

"""simple docstring"""




import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


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

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


A_							:	Any      =       logging.get_logger(__name__)

A_							:	int      =       {
    '''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
    '''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
    '''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}





class       lowerCamelCase  (UpperCamelCase_	):
			lowerCamelCase__      :						Dict    						=							'owlvit_text_model'

			def __init__(    self :  Optional[int]			, __UpperCAmelCase :  List[Any]=4_9_4_0_8			, __UpperCAmelCase :  List[Any]=5_1_2			, __UpperCAmelCase :  int=2_0_4_8			, __UpperCAmelCase :  Dict=1_2			, __UpperCAmelCase :  Optional[Any]=8			, __UpperCAmelCase :  Dict=1_6			, __UpperCAmelCase :  Union[str, Any]="quick_gelu"			, __UpperCAmelCase :  Tuple=1e-5			, __UpperCAmelCase :  List[str]=0.0			, __UpperCAmelCase :  Optional[Any]=0.02			, __UpperCAmelCase :  Union[str, Any]=1.0			, __UpperCAmelCase :  Tuple=0			, __UpperCAmelCase :  Optional[int]=4_9_4_0_6			, __UpperCAmelCase :  str=4_9_4_0_7			, **__UpperCAmelCase :  Any			, )      ->			Any:
						super().__init__(pad_token_id=A_			, bos_token_id=A_			, eos_token_id=A_			, **A_				)

						SCREAMING_SNAKE_CASE__									=					vocab_size
						SCREAMING_SNAKE_CASE__									=					hidden_size
						SCREAMING_SNAKE_CASE__									=					intermediate_size
						SCREAMING_SNAKE_CASE__									=					num_hidden_layers
						SCREAMING_SNAKE_CASE__									=					num_attention_heads
						SCREAMING_SNAKE_CASE__									=					max_position_embeddings
						SCREAMING_SNAKE_CASE__									=					hidden_act
						SCREAMING_SNAKE_CASE__									=					layer_norm_eps
						SCREAMING_SNAKE_CASE__									=					attention_dropout
						SCREAMING_SNAKE_CASE__									=					initializer_range
						SCREAMING_SNAKE_CASE__									=					initializer_factor





			@classmethod
			def     SCREAMING_SNAKE_CASE (    cls :  Union[str, Any]			, __UpperCAmelCase :  Union[str, os.PathLike]			, **__UpperCAmelCase :  Tuple				)      ->			Any:
						cls._set_token_in_kwargs(A_				)

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

						# get the text config dict if we are loading from OwlViTConfig
						if config_dict.get("""model_type"""				) == "owlvit":
									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       lowerCamelCase  (UpperCamelCase_	):
			lowerCamelCase__      :						Tuple    						=							'owlvit_vision_model'

			def __init__(    self :  int			, __UpperCAmelCase :  Dict=7_6_8			, __UpperCAmelCase :  int=3_0_7_2			, __UpperCAmelCase :  Optional[int]=1_2			, __UpperCAmelCase :  str=1_2			, __UpperCAmelCase :  List[Any]=3			, __UpperCAmelCase :  int=7_6_8			, __UpperCAmelCase :  Any=3_2			, __UpperCAmelCase :  List[Any]="quick_gelu"			, __UpperCAmelCase :  int=1e-5			, __UpperCAmelCase :  Optional[int]=0.0			, __UpperCAmelCase :  Any=0.02			, __UpperCAmelCase :  Dict=1.0			, **__UpperCAmelCase :  int			, )      ->			Optional[Any]:
						super().__init__(**A_				)

						SCREAMING_SNAKE_CASE__									=					hidden_size
						SCREAMING_SNAKE_CASE__									=					intermediate_size
						SCREAMING_SNAKE_CASE__									=					num_hidden_layers
						SCREAMING_SNAKE_CASE__									=					num_attention_heads
						SCREAMING_SNAKE_CASE__									=					num_channels
						SCREAMING_SNAKE_CASE__									=					image_size
						SCREAMING_SNAKE_CASE__									=					patch_size
						SCREAMING_SNAKE_CASE__									=					hidden_act
						SCREAMING_SNAKE_CASE__									=					layer_norm_eps
						SCREAMING_SNAKE_CASE__									=					attention_dropout
						SCREAMING_SNAKE_CASE__									=					initializer_range
						SCREAMING_SNAKE_CASE__									=					initializer_factor





