



from collections import OrderedDict

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


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


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

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

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

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

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

a_   :int        =      OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

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

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

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

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

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

a_   :Optional[int]        =      OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

a_   :Dict        =      OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

a_   :Dict        =      OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

a_   :str        =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
a_   :List[str]        =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
a_   :List[Any]        =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
a_   :Dict        =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
a_   :Optional[int]        =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
a_   :Optional[int]        =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
a_   :Union[str, Any]        =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
a_   :str        =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
a_   :Any        =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
a_   :int        =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
a_   :Dict        =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
a_   :Any        =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
a_   :Dict        =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
a_   :List[str]        =      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_MAPPING


a_   :Dict        =      auto_class_update(FlaxAutoModel)




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_PRETRAINING_MAPPING


a_   :Tuple        =      auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_CAUSAL_LM_MAPPING


a_   :Optional[int]        =      auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_MASKED_LM_MAPPING


a_   :int        =      auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


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




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


a_   :Union[str, Any]        =      auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING


a_   :List[Any]        =      auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


a_   :Dict        =      auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING


a_   :Dict        =      auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


a_   :str        =      auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


a_   :int        =      auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING


a_   :Dict        =      auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")




class 					snake_case__						(       _BaseAutoModelClass     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING


a_   :Optional[int]        =      auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)











import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a_   :Tuple        =      logging.get_logger(__name__)

a_   :List[Any]        =      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
a_   :Optional[int]        =      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
def        lowercase_							(A : Union[str, Any]		,					A : str		,					A : Dict		,					A : Optional[Any]		,					A : Optional[Any]      ):
       for attribute in key.split('.'      ):
              snake_case__							:	Any   	=	getattr(A		,					A      )

       if weight_type is not None:
              snake_case__							:	Optional[Any]   	=	getattr(A		,					A      ).shape
       else:
              snake_case__							:	Optional[int]   	=	hf_pointer.shape

       assert hf_shape == value.shape, (
           F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
           F''' {value.shape} for {full_name}'''
       )

       if weight_type == "weight":
              snake_case__							:	Tuple   	=	value
       elif weight_type == "weight_g":
              snake_case__							:	Tuple   	=	value
       elif weight_type == "weight_v":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "bias":
              snake_case__							:	List[Any]   	=	value
       else:
              snake_case__							:	Optional[Any]   	=	value

       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''      )
def        lowercase_							(A : str		,					A : Any      ):
       snake_case__							:	Union[str, Any]   	=	[]
       snake_case__							:	Union[str, Any]   	=	fairseq_model.state_dict()

       snake_case__							:	Union[str, Any]   	=	hf_model.feature_extractor
       snake_case__							:	Any   	=	hf_model.adapter

       for name, value in fairseq_dict.items():
              snake_case__							:	Any   	=	False
              if "conv_layers" in name:
                     load_conv_layer(
                         A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == 'group'		,					)
                     snake_case__							:	List[Any]   	=	True
              elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']      ):
                     load_adapter(A		,					A		,					A		,					A      )
                     snake_case__							:	Optional[Any]   	=	True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
                                   snake_case__							:	Tuple   	=	True
                                   if "*" in mapped_key:
                                          snake_case__							:	List[Any]   	=	name.split(A      )[0].split('.'      )[-2]
                                          snake_case__							:	Optional[int]   	=	mapped_key.replace('*'		,					A      )
                                   if "weight_g" in name:
                                          snake_case__							:	Optional[int]   	=	'weight_g'
                                   elif "weight_v" in name:
                                          snake_case__							:	Optional[Any]   	=	'weight_v'
                                   elif "bias" in name:
                                          snake_case__							:	Union[str, Any]   	=	'bias'
                                   elif "weight" in name:
                                          snake_case__							:	Optional[int]   	=	'weight'
                                   else:
                                          snake_case__							:	Tuple   	=	None
                                   set_recursively(A		,					A		,					A		,					A		,					A      )
                            continue
              if not is_used:
                     unused_weights.append(A      )

       logger.warning(F'''Unused weights: {unused_weights}'''      )
def        lowercase_							(A : Union[str, Any]		,					A : Any		,					A : str		,					A : str		,					A : int      ):
       snake_case__							:	str   	=	full_name.split('conv_layers.'      )[-1]
       snake_case__							:	Optional[int]   	=	name.split('.'      )
       snake_case__							:	Tuple   	=	int(items[0]      )
       snake_case__							:	Any   	=	int(items[1]      )

