




"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class       a__   ( snake_case__				):
						_a							:   Optional[int] =   (PNDMScheduler,)
						_a							:   Optional[int] =   (("""num_inference_steps""", 5_0),)






						def     __SCREAMING_SNAKE_CASE(       self		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	{
											    "num_train_timesteps": 1_0_0_0,
											    "beta_start": 0.00_01,
											    "beta_end": 0.02,
											    "beta_schedule": "linear",
											}

											config.update(**_A      )
											return config






						def     __SCREAMING_SNAKE_CASE(       self		,      _A=0		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	dict(self.forward_default_kwargs      )
											__lowerCAmelCase           =	kwargs.pop("num_inference_steps"		,      _A      )
											__lowerCAmelCase           =	self.dummy_sample
											__lowerCAmelCase           =	0.1 * sample
											__lowerCAmelCase           =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																__lowerCAmelCase           =	self.get_scheduler_config(**_A      )
																__lowerCAmelCase           =	scheduler_class(**_A      )
																scheduler.set_timesteps(_A      )
																# copy over dummy past residuals
																__lowerCAmelCase           =	dummy_past_residuals[:]

																with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(_A      )
																					__lowerCAmelCase           =	scheduler_class.from_pretrained(_A      )
																					new_scheduler.set_timesteps(_A      )
																					# copy over dummy past residuals
																					__lowerCAmelCase           =	dummy_past_residuals[:]

																__lowerCAmelCase           =	scheduler.step_prk(_A		,      _A		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	new_scheduler.step_prk(_A		,      _A		,      _A		,      **_A      ).prev_sample

																assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

																__lowerCAmelCase           =	scheduler.step_plms(_A		,      _A		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	new_scheduler.step_plms(_A		,      _A		,      _A		,      **_A      ).prev_sample

																assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						def     __SCREAMING_SNAKE_CASE(       self		,      _A=0		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	dict(self.forward_default_kwargs      )
											__lowerCAmelCase           =	kwargs.pop("num_inference_steps"		,      _A      )
											__lowerCAmelCase           =	self.dummy_sample
											__lowerCAmelCase           =	0.1 * sample
											__lowerCAmelCase           =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																__lowerCAmelCase           =	self.get_scheduler_config()
																__lowerCAmelCase           =	scheduler_class(**_A      )
																scheduler.set_timesteps(_A      )

																# copy over dummy past residuals (must be after setting timesteps)
																__lowerCAmelCase           =	dummy_past_residuals[:]

																with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(_A      )
																					__lowerCAmelCase           =	scheduler_class.from_pretrained(_A      )
																					# copy over dummy past residuals
																					new_scheduler.set_timesteps(_A      )

																					# copy over dummy past residual (must be after setting timesteps)
																					__lowerCAmelCase           =	dummy_past_residuals[:]

																__lowerCAmelCase           =	scheduler.step_prk(_A		,      _A		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	new_scheduler.step_prk(_A		,      _A		,      _A		,      **_A      ).prev_sample

																assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

																__lowerCAmelCase           =	scheduler.step_plms(_A		,      _A		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	new_scheduler.step_plms(_A		,      _A		,      _A		,      **_A      ).prev_sample

																assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"






						def     __SCREAMING_SNAKE_CASE(       self		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.scheduler_classes[0]
											__lowerCAmelCase           =	self.get_scheduler_config(**_A      )
											__lowerCAmelCase           =	scheduler_class(**_A      )

											__lowerCAmelCase           =	1_0
											__lowerCAmelCase           =	self.dummy_model()
											__lowerCAmelCase           =	self.dummy_sample_deter
											scheduler.set_timesteps(_A      )

											for i, t in enumerate(scheduler.prk_timesteps      ):
																__lowerCAmelCase           =	model(_A		,      _A      )
																__lowerCAmelCase           =	scheduler.step_prk(_A		,      _A		,      _A      ).prev_sample

											for i, t in enumerate(scheduler.plms_timesteps      ):
																__lowerCAmelCase           =	model(_A		,      _A      )
																__lowerCAmelCase           =	scheduler.step_plms(_A		,      _A		,      _A      ).prev_sample

											return sample






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	dict(self.forward_default_kwargs      )

											__lowerCAmelCase           =	kwargs.pop("num_inference_steps"		,      _A      )

											for scheduler_class in self.scheduler_classes:
																__lowerCAmelCase           =	self.get_scheduler_config()
																__lowerCAmelCase           =	scheduler_class(**_A      )

																__lowerCAmelCase           =	self.dummy_sample
																__lowerCAmelCase           =	0.1 * sample

																if num_inference_steps is not None and hasattr(_A		,      "set_timesteps"      ):
																					scheduler.set_timesteps(_A      )
																elif num_inference_steps is not None and not hasattr(_A		,      "set_timesteps"      ):
																					__lowerCAmelCase           =	num_inference_steps

																# copy over dummy past residuals (must be done after set_timesteps)
																__lowerCAmelCase           =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																__lowerCAmelCase           =	dummy_past_residuals[:]

																__lowerCAmelCase           =	scheduler.step_prk(_A		,      0		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	scheduler.step_prk(_A		,      1		,      _A		,      **_A      ).prev_sample

																self.assertEqual(output_a.shape		,      sample.shape      )
																self.assertEqual(output_a.shape		,      output_a.shape      )

																__lowerCAmelCase           =	scheduler.step_plms(_A		,      0		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	scheduler.step_plms(_A		,      1		,      _A		,      **_A      ).prev_sample

																self.assertEqual(output_a.shape		,      sample.shape      )
																self.assertEqual(output_a.shape		,      output_a.shape      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for timesteps in [1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=_A      )

