import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				if isinstance(lowercase							,      (list, tuple)							):
								return honor_type(lowercase							,      (move_to_device(lowercase							,      lowercase							) for t in state)							)
				elif isinstance(lowercase							,      lowercase							):
								return type(lowercase							)({k: move_to_device(lowercase							,      lowercase							) for k, v in state.items()}							)
				elif isinstance(lowercase							,      torch.Tensor							):
								return state.to(lowercase							)
				return state




class        _UpperCamelCase				(						torch.optim.Optimizer			):


			"""simple docstring"""






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

							'''simple docstring'''


							__lowercase			      =							optimizer
							__lowercase			      =							scaler
							__lowercase			      =							AcceleratorState()
							__lowercase			      =							GradientState()
							__lowercase			      =							device_placement
							__lowercase			      =							False
							__lowercase			      =							None

							# Handle device placement
							if device_placement:
											__lowercase			      =							self.optimizer.state_dict()
											if self.accelerator_state.distributed_type == DistributedType.TPU:
															xm.send_cpu_data_to_device(lowerCAmelCase__				,       self.accelerator_state.device						)
											else:
															__lowercase			      =							move_to_device(lowerCAmelCase__				,       self.accelerator_state.device						)
											self.optimizer.load_state_dict(lowerCAmelCase__						)





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							return self.optimizer.state





			@state.setter
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							__lowercase			      =							state





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							return self.optimizer.param_groups





			@param_groups.setter
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							param_groups





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							return self.optimizer.defaults





			@defaults.setter
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							defaults





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							self.optimizer.add_param_group(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
											xm.send_cpu_data_to_device(lowerCAmelCase__				,       self.accelerator_state.device						)
							self.optimizer.load_state_dict(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							return self.optimizer.state_dict()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=None						)					-> int:

							'''simple docstring'''


							if self.gradient_state.sync_gradients:
											__lowercase			      =							'''set_to_none''' in inspect.signature(self.optimizer.zero_grad						).parameters
											if accept_arg:
															if set_to_none is None:
																			__lowercase			      =							False
															self.optimizer.zero_grad(set_to_none=lowerCAmelCase__						)
											else:
															if set_to_none is not None:
																			raise ValueError('''`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'''						)
															self.optimizer.zero_grad()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=None						)					-> str:

							'''simple docstring'''


							if self.gradient_state.sync_gradients:
											if self.accelerator_state.distributed_type == DistributedType.TPU:
															__lowercase			      =							{'''closure''': closure} if closure is not None else {}
															xm.optimizer_step(self.optimizer				,       optimizer_args=lowerCAmelCase__						)
											elif self.scaler is not None:
															__lowercase			      =							False
															if self._last_scale is None:
																			# `get_scale` is an async operation requiring full synchronization
																			# on CPU and GPUs before finishing. As a result, we store away
																			# the prior one to reduce the call overhead
																			__lowercase			      =							self.scaler.get_scale()
																			__lowercase			      =							True
															self.scaler.step(self.optimizer				,       lowerCAmelCase__						)
															self.scaler.update()
															__lowercase			      =							self.scaler.get_scale()
															if not new_scale:
																			# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																			__lowercase			      =							scale_after < self._last_scale
															__lowercase			      =							scale_after

											else:
															self.optimizer.step(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							for param_group in self.optimizer.param_groups:
											__lowercase			      =							[parameters_map.get(lowerCAmelCase__				,       lowerCAmelCase__						) for p in param_group['''params''']]





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

							'''simple docstring'''


							warnings.warn(
							    '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
							    '''`optimizer.step_was_skipped` instead.'''				,       lowerCAmelCase__				,       )
							return self._is_overflow





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

							'''simple docstring'''


							return self._is_overflow





			def __getstate__(       self						)					-> Optional[int]:

							'''simple docstring'''


							return self.__dict__.copy()





			def __setstate__(       self				,       lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							self.__dict__.update(lowerCAmelCase__						)
import logging
import random

import ray

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


__a  :					Optional[Any]          =  logging.getLogger(__name__)




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							False





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							self.retriever.index.init_index()





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

							'''simple docstring'''


							del self.retriever
							__lowercase			      =							False





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

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							self.retriever._main_retrieve(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							self.retriever.index.get_doc_dicts(lowerCAmelCase__						)
							return doc_ids, retrieved_doc_embeds, doc_dicts




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






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

							'''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__				,       )

							__lowercase			      =							retrieval_workers
							__lowercase			      =							question_encoder_tokenizer
							__lowercase			      =							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 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''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				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


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





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


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





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							kwargs.pop('''config'''				,       lowerCAmelCase__						) or RagConfig.from_pretrained(lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							RagTokenizer.from_pretrained(lowerCAmelCase__				,       config=lowerCAmelCase__						)
							__lowercase			      =							rag_tokenizer.question_encoder
							__lowercase			      =							rag_tokenizer.generator

							if indexed_dataset is not None:
											__lowercase			      =							'''custom'''
											__lowercase			      =							CustomHFIndex(config.retrieval_vector_size				,       lowerCAmelCase__						)
							else:
											__lowercase			      =							cls._build_index(lowerCAmelCase__						)

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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''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
							__lowercase			      =							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
							    ]						)