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                     )
                     snake_case__							:	Union[str, Any]   	=	value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                     )
                     snake_case__							:	Union[str, Any]   	=	value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                         " found."
                     )
                     snake_case__							:	Optional[int]   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                     )
                     snake_case__							:	Optional[Any]   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
       else:
              unused_weights.append(A      )
def        lowercase_							(A : Optional[Any]		,					A : Any		,					A : Tuple		,					A : Any      ):
       snake_case__							:	List[str]   	=	full_name.split('adaptor.'      )[-1]
       snake_case__							:	Tuple   	=	name.split('.'      )

       if items[1].isdigit():
              snake_case__							:	Optional[int]   	=	int(items[1]      )
       else:
              snake_case__							:	Any   	=	None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
                            snake_case__							:	List[Any]   	=	value
                            logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''      )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
                            snake_case__							:	int   	=	value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
                            snake_case__							:	str   	=	value
                            logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''      )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
                            snake_case__							:	Dict   	=	value
                            logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''      )
       elif isinstance(A		,					A      ):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
                     snake_case__							:	List[str]   	=	value
                     logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
                     snake_case__							:	List[str]   	=	value
                     logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
       else:
              unused_weights.append(A      )
def        lowercase_							(A : int      ):
       snake_case__	,				snake_case__							:	Union[str, Any]   	=	emb.weight.shape
       snake_case__							:	int   	=	nn.Linear(A		,					A		,					bias=A      )
       snake_case__							:	Optional[Any]   	=	emb.weight.data
       return lin_layer
@torch.no_grad()
def        lowercase_							(A : Tuple		,					A : Tuple		,					A : Any		,					A : Optional[Any]		,					A : int		,					A : Optional[Any]		,					A : Union[str, Any]		,					A : Union[str, Any]		,					A : Optional[Any]		,					A : List[Any]		,					A : Union[str, Any]		,					):
       snake_case__							:	Optional[Any]   	=	WavaVecaConfig.from_pretrained(
           A		,					add_adapter=A		,					adapter_stride=A		,					adapter_kernel_size=A		,					use_auth_token=A		,					output_hidden_size=A		,					)
       snake_case__							:	Dict   	=	MBartConfig.from_pretrained(A      )

       # load model
       snake_case__	,				snake_case__	,				snake_case__							:	Any   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]		,					arg_overrides={
               'config_yaml': config_yaml_path,
               'data': '/'.join(dict_path.split('/'      )[:-1]      ),
               'w2v_path': checkpoint_path,
               'load_pretrained_decoder_from': None,
           }		,					)
       snake_case__							:	List[Any]   	=	model[0].eval()

       # load feature extractor
       snake_case__							:	str   	=	WavaVecaFeatureExtractor.from_pretrained(A		,					use_auth_token=A      )

       # set weights for wav2vec2 encoder
       snake_case__							:	List[str]   	=	WavaVecaModel(A      )

       recursively_load_weights_wavaveca(model.encoder		,					A      )

       # load decoder weights
       snake_case__							:	Any   	=	MBartForCausalLM(A      )
       snake_case__	,				snake_case__							:	int   	=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,					strict=A      )
       logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''      )
       logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''      )

       snake_case__							:	Union[str, Any]   	=	SpeechEncoderDecoderModel(encoder=A		,					decoder=A      )
       snake_case__							:	str   	=	False

       snake_case__							:	int   	=	MBartaaTokenizer(A      )
       tokenizer.save_pretrained(A      )

       snake_case__							:	Any   	=	hf_wavavec.config.to_dict()
       snake_case__							:	Tuple   	=	tokenizer.pad_token_id
       snake_case__							:	Union[str, Any]   	=	tokenizer.bos_token_id
       snake_case__							:	Dict   	=	tokenizer.eos_token_id
       snake_case__							:	Optional[int]   	=	'mbart50'
       snake_case__							:	Union[str, Any]   	=	'wav2vec2'

       snake_case__							:	List[str]   	=	tokenizer.eos_token_id
       snake_case__							:	Union[str, Any]   	=	2_5_0_0_0_4
       snake_case__							:	int   	=	tokenizer.eos_token_id

       snake_case__							:	Union[str, Any]   	=	SpeechEncoderDecoderConfig.from_dict(A      )

       hf_wavavec.save_pretrained(A      )
       feature_extractor.save_pretrained(A      )


if __name__ == "__main__":
 a_   :str        =      argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
 parser.add_argument(
     "--encoder_config_path",
     default="facebook/wav2vec2-xls-r-1b",
     type=str,
     help="Path to hf encoder wav2vec2 checkpoint config",
 )
 parser.add_argument(
     "--decoder_config_path",
     default="facebook/mbart-large-50-one-to-many-mmt",
     type=str,
     help="Path to hf decoder checkpoint config",
 )
 parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
 parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
 parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
 parser.add_argument("--encoder_output_dim", default=1_024, type=int, help="encoder output dim")
 parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config")

 a_   :Union[str, Any]        =      parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )






