





import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
UpperCAmelCase_     							= logging.get_logger('transformers.models.speecht5')

UpperCAmelCase_     							= {
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
UpperCAmelCase_     							= {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
UpperCAmelCase_     							= {
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
UpperCAmelCase_     							= {
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
UpperCAmelCase_     							= {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
UpperCAmelCase_     							= {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
UpperCAmelCase_     							= {
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
UpperCAmelCase_     							= {
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
UpperCAmelCase_     							= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCAmelCase_     							= {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase_     							= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase_     							= []
UpperCAmelCase_     							= [
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
UpperCAmelCase_     							= IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
UpperCAmelCase_     							= IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
UpperCAmelCase_     							= IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			List[str]		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			int      )      ->	int:




    for attribute in key.split('''.'''      ):
        UpperCamelCase__							:				Optional[Any]          =      getattr(lowerCAmelCase__		,   lowerCAmelCase__      )

    if weight_type is not None:
        UpperCamelCase__							:				Optional[int]          =      getattr(lowerCAmelCase__		,   lowerCAmelCase__      ).shape
    else:
        UpperCamelCase__							:				Tuple          =      hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            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":
        UpperCamelCase__							:				str          =      value
    elif weight_type == "weight_g":
        UpperCamelCase__							:				Tuple          =      value
    elif weight_type == "weight_v":
        UpperCamelCase__							:				Union[str, Any]          =      value
    elif weight_type == "bias":
        UpperCamelCase__							:				Union[str, Any]          =      value
    elif weight_type == "running_mean":
        UpperCamelCase__							:				List[Any]          =      value
    elif weight_type == "running_var":
        UpperCamelCase__							:				Optional[Any]          =      value
    elif weight_type == "num_batches_tracked":
        UpperCamelCase__							:				Dict          =      value
    else:
        UpperCamelCase__							:				List[Any]          =      value

    logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			Optional[Any]      )      ->	int:




    for key in ignore_keys:
        if key.endswith('''.*'''      ):
            if name.startswith(key[:-1]      ):
                return True
        elif ".*." in key:
            UpperCamelCase__							:				Optional[Any]          =      key.split('''.*.'''      )
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			str      )      ->	int:
    UpperCamelCase__							:				Optional[int]          =      []

    if task == "s2t":
        UpperCamelCase__							:				int          =      hf_model.speechta.encoder.prenet.feature_encoder
        UpperCamelCase__							:				Optional[Any]          =      MAPPING_S2T
        UpperCamelCase__							:				Optional[Any]          =      IGNORE_KEYS_S2T
    elif task == "t2s":
        UpperCamelCase__							:				Any          =      None
        UpperCamelCase__							:				Dict          =      MAPPING_T2S
        UpperCamelCase__							:				str          =      IGNORE_KEYS_T2S
    elif task == "s2s":
        UpperCamelCase__							:				Union[str, Any]          =      hf_model.speechta.encoder.prenet.feature_encoder
        UpperCamelCase__							:				Tuple          =      MAPPING_S2S
        UpperCamelCase__							:				Any          =      IGNORE_KEYS_S2S
    else:
        raise ValueError(f"Unsupported task: {task}"      )

    for name, value in fairseq_dict.items():
        if should_ignore(lowerCAmelCase__		,   lowerCAmelCase__      ):
            logger.info(f"{name} was ignored"      )
            continue

        UpperCamelCase__							:				Optional[Any]          =      False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCAmelCase__		,   lowerCAmelCase__		,   lowerCAmelCase__		,   lowerCAmelCase__		,   hf_model.config.feat_extract_norm == '''group'''		,   )
            UpperCamelCase__							:				List[Any]          =      True
        else:
            for key, mapped_key in MAPPING.items():
                # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                if "*" in key:
                    UpperCamelCase__							:				Optional[Any]          =      key.split('''.*.'''      )
                    if prefix in name and suffix in name:
                        UpperCamelCase__							:				Optional[int]          =      suffix

                            # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                if key in name:
                    UpperCamelCase__							:				Tuple          =      True
                    if "*" in mapped_key:
                        UpperCamelCase__							:				List[Any]          =      name.split(lowerCAmelCase__      )[0].split('''.'''      )[-2]
                        UpperCamelCase__							:				Dict          =      mapped_key.replace('''*'''		,   lowerCAmelCase__      )
                    if "weight_g" in name:
                        UpperCamelCase__							:				Optional[int]          =      'weight_g'
                    elif "weight_v" in name:
                        UpperCamelCase__							:				Any          =      'weight_v'
                    elif "bias" in name:
                        UpperCamelCase__							:				List[Any]          =      'bias'
                    elif "weight" in name:
                        UpperCamelCase__							:				Optional[Any]          =      'weight'
                    elif "running_mean" in name:
                        UpperCamelCase__							:				Optional[Any]          =      'running_mean'
                    elif "running_var" in name:
                        UpperCamelCase__							:				Optional[Any]          =      'running_var'
                    elif "num_batches_tracked" in name:
                        UpperCamelCase__							:				Union[str, Any]          =      'num_batches_tracked'
                    else:
                        UpperCamelCase__							:				Tuple          =      None
                    set_recursively(lowerCAmelCase__		,   lowerCAmelCase__		,   lowerCAmelCase__		,   lowerCAmelCase__		,   lowerCAmelCase__      )
                continue
        if not is_used:
            unused_weights.append(lowerCAmelCase__      )

