






"""simple docstring"""
import logging
import random

import ray

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


A_     :					Dict							=     logging.getLogger(__name__)



class 						a_ :







      '''simple docstring'''

      def __init__(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		False




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       if not self.initialized:
        lowerCamelCase__	:     int								=		RagRetriever(
            lowerCamelCase_,	question_encoder_tokenizer=lowerCamelCase_,	generator_tokenizer=lowerCamelCase_,	index=lowerCamelCase_,	init_retrieval=lowerCamelCase_,	)
        lowerCamelCase__	:     Any								=		True




      def      a__				(self   ):

       '''simple docstring'''



       self.retriever.index.init_index()




      def      a__				(self   ):

       '''simple docstring'''



       del self.retriever
       lowerCamelCase__	:     Optional[int]								=		False





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__							,	lowerCamelCase__	:     int								=		self.retriever._main_retrieve(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     Tuple								=		self.retriever.index.get_doc_dicts(lowerCamelCase_   )
       return doc_ids, retrieved_doc_embeds, doc_dicts



class 						a_ (      snake_case_					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=None   ):

       '''simple docstring'''



       if index is not None and index.is_initialized() and len(lowerCamelCase_   ) > 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__(
           lowerCamelCase_,	question_encoder_tokenizer=lowerCamelCase_,	generator_tokenizer=lowerCamelCase_,	index=lowerCamelCase_,	init_retrieval=lowerCamelCase_,	)

       lowerCamelCase__	:     Tuple								=		retrieval_workers
       lowerCamelCase__	:     Union[str, Any]								=		question_encoder_tokenizer
       lowerCamelCase__	:     str								=		generator_tokenizer
       if len(self.retrieval_workers   ) > 0:
        ray.get(
            [
                worker.create_rag_retriever.remote(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   )
                for worker in self.retrieval_workers
            ]   )




      def      a__				(self   ):

       '''simple docstring'''



       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      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       if len(self.retrieval_workers   ) > 0:
        # Select a random retrieval actor.
        lowerCamelCase__	:     Dict								=		self.retrieval_workers[random.randint(0,	len(self.retrieval_workers   ) - 1   )]
        lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__	:     Dict								=		ray.get(
            random_worker.retrieve.remote(lowerCamelCase_,	lowerCamelCase_   )   )
       else:
        lowerCamelCase__							,	lowerCamelCase__	:     Optional[int]								=		self._main_retrieve(lowerCamelCase_,	lowerCamelCase_   )
        lowerCamelCase__	:     Tuple								=		self.index.get_doc_dicts(lowerCamelCase_   )
       return retrieved_doc_embeds, doc_ids, doc_dicts




      @classmethod
      def      a__				(cls,	lowerCamelCase_,	lowerCamelCase_=None,	**lowerCamelCase_   ):

       '''simple docstring'''



       return super(lowerCamelCase_,	cls   ).get_tokenizers(lowerCamelCase_,	lowerCamelCase_,	**lowerCamelCase_   )




      @classmethod
      def      a__				(cls,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=None,	**lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     int								=		kwargs.pop('config',	lowerCamelCase_   ) or RagConfig.from_pretrained(lowerCamelCase_,	**lowerCamelCase_   )
       lowerCamelCase__	:     int								=		RagTokenizer.from_pretrained(lowerCamelCase_,	config=lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		rag_tokenizer.question_encoder
       lowerCamelCase__	:     List[str]								=		rag_tokenizer.generator

       if indexed_dataset is not None:
        lowerCamelCase__	:     List[Any]								=		'custom'
        lowerCamelCase__	:     int								=		CustomHFIndex(config.retrieval_vector_size,	lowerCamelCase_   )
       else:
        lowerCamelCase__	:     Union[str, Any]								=		cls._build_index(lowerCamelCase_   )

       return cls(
           lowerCamelCase_,	question_encoder_tokenizer=lowerCamelCase_,	generator_tokenizer=lowerCamelCase_,	retrieval_workers=lowerCamelCase_,	index=lowerCamelCase_,	)





      def      a__				(self   ):

       '''simple docstring'''



       logger.info('re-loading the new dataset with embeddings'   )
       # access from the training loop

       ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]   )

       # build the index object again
       lowerCamelCase__	:     Dict								=		self._build_index(self.config   )

       ray.get(
           [
               worker.create_rag_retriever.remote(
                   self.config,	self.question_encoder_tokenizer,	self.generator_tokenizer,	lowerCamelCase_   )
               for worker in self.retrieval_workers
           ]   )









"""simple docstring"""
import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 						a_ (      snake_case_    ,		unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							Optional[Any]					=      KandinskyPriorPipeline
      lowerCamelCase__			:							Any					=      ['prompt']
      lowerCamelCase__			:							Any					=      ['prompt', 'negative_prompt']
      lowerCamelCase__			:							str					=      [
          'num_images_per_prompt',
          'generator',
          'num_inference_steps',
          'latents',
          'negative_prompt',
          'guidance_scale',
          'output_type',
          'return_dict',
      ]
      lowerCamelCase__			:							Dict					=      False




