codingutf8 2022 the openai s and the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license flax whisper model import math import random from functools import partial from typing import optional tuple import flax linen as nn import jax import jax numpy as jnp from flax core frozendict import frozendict freeze unfreeze from flax linen import combinemasks makecausalmask from flax linen import partitioning as nnpartitioning from flax linen attention import dotproductattentionweights from flax traverseutil import flattendict unflattendict from jax import lax from jax random import prngkey from generation flaxlogitsprocess import flaxwhispertimestamplogitsprocessor from modelingflaxoutputs import flaxbasemodeloutput flaxbasemodeloutputwithpastandcrossattentions flaxcausallmoutputwithcrossattentions flaxseq2seqlmoutput flaxseq2seqmodeloutput flaxsequenceclassifieroutput from modelingflaxutils import act2fn flaxpretrainedmodel appendcallsampledocstring appendreplacereturndocstrings overwritecalldocstring from utils import addstartdocstrings addstartdocstringstomodelforward logging replacereturndocstrings from configurationwhisper import whisperconfig logger logging getloggername checkpointfordoc openaiwhispertiny configfordoc whisperconfig remat nnpartitioning remat def sinusoidalembeddinginitkey shape dtypejnp float jax array whisperinputsdocstring r args inputfeatures numpy ndarray of shape batchsize featuresize sequencelength float values mel features extracted from the raw speech waveform raw speech waveform can be obtained by loading a flac or wav audio file into an array of type listfloat or a numpy ndarray e g via the soundfile library pip install soundfile to prepare the array into inputfeatures the whisperfeatureextractor should be used for extracting the features padding and conversion into a tensor of type numpy ndarray see whisperfeatureextractor call attentionmask numpy ndarray of shape batchsize sequencelength optional whisper does not support masking of the inputfeatures this argument is preserved for compatibility but is not used by default the silence in the input log mel spectrogram are ignored decoderinputids numpy ndarray of shape batchsize targetsequencelength optional indices of decoder input sequence tokens in the vocabulary indices can be obtained using whispertokenizer see pretrainedtokenizer encode and pretrainedtokenizer call for details what are decoder input ids glossarydecoderinputids whisper uses the decoderstarttokenid as the starting token for decoderinputids generation decoderattentionmask numpy ndarray of shape batchsize targetsequencelength optional default behavior generate a tensor that ignores pad tokens in decoderinputids causal mask will also be used by default if you want to change padding behavior you should modify to your needs see diagram 1 in the paperhttps arxiv orgabs1910 13461 for more information on the default strategy positionids numpy ndarray of shape batchsize sequencelength optional whisper does not use positionids in the encoder as inputfeatures is always the same size and doesn t use masking but this argument is preserved for compatibility by default the silence in the input log mel spectrogram are ignored decoderpositionids numpy ndarray of shape batchsize sequencelength optional indices of positions of each decoder input sequence tokens in the position embeddings selected in the range 0 config maxpositionembeddings 1 outputattentions bool optional whether or not to return the attentions tensors of all attention layers see attentions under returned tensors for more detail outputhiddenstates bool optional whether or not to return the hidden states of all layers see hiddenstates under returned tensors for more detail returndict bool optional whether or not to return a utils modeloutput instead of a plain tuple whisperdecodeinputsdocstring r args decoderinputids numpy ndarray of shape batchsize targetsequencelength indices of decoder input sequence tokens in the vocabulary indices can be obtained using whispertokenizer see pretrainedtokenizer encode and pretrainedtokenizer call for details what are decoder input ids glossarydecoderinputids encoderoutputs tupletuplenumpy ndarray tuple consists of lasthiddenstate optional hiddenstates optional attentions lasthiddenstate of shape batchsize sequencelength hiddensize optional is a sequence of hiddenstates at the output of the last layer of the encoder used in the crossattention of the decoder encoderattentionmask numpy ndarray of shape batchsize sequencelength optional whisper does not support masking of the inputfeatures this argument is preserved for compatibility but it is not used by default the silence in the input log mel spectrogram are ignored decoderattentionmask numpy ndarray of shape batchsize targetsequencelength optional default behavior generate a tensor that ignores pad tokens in decoderinputids causal mask will also be used by default if you want to change padding behavior you should modify to your needs see diagram 1 in the paperhttps arxiv orgabs1910 13461 for more information on the default strategy decoderpositionids numpy ndarray of shape batchsize sequencelength optional indices of positions of each decoder input sequence tokens in the position embeddings selected in the range 0 config maxpositionembeddings 1 pastkeyvalues dictstr numpy ndarray optional returned by initcache or when passing previous pastkeyvalues dictionary of precomputed hiddenstates key and values in the attention blocks that can be used for fast autoregressive decoding precomputed key and value hiddenstates are of shape batchsize maxlength outputattentions bool optional whether or not to return the attentions tensors of all attention layers see attentions under returned tensors for more detail outputhiddenstates bool optional whether or not to return the hidden states of all layers see hiddenstates under returned tensors for more detail returndict bool optional whether or not to return a utils modeloutput instead of a plain tuple combine masks if needed during fast autoregressive decoding we feed one position at a time and cache the keys and values step by step convert the boolean attention mask to an attention bias attention mask in the form of attention bias detect if we re initializing by absence of existing cache data update key value caches with our new 1d spatial slices causal mask for cached decoder selfattention our single query position should only attend to those key positions that have already been generated and cached not the remaining zero elements copied from transformers models mbart modelingflaxmbart flaxmbartencoderlayer with mbartwhisper add layerdrop see https arxiv orgabs1909 11556 for description copied from transformers models mbart modelingflaxmbart flaxmbartdecoderlayer with mbartwhisper self attention crossattention block fully connected decoder layers add layerdrop see https arxiv orgabs1909 11556 for description add hidden states from the last decoder layer freeze the sinusoidal embeddings by stopping the backprop update the last element in hiddenstates after applying layernorm above update the last element in hiddenstates after applying layernorm above init input tensors copied from transformers models bart modelingflaxbart flaxbartpretrainedmodel initcache with bartwhisper init input variables to retrieve cache decoderinputids jnp onesbatchsize maxlength dtypei4 decoderattentionmask jnp oneslikedecoderinputids decoderpositionids jnp broadcastto jnp arangejnp atleast2ddecoderinputids shape1 decoderinputids shape def decoderforwardmodule decoderinputids decoderattentionmask decoderpositionids kwargs decodermodule module getdecodermodule return decodermodule decoderinputids decoderattentionmask decoderpositionids kwargs initvariables self module init jax random prngkey0 decoderinputidsdecoderinputids decoderattentionmaskdecoderattentionmask decoderpositionidsdecoderpositionids encoderhiddenstatesencoderoutputs0 initcachetrue methoddecoderforward we only need to call the decoder to init the cache return unfreezeinitvariablescache addstartdocstringswhisperencodeinputsdocstring replacereturndocstringsoutputtypeflaxbasemodeloutput configclasswhisperconfig def encode self inputfeatures jnp ndarray attentionmask optionaljnp ndarray none outputattentions optionalbool none outputhiddenstates optionalbool none returndict optionalbool none train bool false params dict none dropoutrng prngkey none kwargs r returns example python from transformers import whisperprocessor flaxwhisperforconditionalgeneration from datasets import loaddataset processor whisperprocessor frompretrainedopenaiwhispertiny en model flaxwhisperforconditionalgeneration frompretrainedopenaiwhispertiny en frompttrue ds loaddatasethfinternaltestinglibrispeechasrdummy clean splitvalidation inputs processords0audioarray returntensorsnp inputfeatures inputs inputfeatures encoderoutputs model encodeinputfeaturesinputfeatures outputattentions outputattentions if outputattentions is not none else self config outputattentions outputhiddenstates outputhiddenstates if outputhiddenstates is not none else self config outputhiddenstates returndict returndict if returndict is not none else self config returndict handle any prng if needed rngs if dropoutrng is not none rngsdropout dropoutrng def encoderforwardmodule inputfeatures kwargs encodemodule module getencodermodule return encodemoduleinputfeatures kwargs return self module apply params params or self params inputfeaturesjnp arrayinputfeatures dtypef4 outputattentionsoutputattentions outputhiddenstatesoutputhiddenstates returndictreturndict deterministicnot train rngsrngs methodencoderforward addstartdocstringswhisperdecodeinputsdocstring replacereturndocstringsoutputtypeflaxbasemodeloutputwithpastandcrossattentions