


"""simple docstring"""



import copy

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


_A             =   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(snake_case_     )
class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Any							  =  '''rag'''
   __UpperCAmelCase			:	Optional[Any]							  =  True







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=True						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=" / "						,     UpperCamelCase__=" // "						,     UpperCamelCase__=5						,     UpperCamelCase__=300						,     UpperCamelCase__=768						,     UpperCamelCase__=8						,     UpperCamelCase__="wiki_dpr"						,     UpperCamelCase__="train"						,     UpperCamelCase__="compressed"						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=False						,     UpperCamelCase__=False						,     UpperCamelCase__=0.0						,     UpperCamelCase__=True						,     UpperCamelCase__=False						,     UpperCamelCase__=False						,     UpperCamelCase__=False						,     UpperCamelCase__=True						,     UpperCamelCase__=None						,     **UpperCamelCase__						,     )     ->		int:

       '''simple docstring'''




       super().__init__(
           bos_token_id=UpperCamelCase__						,     pad_token_id=UpperCamelCase__						,     eos_token_id=UpperCamelCase__						,     decoder_start_token_id=UpperCamelCase__						,     forced_eos_token_id=UpperCamelCase__						,     is_encoder_decoder=UpperCamelCase__						,     prefix=UpperCamelCase__						,     vocab_size=UpperCamelCase__						,     **UpperCamelCase__						,     )
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       snake_case   :				Tuple				 =   kwargs.pop("question_encoder"						)
       snake_case   :				int				 =   question_encoder_config.pop("model_type"						)
       snake_case   :				Tuple				 =   kwargs.pop("generator"						)
       snake_case   :				Optional[Any]				 =   decoder_config.pop("model_type"						)

       from ..auto.configuration_auto import AutoConfig

       snake_case   :				Optional[Any]				 =   AutoConfig.for_model(UpperCamelCase__						,     **UpperCamelCase__						)
       snake_case   :				str				 =   AutoConfig.for_model(UpperCamelCase__						,     **UpperCamelCase__						)

       snake_case   :				Tuple				 =   reduce_loss
       snake_case   :				str				 =   label_smoothing
       snake_case   :				Union[str, Any]				 =   exclude_bos_score
       snake_case   :				List[str]				 =   do_marginalize

       snake_case   :				Dict				 =   title_sep
       snake_case   :				Union[str, Any]				 =   doc_sep
       snake_case   :				int				 =   n_docs
       snake_case   :				Tuple				 =   max_combined_length

       snake_case   :				Any				 =   dataset
       snake_case   :				List[str]				 =   dataset_split
       snake_case   :				str				 =   index_name

       snake_case   :				Union[str, Any]				 =   retrieval_vector_size
       snake_case   :				Union[str, Any]				 =   retrieval_batch_size
       snake_case   :				Tuple				 =   passages_path
       snake_case   :				List[str]				 =   index_path
       snake_case   :				str				 =   use_dummy_dataset

       snake_case   :				str				 =   output_retrieved

       snake_case   :				int				 =   do_deduplication

       snake_case   :				Union[str, Any]				 =   use_cache

       if self.forced_eos_token_id is None:
           snake_case   :				Union[str, Any]				 =   getattr(self.generator						,     "forced_eos_token_id"						,     UpperCamelCase__						)







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		PretrainedConfig:

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   copy.deepcopy(self.__dict__						)
       snake_case   :				Optional[Any]				 =   self.question_encoder.to_dict()
       snake_case   :				Optional[int]				 =   self.generator.to_dict()
       snake_case   :				Union[str, Any]				 =   self.__class__.model_type
       return output









"""simple docstring"""



import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:







  class _lowerCAmelCase    :







     @staticmethod
     def 			lowerCamelCase		(     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

         '''simple docstring'''




         pass







@is_pipeline_test
@require_vision
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):
   __UpperCAmelCase			:	List[Any]							  =  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   pipeline(
           "zero-shot-object-detection"						,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

       snake_case   :				int				 =   [
           {
               "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "candidate_labels": ["cat", "remote", "couch"],
           }
       ]
       return object_detector, examples







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				str				 =   object_detector(examples[0]						,     threshold=0.0						)

       snake_case   :				str				 =   len(UpperCamelCase__						)
       self.assertGreater(UpperCamelCase__						,     0						)
       self.assertEqual(
           UpperCamelCase__						,     [
               {
                   "score": ANY(UpperCamelCase__						),
                   "label": ANY(UpperCamelCase__						),
                   "box": {"xmin": ANY(UpperCamelCase__						), "ymin": ANY(UpperCamelCase__						), "xmax": ANY(UpperCamelCase__						), "ymax": ANY(UpperCamelCase__						)},
               }
               for i in range(UpperCamelCase__						)
           ]						,     )







   @require_tf
   @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       pass







   @require_torch
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				Dict				 =   pipeline(
           "zero-shot-object-detection"						,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

       snake_case   :				Optional[Any]				 =   object_detector(
           "./tests/fixtures/tests_samples/COCO/000000039769.png"						,     candidate_labels=["cat", "remote", "couch"]						,     threshold=0.64						,     )

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
               {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
               {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
           ]						,     )

       snake_case   :				Dict				 =   object_detector(
           [
               {
                   "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                   "candidate_labels": ["cat", "remote", "couch"],
               }
           ]						,     threshold=0.64						,     )

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               [
                   {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                   {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                   {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
               ]
           ]						,     )







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Tuple				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
               {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
               {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
               {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
           ]						,     )

       snake_case   :				List[Any]				 =   object_detector(
           [
               {
                   "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                   "candidate_labels": ["cat", "remote", "couch"],
               },
               {
                   "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                   "candidate_labels": ["cat", "remote", "couch"],
               },
           ]						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               [
                   {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                   {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                   {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                   {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                   {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
               ],
               [
                   {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                   {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                   {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                   {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                   {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
               ],
           ]						,     )







   @require_tf
   @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       pass







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   0.2
       snake_case   :				List[str]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				List[Any]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     threshold=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
               {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
           ]						,     )







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   2
       snake_case   :				Optional[Any]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Optional[int]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     top_k=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
           ]						,     )





