
import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


_lowercase											=    logging.getLogger(__name__)


class 			lowercase_		(      A       ):


       def __init__(    self						,       __A						,       __A						,       __A						,       __A=None				)						->    List[Any]:
              super().__init__(
                  __A						,       question_encoder_tokenizer=__A						,       generator_tokenizer=__A						,       index=__A						,       init_retrieval=__A						,       )
              SCREAMING_SNAKE_CASE_							:				int		=None


       def 	_snake_case				(    self						,       __A				)						->    Any:
              logger.info('''initializing retrieval'''				)

              # initializing a separate process group for retrieval as the default
              # nccl backend doesn't support gather/scatter operations while gloo
              # is too slow to replace nccl for the core gpu communication
              if dist.is_initialized():
                     logger.info('''dist initialized'''				)
                     # needs to be set manually
                     SCREAMING_SNAKE_CASE_							:				Dict		=self._infer_socket_ifname()
                     # avoid clash with the NCCL port
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=str(distributed_port + 1				)
                     SCREAMING_SNAKE_CASE_							:				Any		=dist.new_group(ranks=__A						,       backend='''gloo'''				)

              # initialize retriever only on the main worker
              if not dist.is_initialized() or self._is_main():
                     logger.info('''dist not initialized / main'''				)
                     self.index.init_index()

              # all processes wait untill the retriever is initialized by the main process
              if dist.is_initialized():
                     torch.distributed.barrier(group=self.process_group				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return dist.get_rank(group=self.process_group				) == 0


       def 	_snake_case				(    self						,       __A						,       __A						,       __A=torch.floataa				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.empty(__A						,       dtype=__A				)
              dist.scatter(__A						,       src=0						,       scatter_list=__A						,       group=self.process_group				)
              return target_tensor


       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				List[str]		=psutil.net_if_addrs()
              # a hacky way to deal with varying network interface names
              SCREAMING_SNAKE_CASE_							:				Any		=next((addr for addr in addrs if addr.startswith('''e'''				))						,       __A				)
              return ifname





       def 	_snake_case				(    self						,       __A						,       __A				)						->    Tuple[np.ndarray, List[dict]]:

              # single GPU training
              if not dist.is_initialized():
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=self._main_retrieve(__A						,       __A				)
                     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__A				)

              # distributed training
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=dist.get_world_size(group=self.process_group				)

              # gather logic
              SCREAMING_SNAKE_CASE_							:				Tuple		=None
              if self._is_main():
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[torch.empty(question_hidden_states.shape						,       dtype=torch.floataa				) for _ in range(__A				)]
              dist.gather(torch.tensor(__A				)						,       dst=0						,       gather_list=__A						,       group=self.process_group				)

              # scatter logic
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=question_hidden_states.shape[0]
              SCREAMING_SNAKE_CASE_							:				str		=[]
              SCREAMING_SNAKE_CASE_							:				List[str]		=[]
              if self._is_main():
                     assert len(__A				) == world_size
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self._main_retrieve(torch.cat(__A				).numpy()						,       __A				)
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor(__A				), torch.tensor(__A				)
                     SCREAMING_SNAKE_CASE_							:				str		=self._chunk_tensor(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=self._chunk_tensor(__A						,       __A				)
              SCREAMING_SNAKE_CASE_							:				int		=self._scattered(__A						,       [n_queries, n_docs]						,       target_type=torch.intaa				)
              SCREAMING_SNAKE_CASE_							:				str		=self._scattered(__A						,       [n_queries, n_docs, question_hidden_states.shape[1]]				)

              return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__A				)


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DPMSolverSDEScheduler,)
       __lowerCamelCase				       =						1_0


       def 	_snake_case				(    self						,       **__A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''noise_sampler_seed''': 0,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self				)						->    Any:
              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 0.002, 0.02]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				int		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Tuple		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				str		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.47_821_044_921_875				) < 1e-2
                     assert abs(result_mean.item() - 0.2_178_705_964_565_277				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_352_111_816_406				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_906_892_299_652				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_scheduler_config(prediction_type='''v_prediction'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				Tuple		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				str		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 124.77_149_200_439_453				) < 1e-2
                     assert abs(result_mean.item() - 0.16_226_289_014_816_284				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 128.1_663_360_595_703				) < 1e-2
                     assert abs(result_mean.item() - 0.16_688_326_001_167_297				) < 1e-3
              else:
                     assert abs(result_sum.item() - 119.8_487_548_828_125				) < 1e-2
                     assert abs(result_mean.item() - 0.1_560_530_662_536_621				) < 1e-3


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps						,       device=__A				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.46_957_397_460_938				) < 1e-2
                     assert abs(result_mean.item() - 0.21_805_934_607_982_635				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_353_637_695_312				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_908_382_415_771				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A						,       use_karras_sigmas=__A				)

              scheduler.set_timesteps(self.num_inference_steps						,       device=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				str		=sample.to(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				int		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 176.66_974_135_742_188				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 177.63_653_564_453_125				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              else:
                     assert abs(result_sum.item() - 170.3_135_223_388_672				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