configclasswhisperconfig def decode self decoderinputids encoderoutputs encoderattentionmask optionaljnp ndarray none decoderattentionmask optionaljnp ndarray none decoderpositionids optionaljnp ndarray none pastkeyvalues dict none outputattentions optionalbool none outputhiddenstates optionalbool none returndict optionalbool none train bool false params dict none dropoutrng prngkey none r returns example python from transformers import whisperprocessor flaxwhisperforconditionalgeneration from datasets import loaddataset import jax numpy as jnp processor whisperprocessor frompretrainedopenaiwhispertiny en model flaxwhisperforconditionalgeneration frompretrainedopenaiwhispertiny en frompttrue ds loaddatasethfinternaltestinglibrispeechasrdummy clean splitvalidation inputfeatures processords0audioarray returntensorsnp inputfeatures encoderoutputs model encodeinputfeaturesinputfeatures decoderstarttokenid model config decoderstarttokenid decoderinputids jnp onesinputfeatures shape0 1 dtypei4 decoderstarttokenid outputs model decodedecoderinputids encoderoutputs lastdecoderhiddenstates outputs lasthiddenstate outputattentions outputattentions if outputattentions is not none else self config outputattentions outputhiddenstates outputhiddenstates if outputhiddenstates is not none else self config outputhiddenstates returndict returndict if returndict is not none else self config returndict encoderhiddenstates encoderoutputs0 batchsize sequencelength decoderinputids shape if decoderpositionids is none if pastkeyvalues is not none raise valueerrormake sure to provide decoderpositionids when passing pastkeyvalues if decoderattentionmask is not none decoderpositionids decoderattentionmask cumsum1 decoderattentionmask 1 else decoderpositionids jnp broadcastto jnp arangesequencelengthnone batchsize sequencelength if decoderattentionmask is none decoderattentionmask jnp onesbatchsize sequencelength handle any prng if needed rngs if dropoutrng is not none rngsdropout dropoutrng inputs params params or self params if pastkeyvalues are passed then cache is already initialized a private flag initcache has to be passed down to ensure cache is used it has to be made sure that cache is marked as mutable so that it can be changed by flaxwhisperattention module if pastkeyvalues inputscache pastkeyvalues mutable cache else mutable false def decoderforwardmodule decoderinputids decoderattentionmask decoderpositionids kwargs decodermodule module getdecodermodule return decodermodule inputidsdecoderinputids attentionmaskdecoderattentionmask positionidsdecoderpositionids kwargs outputs self module apply inputs decoderinputidsjnp arraydecoderinputids dtypei4 decoderattentionmaskjnp arraydecoderattentionmask dtypei4 decoderpositionidsjnp arraydecoderpositionids dtypei4 encoderhiddenstatesencoderhiddenstates outputattentionsoutputattentions outputhiddenstatesoutputhiddenstates returndictreturndict deterministicnot train rngsrngs mutablemutable methoddecoderforward add updated cache to model output if pastkeyvalues is not none and returndict outputs past outputs outputspastkeyvalues unfreezepastcache return outputs elif pastkeyvalues is not none and not returndict outputs past outputs outputs outputs 1 unfreezepastcache outputs1 return outputs addstartdocstringstomodelforwardwhisperinputsdocstring def call self inputfeatures jnp ndarray decoderinputids jnp ndarray attentionmask optionaljnp ndarray none decoderattentionmask optionaljnp ndarray none positionids optionaljnp ndarray none decoderpositionids optionaljnp ndarray none outputattentions optionalbool none outputhiddenstates optionalbool none returndict optionalbool none train bool false params dict none dropoutrng prngkey none outputattentions outputattentions if outputattentions is not none else self config outputattentions outputhiddenstates outputhiddenstates if outputhiddenstates is not none else self config outputhiddenstates returndict returndict if returndict is not none else self config returndict prepare decoder inputs if decoderpositionids is none if decoderattentionmask is not none decoderpositionids decoderattentionmask cumsum1 decoderattentionmask 1 else batchsize sequencelength decoderinputids shape decoderpositionids jnp broadcastto jnp arangesequencelengthnone batchsize sequencelength if decoderattentionmask is none decoderattentionmask jnp oneslikedecoderinputids handle any prng if needed rngs dropout dropoutrng if dropoutrng is not none else return self module apply params params or self params inputfeaturesjnp arrayinputfeatures dtypef4 decoderinputidsjnp arraydecoderinputids dtypei4 decoderattentionmaskjnp arraydecoderattentionmask dtypei4 decoderpositionidsjnp arraydecoderpositionids dtypei4 outputattentionsoutputattentions outputhiddenstatesoutputhiddenstates returndictreturndict deterministicnot train rngsrngs addstartdocstrings the bare whisper model transformer outputting raw hiddenstates without any specific head on top whisperstartdocstring class flaxwhispermodelflaxwhisperpretrainedmodel config whisperconfig dtype jnp dtype jnp float32 the dtype of the computation moduleclass flaxwhispermodule appendcallsampledocstringflaxwhispermodel checkpointfordoc flaxseq2seqmodeloutput configfordoc class flaxwhisperforconditionalgenerationmodulenn module config whisperconfig dtype jnp dtype jnp float32 gradientcheckpointing bool false def setupself none self model flaxwhispermodule configself config dtypeself dtype gradientcheckpointingself gradientcheckpointing self lmhead nn dense self config vocabsize usebiasfalse dtypeself dtype kernelinitjax nn initializers normalself config initstd def getencodermoduleself return self model encoder def getdecodermoduleself return self model decoder def call self inputfeatures decoderinputids decoderattentionmask jnp ndarray none decoderpositionids jnp ndarray none positionids jnp ndarray none attentionmask jnp ndarray none outputattentions bool false outputhiddenstates bool false returndict bool true deterministic bool true outputs self model inputfeaturesinputfeatures decoderinputidsdecoderinputids decoderattentionmaskdecoderattentionmask decoderpositionidsdecoderpositionids outputattentionsoutputattentions outputhiddenstatesoutputhiddenstates returndictreturndict deterministicdeterministic hiddenstates outputs0 if self config tiewordembeddings sharedembedding self model decoder embedtokens variablesparamsembedding lmlogits self lmhead applyparams kernel sharedembedding t hiddenstates else lmlogits self lmheadhiddenstates if not returndict output lmlogits outputs1 return output return flaxseq2seqlmoutput logitslmlogits decoderhiddenstatesoutputs decoderhiddenstates decoderattentionsoutputs decoderattentions crossattentionsoutputs crossattentions encoderlasthiddenstateoutputs encoderlasthiddenstate encoderhiddenstatesoutputs encoderhiddenstates encoderattentionsoutputs encoderattentions addstartdocstringsthe whisper model with a language modeling head whisperstartdocstring class flaxwhisperforconditionalgenerationflaxwhisperpretrainedmodel moduleclass flaxwhisperforconditionalgenerationmodule dtype jnp dtype jnp float32 addstartdocstringswhisperdecodeinputsdocstring replacereturndocstringsoutputtypeflaxcausallmoutputwithcrossattentions configclasswhisperconfig def decode self decoderinputids encoderoutputs encoderattentionmask optionaljnp ndarray none decoderattentionmask optionaljnp ndarray none decoderpositionids optionaljnp ndarray none pastkeyvalues dict none outputattentions optionalbool none outputhiddenstates optionalbool none returndict optionalbool none train bool false params dict none dropoutrng prngkey none r returns example python from transformers import whisperprocessor flaxwhisperforconditionalgeneration from datasets import loaddataset processor whisperprocessor frompretrainedopenaiwhispertiny en model flaxwhisperforconditionalgeneration frompretrainedopenaiwhispertiny en frompttrue ds loaddatasethfinternaltestinglibrispeechasrdummy clean splitvalidation inputs processords0audioarray returntensorsnp inputfeatures inputs inputfeatures encoderoutputs model encodeinputfeaturesinputfeatures decoderstarttokenid model config decoderstarttokenid decoderinputids jnp onesinputs inputids shape0 1 dtypei4 decoderstarttokenid outputs model decodedecoderinputids encoderoutputs lastdecoderhiddenstates outputs lasthiddenstate outputattentions outputattentions if outputattentions is not none else self config outputattentions outputhiddenstates outputhiddenstates if outputhiddenstates is not none else self config outputhiddenstates returndict returndict if returndict is not none else self config returndict encoderhiddenstates encoderoutputs0 batchsize sequencelength decoderinputids shape if decoderpositionids is none if pastkeyvalues is not none raise valueerrormake sure to provide decoderpositionids when passing pastkeyvalues if decoderattentionmask is not none decoderpositionids decoderattentionmask cumsum1 decoderattentionmask 1 else decoderpositionids jnp broadcastto jnp arangesequencelengthnone batchsize sequencelength if decoderattentionmask is none decoderattentionmask jnp onesbatchsize sequencelength dtypei4 handle any prng if needed rngs if dropoutrng is not none rngsdropout dropoutrng inputs params params or self params if pastkeyvalues are passed then cache is already initialized a private flag initcache has to be passed down to ensure cache is used it has to be made sure that cache is marked as mutable so that it can be changed by flaxwhisperattention module if pastkeyvalues inputscache pastkeyvalues mutable cache else mutable false def decoderforwardmodule decoderinputids decoderattentionmask decoderpositionids kwargs decodermodule module getdecodermodule outputs decodermodule inputidsdecoderinputids attentionmaskdecoderattentionmask