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


__a  :     str       =  logging.getLogger(__name__)




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=None						)     ->					Optional[int]:

							'''simple docstring'''


							super().__init__(
							    lowerCAmelCase__      ,   question_encoder_tokenizer=lowerCAmelCase__      ,   generator_tokenizer=lowerCAmelCase__      ,   index=lowerCAmelCase__      ,   init_retrieval=lowerCAmelCase__      ,   )
							__lowercase 			=							None





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					Tuple:

							'''simple docstring'''


							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
											__lowercase 			=							self._infer_socket_ifname()
											# avoid clash with the NCCL port
											__lowercase 			=							str(distributed_port + 1						)
											__lowercase 			=							dist.new_group(ranks=lowerCAmelCase__      ,   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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							return dist.get_rank(group=self.process_group						) == 0





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=torch.floataa						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							torch.empty(lowerCAmelCase__      ,   dtype=lowerCAmelCase__						)
							dist.scatter(lowerCAmelCase__      ,   src=0      ,   scatter_list=lowerCAmelCase__      ,   group=self.process_group						)
							return target_tensor





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							psutil.net_if_addrs()
							# a hacky way to deal with varying network interface names
							__lowercase 			=							next((addr for addr in addrs if addr.startswith('''e'''						))      ,   lowerCAmelCase__						)
							return ifname





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

							'''simple docstring'''


							if not dist.is_initialized():
											__lowercase			,     __lowercase 			=							self._main_retrieve(lowerCAmelCase__      ,   lowerCAmelCase__						)
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCAmelCase__						)

							# distributed training
							__lowercase 			=							dist.get_world_size(group=self.process_group						)

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

							# scatter logic
							__lowercase 			=							question_hidden_states.shape[0]
							__lowercase 			=							[]
							__lowercase 			=							[]
							if self._is_main():
											assert len(lowerCAmelCase__						) == world_size
											__lowercase			,     __lowercase 			=							self._main_retrieve(torch.cat(lowerCAmelCase__						).numpy()      ,   lowerCAmelCase__						)
											__lowercase			,     __lowercase 			=							torch.tensor(lowerCAmelCase__						), torch.tensor(lowerCAmelCase__						)
											__lowercase 			=							self._chunk_tensor(lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							self._chunk_tensor(lowerCAmelCase__      ,   lowerCAmelCase__						)
							__lowercase 			=							self._scattered(lowerCAmelCase__      ,   [n_queries, n_docs]      ,   target_type=torch.intaa						)
							__lowercase 			=							self._scattered(lowerCAmelCase__      ,   [n_queries, n_docs, question_hidden_states.shape[1]]						)

							return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(lowerCAmelCase__						)
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						(KDPMaDiscreteScheduler,)
			__a  : Dict			 =						10





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							{
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**lowerCAmelCase__						)
							return config





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

							'''simple docstring'''


							for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]      ,   [0.0002, 0.002, 0.02]						):
											self.check_over_configs(beta_start=lowerCAmelCase__      ,   beta_end=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config(prediction_type='''v_prediction'''						)
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07						) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07						) < 1E-2
											assert abs(result_mean.item() - 0.0002						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

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

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter.to(lowerCAmelCase__						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if str(lowerCAmelCase__						).startswith('''cpu'''						):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3