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return 3_2




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return 3_2




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return self.time_input_dim




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return self.time_input_dim * 4




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return 1_0_0




      @property
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )
       return tokenizer




      @property
      def      a__				(self   ):

       '''simple docstring'''



       torch.manual_seed(0   )
       lowerCamelCase__	:     int								=		CLIPTextConfig(
           bos_token_id=0,	eos_token_id=2,	hidden_size=self.text_embedder_hidden_size,	projection_dim=self.text_embedder_hidden_size,	intermediate_size=3_7,	layer_norm_eps=1e-05,	num_attention_heads=4,	num_hidden_layers=5,	pad_token_id=1,	vocab_size=1_0_0_0,	)
       return CLIPTextModelWithProjection(lowerCamelCase_   )




      @property
      def      a__				(self   ):

       '''simple docstring'''



       torch.manual_seed(0   )

       lowerCamelCase__	:     Optional[int]								=		{
           'num_attention_heads': 2,
           'attention_head_dim': 1_2,
           'embedding_dim': self.text_embedder_hidden_size,
           'num_layers': 1,
       }

       lowerCamelCase__	:     Optional[int]								=		PriorTransformer(**lowerCamelCase_   )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       lowerCamelCase__	:     Dict								=		nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model




      @property
      def      a__				(self   ):

       '''simple docstring'''



       torch.manual_seed(0   )
       lowerCamelCase__	:     List[str]								=		CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size,	image_size=2_2_4,	projection_dim=self.text_embedder_hidden_size,	intermediate_size=3_7,	num_attention_heads=4,	num_channels=3,	num_hidden_layers=5,	patch_size=1_4,	)

       lowerCamelCase__	:     int								=		CLIPVisionModelWithProjection(lowerCamelCase_   )
       return model




      @property
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		CLIPImageProcessor(
           crop_size=2_2_4,	do_center_crop=lowerCamelCase_,	do_normalize=lowerCamelCase_,	do_resize=lowerCamelCase_,	image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],	image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],	resample=3,	size=2_2_4,	)

       return image_processor




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.dummy_prior
       lowerCamelCase__	:     Optional[Any]								=		self.dummy_image_encoder
       lowerCamelCase__	:     Optional[Any]								=		self.dummy_text_encoder
       lowerCamelCase__	:     Union[str, Any]								=		self.dummy_tokenizer
       lowerCamelCase__	:     List[Any]								=		self.dummy_image_processor

       lowerCamelCase__	:     List[Any]								=		UnCLIPScheduler(
           variance_type='fixed_small_log',	prediction_type='sample',	num_train_timesteps=1_0_0_0,	clip_sample=lowerCamelCase_,	clip_sample_range=10.0,	)

       lowerCamelCase__	:     Union[str, Any]								=		{
           'prior': prior,
           'image_encoder': image_encoder,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'scheduler': scheduler,
           'image_processor': image_processor,
       }

       return components




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=0   ):

       '''simple docstring'''



       if str(lowerCamelCase_   ).startswith('mps'   ):
        lowerCamelCase__	:     List[Any]								=		torch.manual_seed(lowerCamelCase_   )
       else:
        lowerCamelCase__	:     Optional[int]								=		torch.Generator(device=lowerCamelCase_   ).manual_seed(lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		{
           'prompt': 'horse',
           'generator': generator,
           'guidance_scale': 4.0,
           'num_inference_steps': 2,
           'output_type': 'np',
       }
       return inputs




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		'cpu'

       lowerCamelCase__	:     str								=		self.get_dummy_components()

       lowerCamelCase__	:     Any								=		self.pipeline_class(**lowerCamelCase_   )
       lowerCamelCase__	:     Dict								=		pipe.to(lowerCamelCase_   )

       pipe.set_progress_bar_config(disable=lowerCamelCase_   )

       lowerCamelCase__	:     Tuple								=		pipe(**self.get_dummy_inputs(lowerCamelCase_   )   )
       lowerCamelCase__	:     Dict								=		output.image_embeds

       lowerCamelCase__	:     Optional[Any]								=		pipe(
           **self.get_dummy_inputs(lowerCamelCase_   ),	return_dict=lowerCamelCase_,	)[0]

       lowerCamelCase__	:     Dict								=		image[0, -1_0:]
       lowerCamelCase__	:     Tuple								=		image_from_tuple[0, -1_0:]

       assert image.shape == (1, 3_2)

       lowerCamelCase__	:     Dict								=		np.array(
           [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2




      @skip_mps
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		torch_device == 'cpu'
       lowerCamelCase__	:     Any								=		True
       lowerCamelCase__	:     List[Any]								=		False

       self._test_inference_batch_single_identical(
           test_max_difference=lowerCamelCase_,	relax_max_difference=lowerCamelCase_,	test_mean_pixel_difference=lowerCamelCase_,	)





      @skip_mps
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		torch_device == 'cpu'
       lowerCamelCase__	:     int								=		False

       self._test_attention_slicing_forward_pass(
           test_max_difference=lowerCamelCase_,	test_mean_pixel_difference=lowerCamelCase_,	)