positionidsdecoderpositionids kwargs hiddenstates outputs0 if self config tiewordembeddings sharedembedding module model decoder embedtokens variablesparamsembedding lmlogits module lmhead applyparams kernel sharedembedding t hiddenstates else lmlogits module lmheadhiddenstates return lmlogits outputs outputs self module apply inputs decoderinputidsjnp arraydecoderinputids dtypei4 decoderattentionmaskjnp arraydecoderattentionmask dtypei4 decoderpositionidsjnp arraydecoderpositionids dtypei4 encoderhiddenstatesencoderhiddenstates outputattentionsoutputattentions outputhiddenstatesoutputhiddenstates returndictreturndict deterministicnot train rngsrngs mutablemutable methoddecoderforward if pastkeyvalues is none lmlogits decoderoutputs outputs else lmlogits decoderoutputs past outputs if returndict outputs flaxcausallmoutputwithcrossattentions logitslmlogits hiddenstatesdecoderoutputs hiddenstates attentionsdecoderoutputs attentions crossattentionsdecoderoutputs crossattentions else outputs lmlogits decoderoutputs1 add updated cache to model output if pastkeyvalues is not none and returndict outputspastkeyvalues unfreezepastcache return outputs elif pastkeyvalues is not none and not returndict outputs outputs 1 unfreezepastcache outputs1 return outputs def generate self inputfeatures generationconfignone logitsprocessornone returntimestampsnone tasknone languagenone ismultilingualnone kwargs if generationconfig is none generationconfig self generationconfig if returntimestamps is not none generationconfig returntimestamps returntimestamps if task is not none generationconfig task task if ismultilingual is not none generationconfig ismultilingual ismultilingual if language is not none generationconfig language language if kwargs is not none and decoderinputids in kwargs decoderinputlength lenkwargsdecoderinputids else decoderinputlength 1 forceddecoderids if hasattrgenerationconfig ismultilingual and generationconfig ismultilingual if hasattrgenerationconfig language forceddecoderids append1 generationconfig langtoidgenerationconfig language else forceddecoderids append1 none if hasattrgenerationconfig task forceddecoderids append2 generationconfig tasktoidgenerationconfig task else forceddecoderids append2 generationconfig tasktoidtranscribe if hasattrgenerationconfig returntimestamps and generationconfig returntimestamps or returntimestamps logitsprocessor flaxwhispertimestamplogitsprocessorgenerationconfig self config decoderinputlength else if forceddecoderids and forceddecoderids10 generationconfig notimestampstokenid idx forceddecoderids10 1 if forceddecoderids else 1 forceddecoderids appendidx generationconfig notimestampstokenid if lenforceddecoderids 0 generationconfig forceddecoderids forceddecoderids return super generate inputfeatures generationconfig logitsprocessorlogitsprocessor kwargs def prepareinputsforgeneration self decoderinputids maxlength attentionmask optionaljax array none decoderattentionmask optionaljax array none encoderoutputsnone kwargs initializing the cache batchsize seqlength decoderinputids shape pastkeyvalues self initcachebatchsize maxlength encoderoutputs note that usually one would have to put 0 s in the attentionmask for x inputids shape1 and x cachelength but since the decoder uses a causal mask those positions are masked anyways thus we can create a single static attentionmask here which is more efficient for compilation extendedattentionmask jnp onesbatchsize maxlength dtypei4 if decoderattentionmask is not none positionids decoderattentionmask cumsum1 1 extendedattentionmask lax dynamicupdatesliceextendedattentionmask decoderattentionmask 0 0 else positionids jnp broadcasttojnp arangeseqlength dtypei4none batchsize seqlength return pastkeyvalues pastkeyvalues encoderoutputs encoderoutputs encoderattentionmask attentionmask decoderattentionmask extendedattentionmask decoderpositionids positionids def updateinputsforgenerationself modeloutputs modelkwargs modelkwargspastkeyvalues modeloutputs pastkeyvalues modelkwargsdecoderpositionids modelkwargsdecoderpositionids 1 1 return modelkwargs flaxwhisperconditionalgenerationdocstring r returns transcription example python from transformers import whisperprocessor flaxwhisperforconditionalgeneration from datasets import loaddataset processor whisperprocessor frompretrainedopenaiwhispertiny en model flaxwhisperforconditionalgeneration frompretrainedopenaiwhispertiny en frompttrue ds loaddatasethfinternaltestinglibrispeechasrdummy clean splitvalidation inputs processords0audioarray returntensorsnp inputfeatures inputs inputfeatures generatedids model generateinputidsinputfeatures transcription processor batchdecodegeneratedids skipspecialtokenstrue0 transcription mr quilter is the apostle of the middle classes and we are glad to welcome his gospel init input tensors handle any prng if needed overwritecalldocstring flaxwhisperforaudioclassification whisperinputsdocstring flaxwhisperaudioclassificationdocstring appendreplacereturndocstrings flaxwhisperforaudioclassification outputtypeflaxsequenceclassifieroutput configclassconfigfordoc coding utf 8 2022 the openai s and the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license flax whisper model returns sinusoids for positional embedding this model inherits from flaxpretrainedmodel check the superclass documentation for the generic methods the library implements for all its models such as downloading or saving resizing the input embeddings pruning heads etc this model is also a flax linen flax nn module https flax readthedocs io en latest _autosummary flax nn module html subclass use it as a regular flax module and refer to the flax documentation for all matter related to general usage and behavior finally this model supports inherent jax features such as just in time jit compilation https jax readthedocs io en latest jax html just in time compilation jit automatic differentiation https jax readthedocs io en latest jax html automatic differentiation vectorization https jax readthedocs io en latest jax html vectorization vmap parallelization https jax readthedocs io en latest jax html parallelization pmap parameters config whisperconfig model configuration class with all the parameters of the model initializing with a config file does not load the weights associated with the model only the configuration check out the flaxpretrainedmodel from_pretrained method to load the model weights dtype jax numpy dtype optional defaults to jax numpy float32 the data type of the computation can be one of jax numpy float32 jax numpy float16 on gpus and jax numpy bfloat16 on tpus this can be used to enable mixed precision training or half precision inference on gpus or tpus if specified all the computation will be performed with the given dtype note that this only specifies the dtype of the computation and does not influence the dtype of model parameters if you wish to change the dtype of the model parameters see flaxpretrainedmodel to_fp16 and flaxpretrainedmodel to_bf16 args input_features numpy ndarray of shape batch_size feature_size sequence_length float values mel features extracted from the raw speech waveform raw speech waveform can be obtained by loading a flac or wav audio file into an array of type list float or a numpy ndarray e g via the soundfile library pip install soundfile to prepare the array into input_features the whisperfeatureextractor should be used for extracting the features padding and conversion into a tensor of type numpy ndarray see whisperfeatureextractor __call__ attention_mask numpy ndarray of shape batch_size sequence_length optional whisper does not support masking of the input_features this argument is preserved for compatibility but is not used by default the silence in the input log mel spectrogram are ignored decoder_input_ids numpy ndarray of shape batch_size target_sequence_length optional indices of decoder input sequence tokens in the vocabulary indices can be obtained using whispertokenizer see pretrainedtokenizer encode and pretrainedtokenizer __call__ for details what are decoder input ids glossary decoder input ids whisper uses the decoder_start_token_id as the starting token for decoder_input_ids generation decoder_attention_mask numpy ndarray of shape batch_size target_sequence_length optional default behavior generate a tensor that ignores pad tokens in decoder_input_ids causal mask will also be used by default if you want to change padding behavior you should modify to your needs see diagram 1 in the paper https arxiv org abs 1910 13461 for more information on the default strategy position_ids numpy ndarray of shape batch_size sequence_length optional whisper does not use position_ids in the encoder as input_features is always the same size and doesn t use masking but this argument is preserved for compatibility by default the silence in the input log mel spectrogram are ignored decoder_position_ids numpy ndarray of shape batch_size sequence_length optional indices of positions of each decoder input sequence tokens in the position embeddings selected in the range 0 config max_position_embeddings 1 output_attentions bool optional whether or not to return the attentions tensors of all attention layers see attentions under returned tensors for more detail output_hidden_states bool optional whether or not to return the hidden states of all layers see hidden_states under returned tensors for more detail return_dict bool optional whether or not to return a utils modeloutput instead of a plain tuple args input_features numpy ndarray of shape batch_size feature_size sequence_length float values mel features extracted from the raw speech waveform raw speech waveform can be obtained by loading a flac or wav audio file into an array of type list float or a numpy ndarray e g via