'''simple docstring'''






import logging
import random

import ray

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


_UpperCAmelCase    :     int         =       logging.getLogger(__name__)







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Tuple     )  ->			int:
        _A    		=  False
   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[Any]     )  ->			Any:
        if not self.initialized:
             _A    		=  RagRetriever(
                 UpperCamelCase__, question_encoder_tokenizer=UpperCamelCase__, generator_tokenizer=UpperCamelCase__, index=UpperCamelCase__, init_retrieval=UpperCamelCase__, )
             _A    		=  True
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        self.retriever.index.init_index()

   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Dict     )  ->			Optional[Any]:
        _A		,       _A    		=  self.retriever._main_retrieve(UpperCamelCase__, UpperCamelCase__     )
        return doc_ids, retrieved_doc_embeds







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def __init__(      self      :			Optional[Any], UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			int=None     )  ->			Optional[int]:
        if index is not None and index.is_initialized() and len(UpperCamelCase__     ) > 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__(
            UpperCamelCase__, question_encoder_tokenizer=UpperCamelCase__, generator_tokenizer=UpperCamelCase__, index=UpperCamelCase__, init_retrieval=UpperCamelCase__, )
        _A    		=  retrieval_workers
        if len(self.retrieval_workers     ) > 0:
             ray.get(
                 [
                     worker.create_rag_retriever.remote(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     )
                     for worker in self.retrieval_workers
                 ]     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        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      :			Optional[Any], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Tuple     )  ->			Dict:
        if len(self.retrieval_workers     ) > 0:
             # Select a random retrieval actor.
             _A    		=  self.retrieval_workers[random.randint(0, len(self.retrieval_workers     ) - 1     )]
             _A		,       _A    		=  ray.get(random_worker.retrieve.remote(UpperCamelCase__, UpperCamelCase__     )     )
        else:
             _A		,       _A    		=  self._main_retrieve(UpperCamelCase__, UpperCamelCase__     )
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase__     )
   @classmethod
   def 	__UpperCAmelCase   (      cls      :			List[str], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			List[Any]=None, **UpperCamelCase__      :			Optional[int]     )  ->			Optional[Any]:
        return super(UpperCamelCase__, cls     ).get_tokenizers(UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     )

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









import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference



class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= ShapEPipeline
	A    							= ['''prompt''']
	A    							= ['''prompt''']
	A    							= [
	    '''num_images_per_prompt''',
	    '''num_inference_steps''',
	    '''generator''',
	    '''latents''',
	    '''guidance_scale''',
	    '''frame_size''',
	    '''output_type''',
	    '''return_dict''',
	]
	A    							= False





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				return 32





	@property
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				return 32





	@property
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				return self.time_input_dim * 4





	@property
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				return 8





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



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





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						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=37      ,     layer_norm_eps=1e-05      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     pad_token_id=1      ,     vocab_size=1000      ,     )
				return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE      )





	@property
	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				torch.manual_seed(0      )

				SCREAMING_SNAKE_CASE_								=						{
				    'num_attention_heads': 2,
				    'attention_head_dim': 16,
				    'embedding_dim': self.time_input_dim,
				    'num_embeddings': 32,
				    'embedding_proj_dim': self.text_embedder_hidden_size,
				    'time_embed_dim': self.time_embed_dim,
				    'num_layers': 1,
				    'clip_embed_dim': self.time_input_dim * 2,
				    'additional_embeddings': 0,
				    'time_embed_act_fn': 'gelu',
				    'norm_in_type': 'layer',
				    'encoder_hid_proj_type': None,
				    'added_emb_type': None,
				}

				SCREAMING_SNAKE_CASE_								=						PriorTransformer(**SCREAMING_SNAKE_CASE      )
				return model





	@property
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				torch.manual_seed(0      )

				SCREAMING_SNAKE_CASE_								=						{
				    'param_shapes': (
				        (self.renderer_dim, 93),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    'd_latent': self.time_input_dim,
				    'd_hidden': self.renderer_dim,
				    'n_output': 12,
				    'background': (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				SCREAMING_SNAKE_CASE_								=						ShapERenderer(**SCREAMING_SNAKE_CASE      )
				return model





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.dummy_prior
				SCREAMING_SNAKE_CASE_								=						self.dummy_text_encoder
				SCREAMING_SNAKE_CASE_								=						self.dummy_tokenizer
				SCREAMING_SNAKE_CASE_								=						self.dummy_renderer

				SCREAMING_SNAKE_CASE_								=						HeunDiscreteScheduler(
				    beta_schedule='exp'      ,     num_train_timesteps=1024      ,     prediction_type='sample'      ,     use_karras_sigmas=SCREAMING_SNAKE_CASE      ,     clip_sample=SCREAMING_SNAKE_CASE      ,     clip_sample_range=1.0      ,     )
				SCREAMING_SNAKE_CASE_								=						{
				    'prior': prior,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'renderer': renderer,
				    'scheduler': scheduler,
				}

				return components





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=0      )  ->					str:




				"""simple docstring"""



				if str(SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
							SCREAMING_SNAKE_CASE_								=						torch.manual_seed(SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						{
				    'prompt': 'horse',
				    'generator': generator,
				    'num_inference_steps': 1,
				    'frame_size': 32,
				    'output_type': 'np',
				}
				return inputs





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'cpu'

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()

				SCREAMING_SNAKE_CASE_								=						self.pipeline_class(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe.to(SCREAMING_SNAKE_CASE      )

				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )      )
				SCREAMING_SNAKE_CASE_								=						output.images[0]
				SCREAMING_SNAKE_CASE_								=						image[0, -3:, -3:, -1]

				assert image.shape == (20, 32, 32, 3)

				SCREAMING_SNAKE_CASE_								=						np.array(
				    [
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				    ]      )

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





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				self._test_inference_batch_consistent(batch_sizes=[1, 2]      )





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						torch_device == 'cpu'
				SCREAMING_SNAKE_CASE_								=						True

				self._test_inference_batch_single_identical(
				    batch_size=2      ,     test_max_difference=SCREAMING_SNAKE_CASE      ,     relax_max_difference=SCREAMING_SNAKE_CASE      ,     )





	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()
				SCREAMING_SNAKE_CASE_								=						self.pipeline_class(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						1
				SCREAMING_SNAKE_CASE_								=						2

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )

				for key in inputs.keys():
							if key in self.batch_params:
										SCREAMING_SNAKE_CASE_								=						batch_size * [inputs[key]]

				SCREAMING_SNAKE_CASE_								=						pipe(**SCREAMING_SNAKE_CASE      ,     num_images_per_prompt=SCREAMING_SNAKE_CASE      )[0]

				assert images.shape[0] == batch_size * num_images_per_prompt




@slow
@require_torch_gpu
class       a_       (     unittest.TestCase     ):





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/shap_e/test_shap_e_np_out.npy'      )
				SCREAMING_SNAKE_CASE_								=						ShapEPipeline.from_pretrained('openai/shap-e'      )
				SCREAMING_SNAKE_CASE_								=						pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(0      )

				SCREAMING_SNAKE_CASE_								=						pipe(
				    'a shark'      ,     generator=SCREAMING_SNAKE_CASE      ,     guidance_scale=1_5.0      ,     num_inference_steps=64      ,     frame_size=64      ,     output_type='np'      ,     ).images[0]

				assert images.shape == (20, 64, 64, 3)

				assert_mean_pixel_difference(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
