



"""simple docstring"""




import copy

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


_lowerCAmelCase							:   Union[str, 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(_UpperCamelCase		)
class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Any						=					'rag'
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					True



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

           from ..auto.configuration_auto import AutoConfig

           _UpperCAmelCase     :      List[str]														=       AutoConfig.for_model(A	,      **A		)
           _UpperCAmelCase     :      Optional[Any]														=       AutoConfig.for_model(A	,      **A		)

           _UpperCAmelCase     :      Optional[Any]														=       reduce_loss
           _UpperCAmelCase     :      Optional[int]														=       label_smoothing
           _UpperCAmelCase     :      Optional[Any]														=       exclude_bos_score
           _UpperCAmelCase     :      Any														=       do_marginalize

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

           _UpperCAmelCase     :      List[str]														=       dataset
           _UpperCAmelCase     :      Tuple														=       dataset_split
           _UpperCAmelCase     :      List[Any]														=       index_name

           _UpperCAmelCase     :      List[Any]														=       retrieval_vector_size
           _UpperCAmelCase     :      Optional[Any]														=       retrieval_batch_size
           _UpperCAmelCase     :      Any														=       passages_path
           _UpperCAmelCase     :      Tuple														=       index_path
           _UpperCAmelCase     :      Optional[Any]														=       use_dummy_dataset

           _UpperCAmelCase     :      List[Any]														=       output_retrieved

           _UpperCAmelCase     :      Any														=       do_deduplication

           _UpperCAmelCase     :      str														=       use_cache

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



      @classmethod
      def 		snake_case_   (	cls	:      Union[str, Any]	,      A	:      PretrainedConfig	,      A	:      PretrainedConfig	,      **A	:      Optional[Any]		):
           return cls(question_encoder=question_encoder_config.to_dict()	,      generator=generator_config.to_dict()	,      **A		)



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      int														=       copy.deepcopy(self.__dict__		)
           _UpperCAmelCase     :      Optional[Any]														=       self.question_encoder.to_dict()
           _UpperCAmelCase     :      List[Any]														=       self.generator.to_dict()
           _UpperCAmelCase     :      str														=       self.__class__.model_type
           return output





"""simple docstring"""




import os
import tempfile
import unittest

import numpy as np

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


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard

 from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline






@require_flax
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Optional[int]		):
           with tempfile.TemporaryDirectory() as tmpdirname:
                # pipeline has Flax weights
                _UpperCAmelCase     :      Union[str, Any]														=       FlaxDiffusionPipeline.from_pretrained(
                    "hf-internal-testing/tiny-stable-diffusion-pipe"	,      safety_checker=A	,      cache_dir=A		)

                _UpperCAmelCase     :      List[str]														=       [t[-1] for t in os.walk(os.path.join(A	,      os.listdir(A		)[0]	,      "snapshots"		)		)]
                _UpperCAmelCase     :      Union[str, Any]														=       [item for sublist in all_root_files for item in sublist]

                # None of the downloaded files should be a PyTorch file even if we have some here:
                # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
                assert not any(f.endswith(".bin"		) for f in files		)







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



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       FlaxStableDiffusionPipeline.from_pretrained(
               "hf-internal-testing/tiny-stable-diffusion-pipe"	,      safety_checker=A		)

           _UpperCAmelCase     :      str														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      int														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      List[str]														=       4

           _UpperCAmelCase     :      Any														=       jax.device_count()
           _UpperCAmelCase     :      Tuple														=       num_samples * [prompt]
           _UpperCAmelCase     :      Optional[int]														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      Union[str, Any]														=       replicate(A		)
           _UpperCAmelCase     :      List[Any]														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      List[Any]														=       shard(A		)

           _UpperCAmelCase     :      Tuple														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
           if jax.device_count() == 8:
                assert np.abs(np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 4.1_514_745		) < 1e-3
                assert np.abs(np.abs(A	,      dtype=np.floataa		).sum() - 49_947.875		) < 5e-1

           _UpperCAmelCase     :      Optional[int]														=       pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
           assert len(A		) == num_samples



