE_CASE__			=      d_ff
									SCREAMING_SNAKE_CASE__			=      num_layers
									SCREAMING_SNAKE_CASE__			=      num_heads
									SCREAMING_SNAKE_CASE__			=      relative_attention_num_buckets
									SCREAMING_SNAKE_CASE__			=      relative_attention_max_distance
									SCREAMING_SNAKE_CASE__			=      dropout_rate
									SCREAMING_SNAKE_CASE__			=      layer_norm_epsilon
									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      use_cache

									SCREAMING_SNAKE_CASE__			=      eos_token_id
									SCREAMING_SNAKE_CASE__			=      decoder_start_token_id

									# for backwards compatibility
									SCREAMING_SNAKE_CASE__			=      dense_act_fn

									super().__init__(
									    pad_token_id=__A							,				eos_token_id=__A							,				decoder_start_token_id=__A							,				tie_word_embeddings=__A							,				is_decoder=__A							,				**__A							,				)


							@classmethod
							def      _snake_case  (							cls    :Optional[int]							,				__A    :Union[str, os.PathLike]							,				**__A    :Optional[int]		)   ->   "PretrainedConfig":



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

									# get the text config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""		) == "pix2struct":
											SCREAMING_SNAKE_CASE__			=      config_dict["""text_config"""]

									if "model_type" in config_dict and hasattr(cls							,				"""model_type"""		) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
											    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

									return cls.from_dict(__A							,				**__A		)




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct_vision_model"


							def __init__(							self    :Optional[int]							,				__A    :int=768							,				__A    :Optional[Any]=768							,				__A    :Union[str, Any]=2048							,				__A    :int=64							,				__A    :Union[str, Any]=12							,				__A    :str=12							,				__A    :Any="gelu_new"							,				__A    :List[Any]=1E-6							,				__A    :Dict=0.0							,				__A    :int=0.0							,				__A    :int=1E-10							,				__A    :Dict=1.0							,				__A    :int=4096							,				__A    :int=32							,				__A    :int=128							,				**__A    :Tuple							,				)   ->   str:



									"""simple docstring"""



									super().__init__(**__A		)

									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      patch_embed_hidden_size
									SCREAMING_SNAKE_CASE__			=      d_ff
									SCREAMING_SNAKE_CASE__			=      dropout_rate
									SCREAMING_SNAKE_CASE__			=      num_hidden_layers
									SCREAMING_SNAKE_CASE__			=      num_attention_heads
									SCREAMING_SNAKE_CASE__			=      initializer_range
									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      attention_dropout
									SCREAMING_SNAKE_CASE__			=      layer_norm_eps
									SCREAMING_SNAKE_CASE__			=      dense_act_fn
									SCREAMING_SNAKE_CASE__			=      seq_len
									SCREAMING_SNAKE_CASE__			=      relative_attention_num_buckets
									SCREAMING_SNAKE_CASE__			=      relative_attention_max_distance
									SCREAMING_SNAKE_CASE__			=      d_kv


							@classmethod
							def      _snake_case  (							cls    :str							,				__A    :Union[str, os.PathLike]							,				**__A    :str		)   ->   "PretrainedConfig":



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

									# get the vision config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""		) == "pix2struct":
											SCREAMING_SNAKE_CASE__			=      config_dict["""vision_config"""]

									if "model_type" in config_dict and hasattr(cls							,				"""model_type"""		) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
											    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

									return cls.from_dict(__A							,				**__A		)




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct"
							lowerCamelCase_           =					True


							def __init__(							self    :str							,				__A    :Optional[Any]=None							,				__A    :List[str]=None							,				__A    :Optional[Any]=1.0							,				__A    :Optional[Any]=0.0_2							,				__A    :Any=False							,				__A    :Tuple=False							,				__A    :Any=True							,				**__A    :Dict							,				)   ->   Union[str, Any]:



									"""simple docstring"""



									super().__init__(tie_word_embeddings=__A							,				is_encoder_decoder=__A							,				**__A		)

									if text_config is None:
											SCREAMING_SNAKE_CASE__			=      {}
											logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""		)

									if vision_config is None:
											SCREAMING_SNAKE_CASE__			=      {}
											logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""		)

									SCREAMING_SNAKE_CASE__			=      PixaStructTextConfig(**__A		)
									SCREAMING_SNAKE_CASE__			=      PixaStructVisionConfig(**__A		)

									SCREAMING_SNAKE_CASE__			=      self.text_config.decoder_start_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.pad_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.eos_token_id

									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      initializer_range

									SCREAMING_SNAKE_CASE__			=      self.initializer_range
									SCREAMING_SNAKE_CASE__			=      self.initializer_range

									SCREAMING_SNAKE_CASE__			=      is_vqa


							@classmethod
							def      _snake_case  (							cls    :Union[str, Any]							,				__A    :PixaStructTextConfig							,				__A    :PixaStructVisionConfig							,				**__A    :Optional[int]		)   ->   Optional[Any]:



									"""simple docstring"""



									return cls(text_config=text_config.to_dict()							,				vision_config=vision_config.to_dict()							,				**__A		)


							def      _snake_case  (							self    :str		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=      self.text_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.vision_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.__class__.model_type
									return output