the soundfile library pip install soundfile to prepare the array into input_features the whisperfeatureextractor should be used for extracting the mel features padding and conversion into a tensor of type numpy ndarray see whisperfeatureextractor __call__ attention_mask numpy ndarray of shape batch_size sequence_length optional whisper does not support masking of the input_features this argument is preserved for compatibility but is not used by default the silence in the input log mel spectrogram are ignored output_attentions bool optional whether or not to return the attentions tensors of all attention layers see attentions under returned tensors for more detail output_hidden_states bool optional whether or not to return the hidden states of all layers see hidden_states under returned tensors for more detail return_dict bool optional whether or not to return a utils modeloutput instead of a plain tuple args decoder_input_ids numpy ndarray of shape batch_size target_sequence_length indices of decoder input sequence tokens in the vocabulary indices can be obtained using whispertokenizer see pretrainedtokenizer encode and pretrainedtokenizer __call__ for details what are decoder input ids glossary decoder input ids encoder_outputs tuple tuple numpy ndarray tuple consists of last_hidden_state optional hidden_states optional attentions last_hidden_state of shape batch_size sequence_length hidden_size optional is a sequence of hidden states at the output of the last layer of the encoder used in the cross attention of the decoder encoder_attention_mask numpy ndarray of shape batch_size sequence_length optional whisper does not support masking of the input_features this argument is preserved for compatibility but it is not used by default the silence in the input log mel spectrogram are ignored decoder_attention_mask numpy ndarray of shape batch_size target_sequence_length optional default behavior generate a tensor that ignores pad tokens in decoder_input_ids causal mask will also be used by default if you want to change padding behavior you should modify to your needs see diagram 1 in the paper https arxiv org abs 1910 13461 for more information on the default strategy decoder_position_ids numpy ndarray of shape batch_size sequence_length optional indices of positions of each decoder input sequence tokens in the position embeddings selected in the range 0 config max_position_embeddings 1 past_key_values dict str numpy ndarray optional returned by init_cache or when passing previous past_key_values dictionary of pre computed hidden states key and values in the attention blocks that can be used for fast auto regressive decoding pre computed key and value hidden states are of shape batch_size max_length output_attentions bool optional whether or not to return the attentions tensors of all attention layers see attentions under returned tensors for more detail output_hidden_states bool optional whether or not to return the hidden states of all layers see hidden_states under returned tensors for more detail return_dict bool optional whether or not to return a utils modeloutput instead of a plain tuple combine masks if needed during fast autoregressive decoding we feed one position at a time and cache the keys and values step by step convert the boolean attention mask to an attention bias attention mask in the form of attention bias detect if we re initializing by absence of existing cache data update key value caches with our new 1d spatial slices causal mask for cached decoder self attention our single query position should only attend to those key positions that have already been generated and cached not the remaining zero elements copied from transformers models mbart modeling_flax_mbart flaxmbartencoderlayer with mbart whisper the dtype of the computation add layerdrop see https arxiv org abs 1909 11556 for description skip the layer copied from transformers models mbart modeling_flax_mbart flaxmbartdecoderlayer with mbart whisper self attention cross attention block fully connected the dtype of the computation decoder layers add layerdrop see https arxiv org abs 1909 11556 for description add hidden states from the last decoder layer freeze the sinusoidal embeddings by stopping the back prop update the last element in hidden_states after applying layernorm above update the last element in hidden_states after applying layernorm above init input tensors copied from transformers models bart modeling_flax_bart flaxbartpretrainedmodel init_cache with bart whisper args batch_size int batch_size used for fast auto regressive decoding defines the batch size of the initialized cache max_length int maximum possible length for auto regressive decoding defines the sequence length of the initialized cache encoder_outputs union flaxbasemodeloutput tuple tuple jnp ndarray encoder_outputs consists of last_hidden_state optional hidden_states optional attentions last_hidden_state of shape batch_size sequence_length hidden_size optional is a sequence of hidden states at the output of the last layer of the encoder used in the cross attention of the decoder init input variables to retrieve cache we only need to call the decoder to init the cache returns example python from transformers import whisperprocessor flaxwhisperforconditionalgeneration from datasets import load_dataset processor whisperprocessor from_pretrained openai whisper tiny en model flaxwhisperforconditionalgeneration from_pretrained openai whisper tiny en from_pt true ds load_dataset hf internal testing librispeech_asr_dummy clean split validation inputs processor ds 0 audio array return_tensors np input_features inputs input_features encoder_outputs model encode input_features input_features handle any prng if needed returns example python from transformers import whisperprocessor flaxwhisperforconditionalgeneration from datasets import load_dataset import jax numpy as jnp processor whisperprocessor from_pretrained openai whisper tiny en model flaxwhisperforconditionalgeneration from_pretrained openai whisper tiny en from_pt true ds load_dataset hf internal testing librispeech_asr_dummy clean split validation input_features processor ds 0 audio array return_tensors np input_features encoder_outputs model encode input_features input_features decoder_start_token_id model config decoder_start_token_id decoder_input_ids jnp ones input_features shape 0 1 dtype i4 decoder_start_token_id outputs model decode decoder_input_ids encoder_outputs last_decoder_hidden_states outputs last_hidden_state handle any prng if needed if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used it has to be made sure that cache is marked as mutable so that it can be changed by flaxwhisperattention module add updated cache to model output prepare decoder inputs handle any prng if needed the dtype of the computation returns example python from transformers import whisperprocessor flaxwhisperforconditionalgeneration from datasets import load_dataset processor whisperprocessor from_pretrained openai whisper tiny en model flaxwhisperforconditionalgeneration from_pretrained openai whisper tiny en from_pt true ds load_dataset hf internal testing librispeech_asr_dummy clean split validation inputs processor ds 0 audio array return_tensors np input_features inputs input_features encoder_outputs model encode input_features input_features decoder_start_token_id model config decoder_start_token_id decoder_input_ids jnp ones inputs input_ids shape 0 1 dtype i4 decoder_start_token_id outputs model decode decoder_input_ids encoder_outputs last_decoder_hidden_states outputs last_hidden_state handle any prng if needed if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used it has to be made sure that cache is marked as mutable so that it can be changed by flaxwhisperattention module add updated cache to model output initializing the cache note that usually one would have to put 0 s in the attention_mask for x input_ids shape 1 and x cache_length but since the decoder uses a causal mask those positions are masked anyways thus we can create a single static attention_mask here which is more efficient for compilation returns transcription example python from transformers import whisperprocessor flaxwhisperforconditionalgeneration from datasets import load_dataset processor whisperprocessor from_pretrained openai whisper tiny en model flaxwhisperforconditionalgeneration from_pretrained openai whisper tiny en from_pt true ds load_dataset hf internal testing librispeech_asr_dummy clean split validation inputs processor ds 0 audio array return_tensors np input_features inputs input_features generated_ids model generate input_ids input_features transcription processor batch_decode generated_ids skip_special_tokens true 0 transcription mr quilter is the apostle of the middle classes and we are glad to welcome his gospel init input tensors handle any prng if needed returns transcription example python import jax numpy as jnp from transformers import autofeatureextractor flaxwhisperforaudioclassification from datasets import load_dataset feature_extractor autofeatureextractor from_pretrained sanchit gandhi whisper medium fleurs lang id model flaxwhisperforaudioclassification from_pretrained sanchit gandhi whisper medium fleurs lang id from_pt true ds load_dataset google fleurs all split validation streaming true sample next iter ds inputs feature_extractor sample audio array sampling_rate sample audio sampling_rate return_tensors np input_features inputs input_features logits model input_features logits predicted_class_ids jnp argmax logits item predicted_label model config id2label predicted_class_ids predicted_label af_za
import math
import random
from functools import partial
from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey

