





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


UpperCAmelCase_           =	logging.getLogger(__name__)




class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase=None					):
        super().__init__(
            _UpperCamelCase				,							question_encoder_tokenizer=_UpperCamelCase				,							generator_tokenizer=_UpperCamelCase				,							index=_UpperCamelCase				,							init_retrieval=_UpperCamelCase				,							)
        _UpperCAmelCase           =   None





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        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
           _UpperCAmelCase           =   self._infer_socket_ifname()
           # avoid clash with the NCCL port
           _UpperCAmelCase           =   str(distributed_port + 1					)
           _UpperCAmelCase           =   dist.new_group(ranks=_UpperCamelCase				,							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 		UpperCamelCase(     self					):
        return dist.get_rank(group=self.process_group					) == 0





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase=torch.floataa					):
        _UpperCAmelCase           =   torch.empty(_UpperCamelCase				,							dtype=_UpperCamelCase					)
        dist.scatter(_UpperCamelCase				,							src=0				,							scatter_list=_UpperCamelCase				,							group=self.process_group					)
        return target_tensor





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   psutil.net_if_addrs()
        # a hacky way to deal with varying network interface names
        _UpperCAmelCase           =   next((addr for addr in addrs if addr.startswith('''e'''					))				,							_UpperCamelCase					)
        return ifname





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase					):

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

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

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

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

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






from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


UpperCAmelCase_           =	logging.get_logger(__name__)


UpperCAmelCase_           =	OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[str]							  =   FLAX_MODEL_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModel)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Optional[Any]							  =   FLAX_MODEL_FOR_PRETRAINING_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[Any]							  =   FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[str]							  =   FLAX_MODEL_FOR_MASKED_LM_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Dict							  =   FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[str]							  =   FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Dict							  =   FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Union[str, Any]							  =   FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Union[str, Any]							  =   FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Any							  =   FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Dict							  =   FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Optional[int]							  =   FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  str							  =   FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)