




import copy

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


UpperCamelCase								=					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 			__lowerCamelCase	(   _UpperCAmelCase   ):




    """simple docstring"""

    snake_case__ =				"rag"
    snake_case__ =				True


    def __init__(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]=None       ,     SCREAMING_SNAKE_CASE__					:					Tuple=True       ,     SCREAMING_SNAKE_CASE__					:					Tuple=None       ,     SCREAMING_SNAKE_CASE__					:					Any=None       ,     SCREAMING_SNAKE_CASE__					:					str=None       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=None       ,     SCREAMING_SNAKE_CASE__					:					Dict=" / "       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=" // "       ,     SCREAMING_SNAKE_CASE__					:					int=5       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=300       ,     SCREAMING_SNAKE_CASE__					:					Dict=768       ,     SCREAMING_SNAKE_CASE__					:					List[str]=8       ,     SCREAMING_SNAKE_CASE__					:					List[Any]="wiki_dpr"       ,     SCREAMING_SNAKE_CASE__					:					Dict="train"       ,     SCREAMING_SNAKE_CASE__					:					str="compressed"       ,     SCREAMING_SNAKE_CASE__					:					str=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=None       ,     SCREAMING_SNAKE_CASE__					:					Any=False       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=False       ,     SCREAMING_SNAKE_CASE__					:					str=0.0       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=True       ,     SCREAMING_SNAKE_CASE__					:					List[str]=False       ,     SCREAMING_SNAKE_CASE__					:					Tuple=False       ,     SCREAMING_SNAKE_CASE__					:					Any=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]=True       ,     SCREAMING_SNAKE_CASE__					:					str=None       ,     **SCREAMING_SNAKE_CASE__					:					Dict       ,     )				->	Optional[Any]:
         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"
         lowerCAmelCase__          =			kwargs.pop("question_encoder"       )
         lowerCAmelCase__          =			question_encoder_config.pop("model_type"       )
         lowerCAmelCase__          =			kwargs.pop("generator"       )
         lowerCAmelCase__          =			decoder_config.pop("model_type"       )

         from ..auto.configuration_auto import AutoConfig

         lowerCAmelCase__          =			AutoConfig.for_model(_UpperCAmelCase       ,     **_UpperCAmelCase       )
         lowerCAmelCase__          =			AutoConfig.for_model(_UpperCAmelCase       ,     **_UpperCAmelCase       )

         lowerCAmelCase__          =			reduce_loss
         lowerCAmelCase__          =			label_smoothing
         lowerCAmelCase__          =			exclude_bos_score
         lowerCAmelCase__          =			do_marginalize

         lowerCAmelCase__          =			title_sep
         lowerCAmelCase__          =			doc_sep
         lowerCAmelCase__          =			n_docs
         lowerCAmelCase__          =			max_combined_length

         lowerCAmelCase__          =			dataset
         lowerCAmelCase__          =			dataset_split
         lowerCAmelCase__          =			index_name

         lowerCAmelCase__          =			retrieval_vector_size
         lowerCAmelCase__          =			retrieval_batch_size
         lowerCAmelCase__          =			passages_path
         lowerCAmelCase__          =			index_path
         lowerCAmelCase__          =			use_dummy_dataset

         lowerCAmelCase__          =			output_retrieved

         lowerCAmelCase__          =			do_deduplication

         lowerCAmelCase__          =			use_cache

         if self.forced_eos_token_id is None:
              lowerCAmelCase__          =			getattr(self.generator       ,     "forced_eos_token_id"       ,     _UpperCAmelCase       )


    @classmethod
    def        a				(	cls					:					str       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     **SCREAMING_SNAKE_CASE__					:					int       )				->	Union[str, Any]:
         return cls(question_encoder=question_encoder_config.to_dict()       ,     generator=generator_config.to_dict()       ,     **_UpperCAmelCase       )


    def        a				(	self					:					List[Any]       )				->	str:
         lowerCAmelCase__          =			copy.deepcopy(self.__dict__       )
         lowerCAmelCase__          =			self.question_encoder.to_dict()
         lowerCAmelCase__          =			self.generator.to_dict()
         lowerCAmelCase__          =			self.__class__.model_type
         return output





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			tempfile.mkdtemp()

         lowerCAmelCase__          =			BlipImageProcessor()
         lowerCAmelCase__          =			GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"       )

         lowerCAmelCase__          =			BlipaProcessor(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         processor.save_pretrained(self.tmpdirname       )


    def        a				(	self					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).tokenizer


    def        a				(	self					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).image_processor


    def        a				(	self					:					str       )				->	int:
         shutil.rmtree(self.tmpdirname       )


    def        a				(	self					:					List[Any]       )				->	Any:
         lowerCAmelCase__          =			[np.random.randint(255       ,     size=(3, 30, 400)       ,     dtype=np.uinta       )]

         lowerCAmelCase__          =			[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__       ,     0       ,     -1       )       ) for x in image_inputs]

         return image_inputs


    def        a				(	self					:					str       )				->	Dict:
         lowerCAmelCase__          =			BlipaProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         lowerCAmelCase__          =			self.get_tokenizer(bos_token="(BOS)"       ,     eos_token="(EOS)"       )
         lowerCAmelCase__          =			self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

         lowerCAmelCase__          =			BlipaProcessor.from_pretrained(
             self.tmpdirname       ,     bos_token="(BOS)"       ,     eos_token="(EOS)"       ,     do_normalize=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer       ,     SCREAMING_SNAKE_CASE__       )

         self.assertEqual(processor.image_processor.to_json_string()       ,     image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )
         lowerCAmelCase__          =			processor(images=SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()       ,     input_processor[key].sum()       ,     delta=1e-2       )


    def        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			tokenizer(SCREAMING_SNAKE_CASE__       ,     return_token_type_ids=SCREAMING_SNAKE_CASE__       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]       )


    def        a				(	self					:					Dict       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"
         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(list(inputs.keys()       )       ,     ["pixel_values", "input_ids", "attention_mask"]       )

         # test if it raises when no input is passed
         with pytest.raises(SCREAMING_SNAKE_CASE__       ):
              processor()


    def        a				(	self					:					str       )				->	List[str]:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__          =			processor.batch_decode(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Any:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"
         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()       )       ,     ["pixel_values", "input_ids", "attention_mask"]       )