from ...generation.flax_logits_process import FlaxWhisperTimeStampLogitsProcessor
from ...modeling_flax_outputs import (
    FlaxBaseModelOutput,
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxCausalLMOutputWithCrossAttentions,
    FlaxSeq2SeqLMOutput,
    FlaxSeq2SeqModelOutput,
    FlaxSequenceClassifierOutput,
)
from ...modeling_flax_utils import (
    ACT2FN,
    FlaxPreTrainedModel,
    append_call_sample_docstring,
    append_replace_return_docstrings,
    overwrite_call_docstring,
)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_whisper import WhisperConfig


logger = logging.get_logger(__name__)


_CHECKPOINT_FOR_DOC = "openai/whisper-tiny"
_CONFIG_FOR_DOC = "WhisperConfig"

remat = nn_partitioning.remat


def sinusoidal_embedding_init(key, shape, dtype=jnp.float_) -> jax.Array:
    
    length, channels = shape
    if channels % 2 != 0:
        raise ValueError(
            f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels."
        )
    log_timescale_increment = math.log(10000) / (channels // 2 - 1)
    inv_timescales = jnp.exp(-log_timescale_increment * jnp.arange(channels // 2))
    scaled_time = jnp.arange(length).reshape(-1, 1) * inv_timescales.reshape(1, -1)
    return jnp.concatenate([jnp.sin(scaled_time), jnp.cos(scaled_time)], axis=1).astype(dtype)


WHISPER_START_DOCSTRING = r

WHISPER_INPUTS_DOCSTRING = r

WHISPER_ENCODE_INPUTS_DOCSTRING = r

WHISPER_DECODE_INPUTS_DOCSTRING = r


class FlaxWhisperAttention(nn.Module):
    config: WhisperConfig
    embed_dim: int
    num_heads: int
    dropout: float = 0.0
    causal: bool = False
    bias: bool = True
    dtype: jnp.dtype = jnp.float32

    def setup(self) -> None:
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {self.num_heads})."
            )

        dense = partial(
            nn.Dense,
            self.embed_dim,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
        )

        self.q_proj = dense(use_bias=self.bias)
        self.k_proj = dense(use_bias=False)
        self.v_proj = dense(use_bias=self.bias)
        self.out_proj = dense(use_bias=self.bias)

        if self.causal:
            self.causal_mask = make_causal_mask(
                jnp.ones((1, self.config.max_target_positions), dtype="bool"), dtype="bool"
            )

    def __call__(
        self,
        hidden_states: jnp.ndarray,
        key_value_states: Optional[jnp.ndarray] = None,
        attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
    ) -> Tuple[jnp.ndarray]:
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]

        query_states = self.q_proj(hidden_states)

        if is_cross_attention:
            key_states = self.k_proj(key_value_states)
            value_states = self.v_proj(key_value_states)
        else:
            key_states = self.k_proj(hidden_states)
            value_states = self.v_proj(hidden_states)

        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)

        if self.causal:
            query_length, key_length = query_states.shape[1], key_states.shape[1]
            if self.has_variable("cache", "cached_key"):
                mask_shift = self.variables["cache"]["cache_index"]
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_mask = lax.dynamic_slice(
                    self.causal_mask,
                    (0, 0, mask_shift, 0),
                    (1, 1, query_length, max_decoder_length),
                )
            else:
                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])

        
        if attention_mask is not None and self.causal:
            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
            attention_mask = combine_masks(attention_mask, causal_mask)
        elif self.causal:
            attention_mask = causal_mask
        elif attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))

        
        

        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
            key_states, value_states, attention_mask = self._concatenate_to_cache(
                key_states, value_states, query_states, attention_mask
            )

        
        if attention_mask is not None:
            
            attention_bias = lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
            )
        else:
            attention_bias = None

        dropout_rng = None
        if not deterministic and self.dropout > 0.0:
            dropout_rng = self.make_rng("dropout")

        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.dropout,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
            precision=None,
        )

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = self._merge_heads(attn_output)
        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights

    def _split_heads(self, hidden_state) -> jnp.ndarray:
        return hidden_state.reshape(hidden_state.shape[:2] + (self.num_heads, self.head_dim))

    def _merge_heads(self, hidden_state) -> jnp.ndarray:
        return hidden_state.reshape(hidden_state.shape[:2] + (self.embed_dim,))

    @nn.compact
    def _concatenate_to_cache(self, key, value, query, attention_mask) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
        
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))

        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = lax.dynamic_update_slice(cached_key.value, key, indices)
            value = lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            
            
            
            pad_mask = jnp.broadcast_to(
                jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
                tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
            )
            attention_mask = combine_masks(pad_mask, attention_mask)

        return key, value, attention_mask



class FlaxWhisperEncoderLayer(nn.Module):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self) -> None:
        self.embed_dim = self.config.d_model
        self.self_attn = FlaxWhisperAttention(
            config=self.config,
            embed_dim=self.embed_dim,
            num_heads=self.config.encoder_attention_heads,
            dropout=self.config.attention_dropout,
            dtype=self.dtype,
        )
        self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
        self.activation_fn = ACT2FN[self.config.activation_function]
        self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
        self.fc1 = nn.Dense(
            self.config.encoder_ffn_dim,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
        )
        self.fc2 = nn.Dense(
            self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
        )
        self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)

    def __call__(
        self,
        hidden_states: jnp.ndarray,
        attention_mask: jnp.ndarray,
        output_attentions: bool = True,
        deterministic: bool = True,
    ) -> Tuple[jnp.ndarray]:
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask)
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = self.fc2(hidden_states)
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class FlaxWhisperEncoderLayerCollection(nn.Module):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32  
    gradient_checkpointing: bool = False

    def setup(self):
        if self.gradient_checkpointing:
            FlaxWhisperEncoderCheckpointLayer = remat(FlaxWhisperEncoderLayer, static_argnums=(2, 3))
            self.layers = [
                FlaxWhisperEncoderCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.encoder_layers)
            ]
        else:
            self.layers = [
                FlaxWhisperEncoderLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.encoder_layers)
            ]
        self.layerdrop = self.config.encoder_layerdrop

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

        for encoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)
            
            dropout_probability = random.uniform(0, 1)
            if not deterministic and (dropout_probability < self.layerdrop):  
                layer_outputs = (None, None)
            else:
                layer_outputs = encoder_layer(
                    hidden_states,
                    attention_mask,
                    output_attentions,
                    deterministic,
                )
            hidden_states = layer_outputs[0]
            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        outputs = (hidden_states, all_hidden_states, all_attentions)

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )



class FlaxWhisperDecoderLayer(nn.Module):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self) -> None:
        self.embed_dim = self.config.d_model
        self.self_attn = FlaxWhisperAttention(
            config=self.config,
            embed_dim=self.embed_dim,
            num_heads=self.config.decoder_attention_heads,
            dropout=self.config.attention_dropout,
            causal=True,
            dtype=self.dtype,
        )
        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
        self.activation_fn = ACT2FN[self.config.activation_function]
        self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)

        self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
        self.encoder_attn = FlaxWhisperAttention(
            config=self.config,
            embed_dim=self.embed_dim,
            num_heads=self.config.decoder_attention_heads,
            dropout=self.config.attention_dropout,
            dtype=self.dtype,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
        self.fc1 = nn.Dense(
            self.config.decoder_ffn_dim,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
        )
        self.fc2 = nn.Dense(
            self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
        )
        self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)

    def __call__(
        self,
        hidden_states: jnp.ndarray,
        attention_mask: jnp.ndarray,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        output_attentions: bool = True,
        deterministic: bool = True,
    ) -> Tuple[jnp.ndarray]:
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
        )
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = residual + hidden_states

        
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states

            hidden_states = self.encoder_attn_layer_norm(hidden_states)
            hidden_states, cross_attn_weights = self.encoder_attn(
                hidden_states=hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
            )
            hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
            hidden_states = residual + hidden_states

        
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = self.fc2(hidden_states)
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        return outputs


class FlaxWhisperDecoderLayerCollection(nn.Module):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32  
    gradient_checkpointing: bool = False

    def setup(self):
        if self.gradient_checkpointing:
            FlaxWhisperDecoderCheckpointLayer = remat(FlaxWhisperDecoderLayer, static_argnums=(4, 5, 6))
            self.layers = [
                FlaxWhisperDecoderCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.decoder_layers)
            ]
        else:
            self.layers = [
                FlaxWhisperDecoderLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.decoder_layers)
            ]
        self.layerdrop = self.config.decoder_layerdrop

    def __call__(
        self,
        hidden_states,
        attention_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        deterministic: bool = True,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
                
            dropout_probability = random.uniform(0, 1)
            if not deterministic and (dropout_probability < self.layerdrop):
                layer_outputs = (None, None, None)
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    init_cache,
                    output_attentions,
                    deterministic,
                )

            hidden_states = layer_outputs[0]
            if output_attentions:
                all_self_attns += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
        )


class FlaxWhisperEncoder(nn.Module):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self) -> None:
        self.conv1 = nn.Conv(
            self.config.d_model,
            kernel_size=(3,),
            padding=1,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
            dtype=self.dtype,
        )
        self.conv2 = nn.Conv(
            self.config.d_model,
            kernel_size=(3,),
            strides=2,
            padding=1,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
            dtype=self.dtype,
        )

        self.dropout_layer = nn.Dropout(rate=self.config.dropout)

        self.layers = FlaxWhisperEncoderLayerCollection(
            self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )

        self.embed_positions = nn.Embed(
            self.config.max_source_positions,
            self.config.d_model,
            dtype=self.dtype,
            embedding_init=sinusoidal_embedding_init,
        )

        self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)

    def __call__(
        self,
        input_features: jnp.ndarray,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        deterministic: bool = True,
    ) -> Tuple[jnp.ndarray]:
        if input_features.shape[1:] != (self.config.num_mel_bins, self.config.max_source_positions * 2):
            raise ValueError(
                "input_features.shape[1:], must be equal to (self.config.num_mel_bins,"
                f" self.config.max_source_positions * 2) (got {input_features.shape[1:]}, but should be"
                f" ({self.config.num_mel_bins}, {self.config.max_source_positions * 2}))"
            )

        input_features = input_features.transpose(0, 2, 1)
        hidden_states = jax.nn.gelu(self.conv1(input_features), approximate=False)
        hidden_states = jax.nn.gelu(self.conv2(hidden_states), approximate=False)

        embed_positions = self.embed_positions(jnp.arange(self.config.max_source_positions))
        
        embed_positions = jax.lax.stop_gradient(embed_positions)
        hidden_states = hidden_states + embed_positions

        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)

        outputs = self.layers(
            hidden_states,
            attention_mask=None,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        last_hidden_states = outputs[0]
        last_hidden_states = self.layer_norm(last_hidden_states)

        
        hidden_states = None
        if output_hidden_states:
            hidden_states = outputs[1]
            hidden_states = hidden_states[:-1] + (last_hidden_states,)

        if not return_dict:
            outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutput(
            last_hidden_state=last_hidden_states,
            hidden_states=hidden_states,
            attentions=outputs.attentions,
        )


class FlaxWhisperDecoder(nn.Module):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self) -> None:
        self.embed_tokens = nn.Embed(self.config.vocab_size, self.config.d_model, dtype=self.dtype)
        self.embed_positions = nn.Embed(self.config.max_target_positions, self.config.d_model, dtype=self.dtype)

        self.layers = FlaxWhisperDecoderLayerCollection(
            self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )

        self.dropout_layer = nn.Dropout(rate=self.config.dropout)

        self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-5)

    def __call__(
        self,
        input_ids: jnp.ndarray,
        attention_mask: jnp.ndarray,
        position_ids: jnp.ndarray,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        deterministic: bool = True,
    ) -> Tuple[jnp.ndarray]:
        input_embeds = self.embed_tokens(input_ids)
        position_embeds = self.embed_positions(position_ids)

        hidden_states = input_embeds + position_embeds
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)

        outputs = self.layers(
            hidden_states,
            attention_mask=attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            deterministic=deterministic,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        last_hidden_states = outputs[0]
        last_hidden_states = self.layer_norm(last_hidden_states)

        
        hidden_states = None
        if output_hidden_states:
            hidden_states = outputs[1]
            hidden_states = hidden_states[:-1] + (last_hidden_states,)

        if not return_dict:
            outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=last_hidden_states,
            hidden_states=hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


class FlaxWhisperModule(nn.Module):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self) -> None:
        self.encoder = FlaxWhisperEncoder(
            self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.decoder = FlaxWhisperDecoder(
            self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )

    def __call__(
        self,
        input_features: jnp.ndarray,
        decoder_input_ids: jnp.ndarray,
        decoder_attention_mask: jnp.ndarray,
        decoder_position_ids: jnp.ndarray,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        deterministic: bool = True,
    ):
        encoder_outputs = self.encoder(
            input_features,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=deterministic,
        )

