"""simple docstring"""





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_UpperCamelCase     :		Optional[Any]											=					logging.getLogger(__name__)


class   UpperCAmelCase_			:


      def __init__(     self							) ->      List[str]:
             lowercase__    :	List[Any]     					=			False


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a							) ->      List[str]:
             if not self.initialized:
                    lowercase__    :	str     					=			RagRetriever(
                        a	,    question_encoder_tokenizer=a	,    generator_tokenizer=a	,    index=a	,    init_retrieval=a	,    )
                    lowercase__    :	int     					=			True


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             self.retriever.index.init_index()


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      List[str]:
             lowercase__					,   lowercase__    :	Union[str, Any]     					=			self.retriever._main_retrieve(a	,    a							)
             return doc_ids, retrieved_doc_embeds





class   UpperCAmelCase_			(					_a):


      def __init__(     self	,    a	,    a	,    a	,    a	,    a=None							) ->      Dict:
             if index is not None and index.is_initialized() and len(a							) > 0:
                    raise ValueError(
                        'When using Ray for distributed fine-tuning, '
                        'you\'ll need to provide the paths instead, '
                        'as the dataset and the index are loaded '
                        'separately. More info in examples/rag/use_own_knowledge_dataset.py '							)
             super().__init__(
                 a	,    question_encoder_tokenizer=a	,    generator_tokenizer=a	,    index=a	,    init_retrieval=a	,    )
             lowercase__    :	int     					=			retrieval_workers
             if len(self.retrieval_workers							) > 0:
                    ray.get(
                        [
                            worker.create_rag_retriever.remote(a	,    a	,    a	,    a							)
                            for worker in self.retrieval_workers
                        ]							)


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             logger.info('initializing retrieval'							)

             if len(self.retrieval_workers							) > 0:
                    ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]							)
             else:
                    # Non-distributed training. Load index into this same process.
                    self.index.init_index()


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
             if len(self.retrieval_workers							) > 0:
                    # Select a random retrieval actor.
                    lowercase__    :	List[str]     					=			self.retrieval_workers[random.randint(0	,    len(self.retrieval_workers							) - 1							)]
                    lowercase__					,   lowercase__    :	int     					=			ray.get(random_worker.retrieve.remote(a	,    a							)							)
             else:
                    lowercase__					,   lowercase__    :	List[str]     					=			self._main_retrieve(a	,    a							)
             return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a							)


      @classmethod
      def 	_UpperCAmelCase (     cls	,    a	,    a=None	,    **a							) ->      Optional[int]:
             return super(a	,    cls							).get_tokenizers(a	,    a	,    **a							)


      @classmethod
      def 	_UpperCAmelCase (     cls	,    a	,    a	,    a=None	,    **a							) ->      int:
             lowercase__    :	List[str]     					=			kwargs.pop('config'	,    a							) or RagConfig.from_pretrained(a	,    **a							)
             lowercase__    :	Any     					=			RagTokenizer.from_pretrained(a	,    config=a							)
             lowercase__    :	List[str]     					=			rag_tokenizer.question_encoder
             lowercase__    :	str     					=			rag_tokenizer.generator
             if indexed_dataset is not None:
                    lowercase__    :	Tuple     					=			'custom'
                    lowercase__    :	List[Any]     					=			CustomHFIndex(config.retrieval_vector_size	,    a							)
             else:
                    lowercase__    :	List[Any]     					=			cls._build_index(a							)
             return cls(
                 a	,    question_encoder_tokenizer=a	,    generator_tokenizer=a	,    retrieval_workers=a	,    index=a	,    )


"""simple docstring"""





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


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


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


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


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__					,   lowercase__    :	str     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-canny'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	List[str]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	List[Any]     					=			controlnet_params

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Any     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	Tuple     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			jnp.array(
                 [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Optional[Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([pose_image] * num_samples							)

             lowercase__    :	List[str]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	str     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			jnp.array(
                 [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