											__lowerCAmelCase           =	self.scheduler_classes[0]
											__lowerCAmelCase           =	self.get_scheduler_config(steps_offset=1      )
											__lowerCAmelCase           =	scheduler_class(**_A      )
											scheduler.set_timesteps(1_0      )
											assert torch.equal(
											    scheduler.timesteps		,      torch.LongTensor(
											        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]      )		,      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for beta_start, beta_end in zip([0.00_01, 0.0_01]		,      [0.0_02, 0.02]      ):
																self.check_over_configs(beta_start=_A		,      beta_end=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for t in [1, 5, 1_0]:
																self.check_over_forward(time_step=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for t, num_inference_steps in zip([1, 5, 1_0]		,      [1_0, 5_0, 1_0_0]      ):
																self.check_over_forward(num_inference_steps=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	2_7

											for scheduler_class in self.scheduler_classes:
																__lowerCAmelCase           =	self.dummy_sample
																__lowerCAmelCase           =	0.1 * sample

																__lowerCAmelCase           =	self.get_scheduler_config()
																__lowerCAmelCase           =	scheduler_class(**_A      )

																scheduler.set_timesteps(_A      )

																# before power of 3 fix, would error on first step, so we only need to do two
																for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
																					__lowerCAmelCase           =	scheduler.step_prk(_A		,      _A		,      _A      ).prev_sample






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaises(_A      ):
																__lowerCAmelCase           =	self.scheduler_classes[0]
																__lowerCAmelCase           =	self.get_scheduler_config()
																__lowerCAmelCase           =	scheduler_class(**_A      )

																scheduler.step_plms(self.dummy_sample		,      1		,      self.dummy_sample      ).prev_sample






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.full_loop()
											__lowerCAmelCase           =	torch.sum(torch.abs(_A      )      )
											__lowerCAmelCase           =	torch.mean(torch.abs(_A      )      )

											assert abs(result_sum.item() - 1_9_8.1_3_1_8      ) < 1E-2
											assert abs(result_mean.item() - 0.25_80      ) < 1E-3






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.full_loop(prediction_type="v_prediction"      )
											__lowerCAmelCase           =	torch.sum(torch.abs(_A      )      )
											__lowerCAmelCase           =	torch.mean(torch.abs(_A      )      )

											assert abs(result_sum.item() - 67.39_86      ) < 1E-2
											assert abs(result_mean.item() - 0.08_78      ) < 1E-3






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.full_loop(set_alpha_to_one=_A		,      beta_start=0.01      )
											__lowerCAmelCase           =	torch.sum(torch.abs(_A      )      )
											__lowerCAmelCase           =	torch.mean(torch.abs(_A      )      )

											assert abs(result_sum.item() - 2_3_0.0_3_9_9      ) < 1E-2
											assert abs(result_mean.item() - 0.29_95      ) < 1E-3






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.full_loop(set_alpha_to_one=_A		,      beta_start=0.01      )
											__lowerCAmelCase           =	torch.sum(torch.abs(_A      )      )
											__lowerCAmelCase           =	torch.mean(torch.abs(_A      )      )

											assert abs(result_sum.item() - 1_8_6.9_4_8_2      ) < 1E-2
											assert abs(result_mean.item() - 0.24_34      ) < 1E-3










import logging
import random

import ray

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


UpperCamelCase__         =      logging.getLogger(__name__)
class       a__   :






						def __init__(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	False






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											if not self.initialized:
																__lowerCAmelCase           =	RagRetriever(
																    _A		,      question_encoder_tokenizer=_A		,      generator_tokenizer=_A		,      index=_A		,      init_retrieval=_A		,      )
																__lowerCAmelCase           =	True






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.retriever.index.init_index()






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.retriever._main_retrieve(_A		,      _A      )
											return doc_ids, retrieved_doc_embeds






class       a__   ( snake_case__				):






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




											"""simple docstring"""



											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		,      )
											__lowerCAmelCase           =	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     __SCREAMING_SNAKE_CASE(       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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											if len(self.retrieval_workers      ) > 0:
																# Select a random retrieval actor.
																__lowerCAmelCase           =	self.retrieval_workers[random.randint(0		,      len(self.retrieval_workers      ) - 1      )]
																__lowerCAmelCase       ,							__lowerCAmelCase           =	ray.get(random_worker.retrieve.remote(_A		,      _A      )      )
											else:
																__lowerCAmelCase       ,							__lowerCAmelCase           =	self._main_retrieve(_A		,      _A      )
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_A      )






						@classmethod
						def     __SCREAMING_SNAKE_CASE(       cls		,      _A		,      _A=None		,      **_A      ):




											"""simple docstring"""



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






						@classmethod
						def     __SCREAMING_SNAKE_CASE(       cls		,      _A		,      _A		,      _A=None		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	kwargs.pop("config"		,      _A      ) or RagConfig.from_pretrained(_A		,      **_A      )
											__lowerCAmelCase           =	RagTokenizer.from_pretrained(_A		,      config=_A      )
											__lowerCAmelCase           =	rag_tokenizer.question_encoder
											__lowerCAmelCase           =	rag_tokenizer.generator
											if indexed_dataset is not None:
																__lowerCAmelCase           =	"custom"
																__lowerCAmelCase           =	CustomHFIndex(config.retrieval_vector_size		,      _A      )
											else:
																__lowerCAmelCase           =	cls._build_index(_A      )
											return cls(
											    _A		,      question_encoder_tokenizer=_A		,      generator_tokenizer=_A		,      retrieval_workers=_A		,      index=_A		,      )