    logger.warning(f"Unused weights: {unused_weights}"      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[str]		,   __UpperCAmelCase:			Union[str, Any]		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			Tuple      )      ->	List[Any]:
    UpperCamelCase__							:				List[str]          =      full_name.split('''conv_layers.'''      )[-1]
    UpperCamelCase__							:				Optional[Any]          =      name.split('''.'''      )
    UpperCamelCase__							:				str          =      int(items[0]      )
    UpperCamelCase__							:				Any          =      int(items[1]      )

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."      )
            UpperCamelCase__							:				Optional[int]          =      value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."      )
            UpperCamelCase__							:				List[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:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."      )
            UpperCamelCase__							:				List[str]          =      value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."      )
            UpperCamelCase__							:				List[str]          =      value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
    else:
        unused_weights.append(lowerCAmelCase__      )




@torch.no_grad()
def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Optional[int]=None		,   __UpperCAmelCase:			Any=None		,   __UpperCAmelCase:			Tuple=None		,   )      ->	List[Any]:




    if config_path is not None:
        UpperCamelCase__							:				int          =      SpeechTaConfig.from_pretrained(lowerCAmelCase__      )
    else:
        UpperCamelCase__							:				int          =      SpeechTaConfig()

    if task == "s2t":
        UpperCamelCase__							:				Dict          =      config.max_text_positions
        UpperCamelCase__							:				Dict          =      SpeechTaForSpeechToText(lowerCAmelCase__      )
    elif task == "t2s":
        UpperCamelCase__							:				Any          =      1876
        UpperCamelCase__							:				int          =      600
        UpperCamelCase__							:				Dict          =      config.max_speech_positions
        UpperCamelCase__							:				Any          =      SpeechTaForTextToSpeech(lowerCAmelCase__      )
    elif task == "s2s":
        UpperCamelCase__							:				Dict          =      1876
        UpperCamelCase__							:				str          =      config.max_speech_positions
        UpperCamelCase__							:				str          =      SpeechTaForSpeechToSpeech(lowerCAmelCase__      )
    else:
        raise ValueError(f"Unknown task name: {task}"      )

    if vocab_path:
        UpperCamelCase__							:				Union[str, Any]          =      SpeechTaTokenizer(lowerCAmelCase__		,   model_max_length=config.max_text_positions      )

        # Mask token behaves like a normal word, i.e. include the space before it
        UpperCamelCase__							:				Any          =      AddedToken('''<mask>'''		,   lstrip=lowerCAmelCase__		,   rstrip=lowerCAmelCase__      )
        UpperCamelCase__							:				Dict          =      mask_token
        tokenizer.add_special_tokens({'''mask_token''': mask_token}      )
        tokenizer.add_tokens(['''<ctc_blank>''']      )

    UpperCamelCase__							:				str          =      SpeechTaFeatureExtractor()
    UpperCamelCase__							:				Optional[int]          =      SpeechTaProcessor(tokenizer=lowerCAmelCase__		,   feature_extractor=lowerCAmelCase__      )
    processor.save_pretrained(lowerCAmelCase__      )

    UpperCamelCase__							:				List[Any]          =      torch.load(lowerCAmelCase__      )
    recursively_load_weights(fairseq_checkpoint['''model''']		,   lowerCAmelCase__		,   lowerCAmelCase__      )

    model.save_pretrained(lowerCAmelCase__      )

    if repo_id:
        print('''Pushing to the hub...'''      )
        processor.push_to_hub(lowerCAmelCase__      )
        model.push_to_hub(lowerCAmelCase__      )


if __name__ == "__main__":
  UpperCAmelCase_     							= argparse.ArgumentParser()
  parser.add_argument(
      '--task',
      default='s2t',
      type=str,
      help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
  )
  parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
  parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  parser.add_argument(
      '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
  )

  UpperCAmelCase_     							= parser.parse_args()
  convert_speechta_checkpoint(
      args.task,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.vocab_path,
      args.push_to_hub,
  )






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__		:				int          =	{"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Optional[int]          =	["""MBartTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Dict          =	["""MBartTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Any          =	[
         """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MBartForCausalLM""",
         """MBartForConditionalGeneration""",
         """MBartForQuestionAnswering""",
         """MBartForSequenceClassification""",
         """MBartModel""",
         """MBartPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """TFMBartForConditionalGeneration""",
         """TFMBartModel""",
         """TFMBartPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """FlaxMBartForConditionalGeneration""",
         """FlaxMBartForQuestionAnswering""",
         """FlaxMBartForSequenceClassification""",
         """FlaxMBartModel""",
         """FlaxMBartPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowercase__		:				int          =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