			@classmethod
			def     SCREAMING_SNAKE_CASE (    cls :  Tuple			, __UpperCAmelCase :  Union[str, os.PathLike]			, **__UpperCAmelCase :  Tuple				)      ->			List[str]:
						cls._set_token_in_kwargs(A_				)

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

						# get the vision config dict if we are loading from OwlViTConfig
						if config_dict.get("""model_type"""				) == "owlvit":
									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       lowerCamelCase  (UpperCamelCase_	):
			lowerCamelCase__      :						List[Any]    						=							'owlvit'
			lowerCamelCase__      :						Optional[Any]    						=							True

			def __init__(    self :  Dict			, __UpperCAmelCase :  Union[str, Any]=None			, __UpperCAmelCase :  Tuple=None			, __UpperCAmelCase :  Tuple=5_1_2			, __UpperCAmelCase :  Union[str, Any]=2.6_592			, __UpperCAmelCase :  str=True			, **__UpperCAmelCase :  Any			, )      ->			str:
						super().__init__(**A_				)

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

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

						SCREAMING_SNAKE_CASE__									=					OwlViTTextConfig(**A_				)
						SCREAMING_SNAKE_CASE__									=					OwlViTVisionConfig(**A_				)

						SCREAMING_SNAKE_CASE__									=					projection_dim
						SCREAMING_SNAKE_CASE__									=					logit_scale_init_value
						SCREAMING_SNAKE_CASE__									=					return_dict
						SCREAMING_SNAKE_CASE__									=					1.0

			@classmethod
			def     SCREAMING_SNAKE_CASE (    cls :  List[str]			, __UpperCAmelCase :  Union[str, os.PathLike]			, **__UpperCAmelCase :  int				)      ->			Any:
						cls._set_token_in_kwargs(A_				)

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

						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_				)

			@classmethod
			def     SCREAMING_SNAKE_CASE (    cls :  List[Any]			, __UpperCAmelCase :  Dict			, __UpperCAmelCase :  Dict			, **__UpperCAmelCase :  str				)      ->			Union[str, Any]:
						SCREAMING_SNAKE_CASE__									=					{}
						SCREAMING_SNAKE_CASE__									=					text_config
						SCREAMING_SNAKE_CASE__									=					vision_config

						return cls.from_dict(A_			, **A_				)





			def     SCREAMING_SNAKE_CASE (    self :  Optional[Any]				)      ->			List[Any]:
						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





class       lowerCamelCase  (UpperCamelCase_	):

			@property
			def     SCREAMING_SNAKE_CASE (    self :  Any				)      ->			List[str]:
						return OrderedDict(
						    [
						        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
						        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
						        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
						    ]				)

			@property
			def     SCREAMING_SNAKE_CASE (    self :  Dict				)      ->			Any:
						return OrderedDict(
						    [
						        ("""logits_per_image""", {0: """batch"""}),
						        ("""logits_per_text""", {0: """batch"""}),
						        ("""text_embeds""", {0: """batch"""}),
						        ("""image_embeds""", {0: """batch"""}),
						    ]				)

			@property
			def     SCREAMING_SNAKE_CASE (    self :  List[str]				)      ->			Dict:
						return 1e-4

			def     SCREAMING_SNAKE_CASE (    self :  Optional[int]			, __UpperCAmelCase :  "ProcessorMixin"			, __UpperCAmelCase :  int = -1			, __UpperCAmelCase :  int = -1			, __UpperCAmelCase :  Optional["TensorType"] = None			, )      ->			str:
						SCREAMING_SNAKE_CASE__									=					super().generate_dummy_inputs(
						    processor.tokenizer			, batch_size=A_			, seq_length=A_			, framework=A_				)
						SCREAMING_SNAKE_CASE__									=					super().generate_dummy_inputs(
						    processor.image_processor			, batch_size=A_			, framework=A_				)
						return {**text_input_dict, **image_input_dict}





