
import copy

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


A     :	Optional[Any]    = 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			):
				snake_case_						   =    '''rag'''
				snake_case_						   =    True




				def __init__(     self		,     _snake_case=None		,     _snake_case=True		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=" / "		,     _snake_case=" // "		,     _snake_case=5		,     _snake_case=300		,     _snake_case=768		,     _snake_case=8		,     _snake_case="wiki_dpr"		,     _snake_case="train"		,     _snake_case="compressed"		,     _snake_case=None		,     _snake_case=None		,     _snake_case=False		,     _snake_case=False		,     _snake_case=0.0		,     _snake_case=True		,     _snake_case=False		,     _snake_case=False		,     _snake_case=False		,     _snake_case=True		,     _snake_case=None		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




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

								from ..auto.configuration_auto import AutoConfig

								__a										=    AutoConfig.for_model(_snake_case		,     **_snake_case				)
								__a										=    AutoConfig.for_model(_snake_case		,     **_snake_case				)

								__a										=    reduce_loss
								__a										=    label_smoothing
								__a										=    exclude_bos_score
								__a										=    do_marginalize

								__a										=    title_sep
								__a										=    doc_sep
								__a										=    n_docs
								__a										=    max_combined_length

								__a										=    dataset
								__a										=    dataset_split
								__a										=    index_name

								__a										=    retrieval_vector_size
								__a										=    retrieval_batch_size
								__a										=    passages_path
								__a										=    index_path
								__a										=    use_dummy_dataset

								__a										=    output_retrieved

								__a										=    do_deduplication

								__a										=    use_cache

								if self.forced_eos_token_id is None:
												__a										=    getattr(self.generator		,     '''forced_eos_token_id'''		,     _snake_case				)




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      PretrainedConfig:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




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



'''simple docstring'''



import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp





@slow
@require_flax
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         return f'gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE_	) for s in shape]	)}.npy'







    def       lowercase_				(							self	)				->     Optional[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 4, 64, 64)			,     SCREAMING_SNAKE_CASE_=False	)				->     str:
         __lowerCamelCase   :	List[str]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Tuple								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return image







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="CompVis/stable-diffusion-v1-4"	)				->     Dict:
         __lowerCamelCase   :	Union[str, Any]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		'bf16' if fpaa else None

         __lowerCamelCase				,       __lowerCamelCase   :	str								=		FlaxUNetaDConditionModel.from_pretrained(
             SCREAMING_SNAKE_CASE_			,     subfolder='unet'			,     dtype=SCREAMING_SNAKE_CASE_			,     revision=SCREAMING_SNAKE_CASE_	)
         return model, params







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 77, 7_68)			,     SCREAMING_SNAKE_CASE_=False	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return hidden_states







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
            [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
            [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
            [3, 10_00, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Any								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
            [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
            [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
            [3, 10_00, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		self.get_unet_model(model_id='stabilityai/stable-diffusion-2'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     shape=(4, 4, 96, 96)			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     shape=(4, 77, 10_24)			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Optional[int]								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Tuple								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)