        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            position_ids=decoder_position_ids,
            encoder_hidden_states=encoder_outputs[0],
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=deterministic,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return FlaxSeq2SeqModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

    def _get_encoder_module(self):
        return self.encoder

    def _get_decoder_module(self):
        return self.decoder


class FlaxWhisperPreTrainedModel(FlaxPreTrainedModel):
    config_class = WhisperConfig
    base_model_prefix: str = "model"
    main_input_name = "input_features"
    module_class: nn.Module = None

    def __init__(
        self,
        config: WhisperConfig,
        input_shape: Tuple[int] = None,
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        gradient_checkpointing: bool = False,
        **kwargs,
    ):
        module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
        if input_shape is None:
            input_shape = (1, config.num_mel_bins, 2 * config.max_source_positions)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

    def enable_gradient_checkpointing(self):
        self._module = self.module_class(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=True,
        )

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        
        input_features = jnp.zeros(input_shape, dtype="f4")
        input_features = input_features.at[(..., -1)].set(self.config.eos_token_id)

        decoder_input_ids = jnp.zeros((input_shape[0], 1), dtype="i4")
        decoder_attention_mask = jnp.ones_like(decoder_input_ids)

        batch_size, sequence_length = decoder_input_ids.shape
        decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}

        random_params = self.module.init(
            rngs,
            input_features=input_features,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            decoder_position_ids=decoder_position_ids,
        )["params"]

        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    
    def init_cache(self, batch_size, max_length, encoder_outputs):
        r
        
        decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        decoder_attention_mask = jnp.ones_like(decoder_input_ids)
        decoder_position_ids = jnp.broadcast_to(
            jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
        )

        def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
            decoder_module = module._get_decoder_module()
            return decoder_module(
                decoder_input_ids,
                decoder_attention_mask,
                decoder_position_ids,
                **kwargs,
            )

        init_variables = self.module.init(
            jax.random.PRNGKey(0),
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            decoder_position_ids=decoder_position_ids,
            encoder_hidden_states=encoder_outputs[0],
            init_cache=True,
            method=_decoder_forward,  
        )
        return unfreeze(init_variables["cache"])

    @add_start_docstrings(WHISPER_ENCODE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=WhisperConfig)
    def encode(
        self,
        input_features: jnp.ndarray,
        attention_mask: Optional[jnp.ndarray] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
        **kwargs,
    ):
        r
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        def _encoder_forward(module, input_features, **kwargs):
            encode_module = module._get_encoder_module()
            return encode_module(input_features, **kwargs)

        return self.module.apply(
            {"params": params or self.params},
            input_features=jnp.array(input_features, dtype="f4"),
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
            method=_encoder_forward,
        )

    @add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=WhisperConfig)
    def decode(
        self,
        decoder_input_ids,
        encoder_outputs,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        decoder_attention_mask: Optional[jnp.ndarray] = None,
        decoder_position_ids: Optional[jnp.ndarray] = None,
        past_key_values: dict = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        r

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        encoder_hidden_states = encoder_outputs[0]

        batch_size, sequence_length = decoder_input_ids.shape
        if decoder_position_ids is None:
            if past_key_values is not None:
                raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")

            if decoder_attention_mask is not None:
                decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
            else:
                decoder_position_ids = jnp.broadcast_to(
                    jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
                )

        if decoder_attention_mask is None:
            decoder_attention_mask = jnp.ones((batch_size, sequence_length))

        
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        
        
        
        if past_key_values:
            inputs["cache"] = past_key_values
            mutable = ["cache"]
        else:
            mutable = False

        def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
            decoder_module = module._get_decoder_module()
            return decoder_module(
                input_ids=decoder_input_ids,
                attention_mask=decoder_attention_mask,
                position_ids=decoder_position_ids,
                **kwargs,
            )

        outputs = self.module.apply(
            inputs,
            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
            decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
            encoder_hidden_states=encoder_hidden_states,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
            mutable=mutable,
            method=_decoder_forward,
        )

        
        if past_key_values is not None and return_dict:
            outputs, past = outputs
            outputs["past_key_values"] = unfreeze(past["cache"])
            return outputs
        elif past_key_values is not None and not return_dict:
            outputs, past = outputs
            outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]

        return outputs

    @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
    def __call__(
        self,
        input_features: jnp.ndarray,
        decoder_input_ids: jnp.ndarray,
        attention_mask: Optional[jnp.ndarray] = None,
        decoder_attention_mask: Optional[jnp.ndarray] = None,
        position_ids: Optional[jnp.ndarray] = None,
        decoder_position_ids: Optional[jnp.ndarray] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        
        if decoder_position_ids is None:
            if decoder_attention_mask is not None:
                decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
            else:
                batch_size, sequence_length = decoder_input_ids.shape
                decoder_position_ids = jnp.broadcast_to(
                    jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
                )
        if decoder_attention_mask is None:
            decoder_attention_mask = jnp.ones_like(decoder_input_ids)

        
        rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}

        return self.module.apply(
            {"params": params or self.params},
            input_features=jnp.array(input_features, dtype="f4"),
            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
            decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
        )


@add_start_docstrings(
    "The bare Whisper Model transformer outputting raw hidden-states without any specific head on top.",
    WHISPER_START_DOCSTRING,
)
class FlaxWhisperModel(FlaxWhisperPreTrainedModel):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32  
    module_class = FlaxWhisperModule


append_call_sample_docstring(FlaxWhisperModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)


class FlaxWhisperForConditionalGenerationModule(nn.Module):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self) -> None:
        self.model = FlaxWhisperModule(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.lm_head = nn.Dense(
            self.config.vocab_size,
            use_bias=False,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
        )

    def _get_encoder_module(self):
        return self.model.encoder

    def _get_decoder_module(self):
        return self.model.decoder

    def __call__(
        self,
        input_features,
        decoder_input_ids,
        decoder_attention_mask: jnp.ndarray = None,
        decoder_position_ids: jnp.ndarray = None,
        position_ids: jnp.ndarray = None,
        attention_mask: jnp.ndarray = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        deterministic: bool = True,
    ):
        outputs = self.model(
            input_features=input_features,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            decoder_position_ids=decoder_position_ids,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=deterministic,
        )

        hidden_states = outputs[0]

        if self.config.tie_word_embeddings:
            shared_embedding = self.model.decoder.embed_tokens.variables["params"]["embedding"]
            lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else:
            lm_logits = self.lm_head(hidden_states)

        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return output

        return FlaxSeq2SeqLMOutput(
            logits=lm_logits,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )


@add_start_docstrings("The Whisper Model with a language modeling head.", WHISPER_START_DOCSTRING)
class FlaxWhisperForConditionalGeneration(FlaxWhisperPreTrainedModel):
    module_class = FlaxWhisperForConditionalGenerationModule
    dtype: jnp.dtype = jnp.float32