			@property
			def     SCREAMING_SNAKE_CASE (    self :  Any				)      ->			Optional[int]:
						return 1_4







import copy

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


A__  :  Union[str, Any]								=					R'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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



@add_start_docstrings(UpperCamelCase_						)
class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''rag'''
							_a			      = True




							def __init__(       self     :       str				,				A_     :       List[Any]=None				,				A_     :       str=True				,				A_     :       Tuple=None				,				A_     :       Union[str, Any]=None				,				A_     :       List[str]=None				,				A_     :       List[str]=None				,				A_     :       List[Any]=None				,				A_     :       Union[str, Any]=" / "				,				A_     :       Tuple=" // "				,				A_     :       Any=5				,				A_     :       Optional[Any]=3_0_0				,				A_     :       Tuple=7_6_8				,				A_     :       Union[str, Any]=8				,				A_     :       Dict="wiki_dpr"				,				A_     :       Optional[Any]="train"				,				A_     :       Dict="compressed"				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				A_     :       str=False				,				A_     :       Dict=False				,				A_     :       Dict=0.0				,				A_     :       List[str]=True				,				A_     :       List[str]=False				,				A_     :       List[Any]=False				,				A_     :       Any=False				,				A_     :       Optional[int]=True				,				A_     :       int=None				,				**A_     :       List[str]				,				):
									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"
									lowerCAmelCase_			:     List[str]									= kwargs.pop('''question_encoder''')
									lowerCAmelCase_			:     Tuple									= question_encoder_config.pop('''model_type''')
									lowerCAmelCase_			:     Tuple									= kwargs.pop('''generator''')
									lowerCAmelCase_			:     Dict									= decoder_config.pop('''model_type''')

									from ..auto.configuration_auto import AutoConfig

									lowerCAmelCase_			:     Union[str, Any]									= AutoConfig.for_model(A_				,				**A_)
									lowerCAmelCase_			:     int									= AutoConfig.for_model(A_				,				**A_)

									lowerCAmelCase_			:     List[Any]									= reduce_loss
									lowerCAmelCase_			:     Optional[Any]									= label_smoothing
									lowerCAmelCase_			:     Union[str, Any]									= exclude_bos_score
									lowerCAmelCase_			:     List[Any]									= do_marginalize

									lowerCAmelCase_			:     int									= title_sep
									lowerCAmelCase_			:     Optional[int]									= doc_sep
									lowerCAmelCase_			:     List[str]									= n_docs
									lowerCAmelCase_			:     int									= max_combined_length

									lowerCAmelCase_			:     Union[str, Any]									= dataset
									lowerCAmelCase_			:     int									= dataset_split
									lowerCAmelCase_			:     Dict									= index_name

									lowerCAmelCase_			:     Union[str, Any]									= retrieval_vector_size
									lowerCAmelCase_			:     Optional[Any]									= retrieval_batch_size
									lowerCAmelCase_			:     List[str]									= passages_path
									lowerCAmelCase_			:     Any									= index_path
									lowerCAmelCase_			:     int									= use_dummy_dataset

									lowerCAmelCase_			:     Tuple									= output_retrieved

									lowerCAmelCase_			:     List[Any]									= do_deduplication

									lowerCAmelCase_			:     Union[str, Any]									= use_cache

									if self.forced_eos_token_id is None:
											lowerCAmelCase_			:     List[Any]									= getattr(self.generator				,				'''forced_eos_token_id'''				,				A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       str				,				A_     :       PretrainedConfig				,				A_     :       PretrainedConfig				,				**A_     :       Any):
									return cls(question_encoder=question_encoder_config.to_dict()				,				generator=generator_config.to_dict()				,				**A_)







							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Tuple									= self.question_encoder.to_dict()
									lowerCAmelCase_			:     Dict									= self.generator.to_dict()
									lowerCAmelCase_			:     str									= self.__class__.model_type
									return output





