




'''simple docstring'''






import logging
import random

import ray

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


lowercase						:   Any      						=							logging.getLogger(__name__)

class 	SCREAMING_SNAKE_CASE_		:



 def __init__(	self			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   False



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A ,		A			):






        '''simple docstring'''




        if not self.initialized:
               lowercase	:		Any    =   RagRetriever(
                   A ,		question_encoder_tokenizer=A ,		generator_tokenizer=A ,		index=A ,		init_retrieval=A ,		)
               lowercase	:		Dict    =   True



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        self.retriever.index.init_index()



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        del self.retriever
        lowercase	:		int    =   False





 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        lowercase,  lowercase	:		Tuple    =   self.retriever._main_retrieve(A ,		A			)
        lowercase	:		List[str]    =   self.retriever.index.get_doc_dicts(A			)
        return doc_ids, retrieved_doc_embeds, doc_dicts






class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):



 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 ,		)

        lowercase	:		List[Any]    =   retrieval_workers
        lowercase	:		Tuple    =   question_encoder_tokenizer
        lowercase	:		Tuple    =   generator_tokenizer
        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.
               lowercase	:		str    =   self.retrieval_workers[random.randint(0 ,		len(self.retrieval_workers			) - 1			)]
               lowercase,  lowercase,  lowercase	:		Union[str, Any]    =   ray.get(
                   random_worker.retrieve.remote(A ,		A			)			)
        else:
               lowercase,  lowercase	:		Any    =   self._main_retrieve(A ,		A			)
               lowercase	:		Tuple    =   self.index.get_doc_dicts(A			)
        return retrieved_doc_embeds, doc_ids, doc_dicts



 @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'''




        lowercase	:		List[str]    =   kwargs.pop('''config''' ,		A			) or RagConfig.from_pretrained(A ,		**A			)
        lowercase	:		List[Any]    =   RagTokenizer.from_pretrained(A ,		config=A			)
        lowercase	:		Any    =   rag_tokenizer.question_encoder
        lowercase	:		Any    =   rag_tokenizer.generator

        if indexed_dataset is not None:
               lowercase	:		Optional[int]    =   '''custom'''
               lowercase	:		Optional[int]    =   CustomHFIndex(config.retrieval_vector_size ,		A			)
        else:
               lowercase	:		Tuple    =   cls._build_index(A			)

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





 def  SCREAMING_SNAKE_CASE(	self			):






        '''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	:		int    =   self._build_index(self.config			)

        ray.get(
            [
                worker.create_rag_retriever.remote(
                    self.config ,		self.question_encoder_tokenizer ,		self.generator_tokenizer ,		A			)
                for worker in self.retrieval_workers
            ]			)










'''simple docstring'''






import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
      import torch

      from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
      import pyanvml.pyanvml as nvml


lowercase						:   List[Any]      						=							logging.get_logger(__name__)

class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  PyTorchBenchmarkArguments
 __snake_case						:  PretrainedConfig
 __snake_case						:  str       =   "PyTorch"



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return torch.__version__



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		str    =   self._prepare_inference_func(A ,		A ,		A			)
        return self._measure_speed(_inference			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		int    =   self._prepare_inference_func(A ,		A ,		A			)
        return self._measure_memory(_inference			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Union[str, Any]    =   self._prepare_train_func(A ,		A ,		A			)
        return self._measure_speed(_train			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Any    =   self._prepare_train_func(A ,		A ,		A			)
        return self._measure_memory(_train			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Any    =   self.config_dict[model_name]

        if self.args.torchscript:
               lowercase	:		Any    =   True

        lowercase	:		Union[str, Any]    =   (
            hasattr(A ,		'''architectures'''			)
            and isinstance(config.architectures ,		A			)
            and len(config.architectures			) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                      lowercase	:		Any    =   config.architectures[0]
                      lowercase	:		Dict    =   __import__('''transformers''' ,		fromlist=[model_class]			)
                      lowercase	:		List[str]    =   getattr(A ,		A			)
                      lowercase	:		Dict    =   model_cls(A			)
               except ImportError:
                      raise ImportError(
                          f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                          ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''			)
        else:
               lowercase	:		Optional[int]    =   MODEL_MAPPING[config.__class__](A			)

        model.eval()
        model.to(self.args.device			)