    @add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=WhisperConfig)
    def decode(
        self,
        decoder_input_ids,
        encoder_outputs,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        decoder_attention_mask: Optional[jnp.ndarray] = None,
        decoder_position_ids: Optional[jnp.ndarray] = None,
        past_key_values: dict = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        r
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        encoder_hidden_states = encoder_outputs[0]

        batch_size, sequence_length = decoder_input_ids.shape
        if decoder_position_ids is None:
            if past_key_values is not None:
                raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")

            if decoder_attention_mask is not None:
                decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
            else:
                decoder_position_ids = jnp.broadcast_to(
                    jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
                )
        if decoder_attention_mask is None:
            decoder_attention_mask = jnp.ones((batch_size, sequence_length), dtype="i4")

        
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        
        
        
        if past_key_values:
            inputs["cache"] = past_key_values
            mutable = ["cache"]
        else:
            mutable = False

        def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
            decoder_module = module._get_decoder_module()
            outputs = decoder_module(
                input_ids=decoder_input_ids,
                attention_mask=decoder_attention_mask,
                position_ids=decoder_position_ids,
                **kwargs,
            )
            hidden_states = outputs[0]

            if self.config.tie_word_embeddings:
                shared_embedding = module.model.decoder.embed_tokens.variables["params"]["embedding"]
                lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
            else:
                lm_logits = module.lm_head(hidden_states)

            return lm_logits, outputs

        outputs = self.module.apply(
            inputs,
            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
            decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
            encoder_hidden_states=encoder_hidden_states,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
            mutable=mutable,
            method=_decoder_forward,
        )

        if past_key_values is None:
            lm_logits, decoder_outputs = outputs
        else:
            (lm_logits, decoder_outputs), past = outputs

        if return_dict:
            outputs = FlaxCausalLMOutputWithCrossAttentions(
                logits=lm_logits,
                hidden_states=decoder_outputs.hidden_states,
                attentions=decoder_outputs.attentions,
                cross_attentions=decoder_outputs.cross_attentions,
            )
        else:
            outputs = (lm_logits,) + decoder_outputs[1:]

        
        if past_key_values is not None and return_dict:
            outputs["past_key_values"] = unfreeze(past["cache"])
            return outputs
        elif past_key_values is not None and not return_dict:
            outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]

        return outputs

    def generate(
        self,
        input_features,
        generation_config=None,
        logits_processor=None,
        return_timestamps=None,
        task=None,
        language=None,
        is_multilingual=None,
        **kwargs,
    ):
        if generation_config is None:
            generation_config = self.generation_config

        if return_timestamps is not None:
            generation_config.return_timestamps = return_timestamps

        if task is not None:
            generation_config.task = task

        if is_multilingual is not None:
            generation_config.is_multilingual = is_multilingual

        if language is not None:
            generation_config.language = language

        if kwargs is not None and "decoder_input_ids" in kwargs:
            decoder_input_length = len(kwargs["decoder_input_ids"])
        else:
            decoder_input_length = 1

        forced_decoder_ids = []

        if hasattr(generation_config, "is_multilingual") and generation_config.is_multilingual:
            if hasattr(generation_config, "language"):
                forced_decoder_ids.append((1, generation_config.lang_to_id[generation_config.language]))
            else:
                forced_decoder_ids.append((1, None))

            if hasattr(generation_config, "task"):
                forced_decoder_ids.append((2, generation_config.task_to_id[generation_config.task]))
            else:
                forced_decoder_ids.append((2, generation_config.task_to_id["transcribe"]))

        if (
            hasattr(generation_config, "return_timestamps") and generation_config.return_timestamps
        ) or return_timestamps:
            logits_processor = [
                FlaxWhisperTimeStampLogitsProcessor(generation_config, self.config, decoder_input_length)
            ]
        else:
            if forced_decoder_ids and forced_decoder_ids[-1][0] != generation_config.no_timestamps_token_id:
                idx = forced_decoder_ids[-1][0] + 1 if forced_decoder_ids else 1
                forced_decoder_ids.append((idx, generation_config.no_timestamps_token_id))

        if len(forced_decoder_ids) > 0:
            generation_config.forced_decoder_ids = forced_decoder_ids

        return super().generate(
            input_features,
            generation_config,
            logits_processor=logits_processor,
            **kwargs,
        )

    def prepare_inputs_for_generation(
        self,
        decoder_input_ids,
        max_length,
        attention_mask: Optional[jax.Array] = None,
        decoder_attention_mask: Optional[jax.Array] = None,
        encoder_outputs=None,
        **kwargs,
    ):
        
        batch_size, seq_length = decoder_input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
        
        
        
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if decoder_attention_mask is not None:
            position_ids = decoder_attention_mask.cumsum(-1) - 1
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
        else:
            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))

        return {
            "past_key_values": past_key_values,
            "encoder_outputs": encoder_outputs,
            "encoder_attention_mask": attention_mask,
            "decoder_attention_mask": extended_attention_mask,
            "decoder_position_ids": position_ids,
        }

    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
        return model_kwargs


FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING = r

overwrite_call_docstring(
    FlaxWhisperForConditionalGeneration, WHISPER_INPUTS_DOCSTRING + FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING
)
append_replace_return_docstrings(
    FlaxWhisperForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
)


class FlaxWhisperForAudioClassificationModule(nn.Module):
    config: WhisperConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self) -> None:
        self.encoder = FlaxWhisperEncoder(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.config.is_encoder_decoder = False
        num_layers = self.config.num_hidden_layers + 1
        if self.config.use_weighted_layer_sum:
            self.layer_weights = jnp.repeat(1 / num_layers, num_layers)
        self.projector = nn.Dense(self.config.classifier_proj_size, dtype=self.dtype)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_features,
        encoder_outputs=None,
        output_attentions=None,
        output_hidden_states: bool = True,
        return_dict: bool = True,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_features,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )

        if self.config.use_weighted_layer_sum:
            hidden_states = jnp.stack(encoder_outputs, axis=1)
            norm_weights = jax.nn.softmax(self.layer_weights, axis=-1)
            hidden_states = jnp.sum(hidden_states * jnp.reshape(norm_weights, [-1, 1, 1]), axis=1)
        else:
            hidden_states = encoder_outputs[0]

        hidden_states = self.projector(hidden_states)
        pooled_output = jnp.mean(hidden_states, axis=1)

        logits = self.classifier(pooled_output)

        if not return_dict:
            return (logits,) + encoder_outputs[1:]

        return FlaxSequenceClassifierOutput(
            logits=logits,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


@add_start_docstrings("The Whisper Model with an audio classification head on top.", WHISPER_START_DOCSTRING)
class FlaxWhisperForAudioClassification(FlaxWhisperPreTrainedModel):
    module_class = FlaxWhisperForAudioClassificationModule
    dtype: jnp.dtype = jnp.float32

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        
        input_features = jnp.zeros(input_shape, dtype="f4")
        input_features = input_features.at[(..., -1)].set(self.config.eos_token_id)

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}

        random_params = self.module.init(
            rngs,
            input_features=input_features,
        )["params"]

        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
    def __call__(
        self,
        input_features: jnp.ndarray,
        attention_mask: Optional[jnp.ndarray] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
        **kwargs,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        return self.module.apply(
            {"params": params or self.params},
            input_features=jnp.array(input_features, dtype="f4"),
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            rngs=rngs,
        )


FLAX_WHISPER_AUDIO_CLASSIFICATION_DOCSTRING = r

overwrite_call_docstring(
    FlaxWhisperForAudioClassification, WHISPER_INPUTS_DOCSTRING + FLAX_WHISPER_AUDIO_CLASSIFICATION_DOCSTRING
)
append_replace_return_docstrings(
    FlaxWhisperForAudioClassification, output_type=FlaxSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC
)