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="flax"	,      safety_checker=A		)

           _UpperCAmelCase     :      Dict														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      Dict														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      str														=       5_0

           _UpperCAmelCase     :      List[str]														=       jax.device_count()
           _UpperCAmelCase     :      Any														=       num_samples * [prompt]
           _UpperCAmelCase     :      Dict														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      Dict														=       replicate(A		)
           _UpperCAmelCase     :      str														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      Optional[Any]														=       shard(A		)

           _UpperCAmelCase     :      List[Any]														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.05_652_401)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_383_808.2)		) < 5e-1



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      safety_checker=A		)

           _UpperCAmelCase     :      Any														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      List[Any]														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      List[Any]														=       5_0

           _UpperCAmelCase     :      Union[str, Any]														=       jax.device_count()
           _UpperCAmelCase     :      Optional[int]														=       num_samples * [prompt]
           _UpperCAmelCase     :      Union[str, Any]														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      int														=       replicate(A		)
           _UpperCAmelCase     :      Optional[int]														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      str														=       shard(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.04_003_906)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_373_516.75)		) < 5e-1



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa		)

           _UpperCAmelCase     :      str														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      Optional[int]														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      Dict														=       5_0

           _UpperCAmelCase     :      Union[str, Any]														=       jax.device_count()
           _UpperCAmelCase     :      str														=       num_samples * [prompt]
           _UpperCAmelCase     :      int														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      Optional[int]														=       replicate(A		)
           _UpperCAmelCase     :      Tuple														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      Dict														=       shard(A		)

           _UpperCAmelCase     :      Optional[Any]														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.04_003_906)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_373_516.75)		) < 5e-1



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       FlaxDDIMScheduler(
               beta_start=0.00_085	,      beta_end=0.012	,      beta_schedule="scaled_linear"	,      set_alpha_to_one=A	,      steps_offset=1	,      )

           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      scheduler=A	,      safety_checker=A	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       scheduler.create_state()

           _UpperCAmelCase     :      str														=       scheduler_state

           _UpperCAmelCase     :      Union[str, Any]														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      str														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      List[str]														=       5_0

           _UpperCAmelCase     :      Optional[int]														=       jax.device_count()
           _UpperCAmelCase     :      int														=       num_samples * [prompt]
           _UpperCAmelCase     :      Optional[int]														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      List[Any]														=       replicate(A		)
           _UpperCAmelCase     :      Tuple														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      Union[str, Any]														=       shard(A		)

           _UpperCAmelCase     :      Dict														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.045_043_945)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_347_693.5)		) < 5e-1



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Optional[Any]														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      Optional[Any]														=       jax.device_count()
           _UpperCAmelCase     :      Union[str, Any]														=       num_samples * [prompt]
           _UpperCAmelCase     :      Optional[Any]														=       jax.random.split(jax.random.PRNGKey(0		)	,      A		)

           _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      safety_checker=A	,      )

           _UpperCAmelCase     :      int														=       replicate(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       pipeline.prepare_inputs(A		)
           _UpperCAmelCase     :      int														=       shard(A		)
           _UpperCAmelCase     :      Dict														=       pipeline(A	,      A	,      A	,      jit=A		).images
           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           _UpperCAmelCase     :      List[str]														=       images[2, 0, 2_5_6, 1_0:1_7, 1]

           # With memory efficient attention
           _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      safety_checker=A	,      use_memory_efficient_attention=A	,      )

           _UpperCAmelCase     :      Any														=       replicate(A		)
           _UpperCAmelCase     :      str														=       pipeline.prepare_inputs(A		)
           _UpperCAmelCase     :      Tuple														=       shard(A		)
           _UpperCAmelCase     :      Tuple														=       pipeline(A	,      A	,      A	,      jit=A		).images
           assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           _UpperCAmelCase     :      str														=       images[2, 0, 2_5_6, 1_0:1_7, 1]

           # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
           # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
           assert abs(slice_eff - slice		).max() < 1e-2