        # encoder-decoder has vocab size saved differently
        lowercase	:		str    =   config.vocab_size if hasattr(A ,		'''vocab_size'''			) else config.encoder.vocab_size
        lowercase	:		Union[str, Any]    =   torch.randint(A ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device			)

        if self.args.fpaa:
               logger.info('''Running training in Mixed Precision...'''			)
               if not self.args.is_gpu:
                      raise ValueError('''Mixed precision is possible only for GPU.'''			)
               # amp seems to have memory leaks so that memory usage
               # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
               model.half()

        if self.args.torchscript:
               with torch.no_grad():
                      lowercase	:		int    =   torch.jit.trace(A ,		A			)
        else:
               lowercase	:		Any    =   model

        def encoder_decoder_forward():
               with torch.no_grad():
                      lowercase	:		int    =   inference_model(A ,		decoder_input_ids=A			)
               return outputs

        def encoder_forward():
               with torch.no_grad():
                      lowercase	:		Tuple    =   inference_model(A			)
               return outputs

        lowercase	:		int    =   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   self.config_dict[model_name]

        lowercase	:		Optional[Any]    =   (
            hasattr(A ,		'''architectures'''			)
            and isinstance(config.architectures ,		A			)
            and len(config.architectures			) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                      lowercase	:		str    =   config.architectures[0]
                      lowercase	:		List[str]    =   __import__('''transformers''' ,		fromlist=[model_class]			)
                      lowercase	:		int    =   getattr(A ,		A			)
                      lowercase	:		Any    =   model_cls(A			)
               except ImportError:
                      raise ImportError(
                          f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                          ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''			)
        else:
               lowercase	:		Union[str, Any]    =   MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A			)

        if self.args.torchscript:
               raise NotImplementedError('''Training for torchscript is currently not implemented'''			)
        else:
               lowercase	:		Dict    =   model

        model.train()
        model.to(self.args.device			)

        # encoder-decoder has vocab size saved differently
        lowercase	:		Union[str, Any]    =   config.vocab_size if hasattr(A ,		'''vocab_size'''			) else config.encoder.vocab_size
        lowercase	:		List[Any]    =   torch.randint(A ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device			)

        if self.args.fpaa:
               logger.info('''Running training in Mixed Precision...'''			)
               if not self.args.is_gpu:
                      raise ValueError('''Mixed precision is possible only for GPU.'''			)

               # amp seems to have memory leaks so that memory usage
               # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
               model.half()

        def compute_loss_and_backprob_encoder():
               lowercase	:		Optional[Any]    =   train_model(A ,		labels=A			)[0]
               loss.backward()
               return loss

        def compute_loss_and_backprob_encoder_decoder():
               lowercase	:		List[Any]    =   train_model(A ,		decoder_input_ids=A ,		labels=A			)[0]
               loss.backward()
               return loss

        lowercase	:		Any    =   (
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        try:
               if self.args.is_tpu or self.args.torchscript:
                      # run additional 10 times to stabilize compilation for tpu and torchscript
                      logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''			)
                      timeit.repeat(
                          A ,		repeat=1 ,		number=5 ,		)

               # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
               lowercase	:		str    =   timeit.repeat(
                   A ,		repeat=self.args.repeat ,		number=10 ,		)

               if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                      import torch_xla.debug.metrics as met

                      self.print_fn(met.metrics_report()			)

               return min(A			) / 1_0.0
        except RuntimeError as e:
               self.print_fn(f"""Doesn't fit on GPU. {e}"""			)
               return "N/A"





 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        try:
               if self.args.trace_memory_line_by_line:
                      lowercase	:		int    =   start_memory_tracing('''transformers'''			)

               if self.args.is_tpu:
                      # tpu
                      raise NotImplementedError(
                          '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
                          ''' `--no-memory` or `args.memory=False`'''			)
               elif self.args.is_gpu:
                      if not is_pyanvml_available():
                             logger.warning(
                                 '''py3nvml not installed, we won\'t log GPU memory usage. '''
                                 '''Install py3nvml (pip install py3nvml) to log information about GPU.'''			)
                             lowercase	:		str    =   '''N/A'''
                      else:
                             logger.info(
                                 '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
                                 ''' on the same GPU.'''			)
                             # init nvml
                             nvml.nvmlInit()
                             func()
                             lowercase	:		List[Any]    =   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
                             lowercase	:		List[Any]    =   nvml.nvmlDeviceGetMemoryInfo(A			)
                             lowercase	:		int    =   meminfo.used
                             lowercase	:		Dict    =   Memory(A			)
                             # shutdown nvml
                             nvml.nvmlShutdown()
               else:
                      # cpu
                      lowercase	:		Optional[Any]    =   measure_peak_memory_cpu(A			)
                      lowercase	:		Union[str, Any]    =   Memory(A			) if isinstance(A ,		A			) else memory_bytes

               if self.args.trace_memory_line_by_line:
                      lowercase	:		Tuple    =   stop_memory_tracing(A			)
               else:
                      lowercase	:		int    =   None

               return memory, summary
        except RuntimeError as e:
               self.print_fn(f"""Doesn't fit on GPU. {e}"""			)
               return "N/A", None




