import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def __a (	__lowerCamelCase			:						Tuple				)	->							List[Any]:
      '''simple docstring'''


      lowercase_     =      [
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "decoder.output_projection.weight",
          "_float_tensor",
          "encoder.embed_positions._float_tensor",
          "decoder.embed_positions._float_tensor",
      ]
      for k in ignore_keys:
            state_dict.pop(__lowerCamelCase ,		__lowerCamelCase				)


def __a (	__lowerCamelCase			:						Optional[Any]				)	->							Any:
      '''simple docstring'''


      lowercase_ ,							lowercase_     =      emb.weight.shape
      lowercase_     =      nn.Linear(__lowerCamelCase ,		__lowerCamelCase ,		bias=__lowerCamelCase				)
      lowercase_     =      emb.weight.data
      return lin_layer


def __a (	__lowerCamelCase			:						Any				)	->							Union[str, Any]:
      '''simple docstring'''


      lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)
      lowercase_     =      mam_aaa["args"] or mam_aaa["cfg"]["model"]
      lowercase_     =      mam_aaa["model"]
      remove_ignore_keys_(__lowerCamelCase				)
      lowercase_     =      state_dict["encoder.embed_tokens.weight"].shape[0]

      lowercase_     =      MaMaaaConfig(
          vocab_size=__lowerCamelCase ,		max_position_embeddings=1_024 ,		encoder_layers=args.encoder_layers ,		decoder_layers=args.decoder_layers ,		encoder_attention_heads=args.encoder_attention_heads ,		decoder_attention_heads=args.decoder_attention_heads ,		encoder_ffn_dim=args.encoder_ffn_embed_dim ,		decoder_ffn_dim=args.decoder_ffn_embed_dim ,		d_model=args.encoder_embed_dim ,		encoder_layerdrop=args.encoder_layerdrop ,		decoder_layerdrop=args.decoder_layerdrop ,		dropout=args.dropout ,		attention_dropout=args.attention_dropout ,		activation_dropout=args.activation_dropout ,		activation_function="relu" ,		)

      lowercase_     =      state_dict["decoder.embed_tokens.weight"]
      lowercase_     =      MaMaaaForConditionalGeneration(__lowerCamelCase				)
      model.model.load_state_dict(__lowerCamelCase ,		strict=__lowerCamelCase				)
      lowercase_     =      make_linear_from_emb(model.model.shared				)

      return model


if __name__ == "__main__":
       lowerCAmelCase_ : Union[str, Any]						  =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
       parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       lowerCAmelCase_ : str						  =      parser.parse_args()
       lowerCAmelCase_ : Optional[int]						  =      convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
       model.save_pretrained(args.pytorch_dump_folder_path)







'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase_ : str						  =      {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      [
           "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Wav2Vec2ForAudioFrameClassification",
           "Wav2Vec2ForCTC",
           "Wav2Vec2ForMaskedLM",
           "Wav2Vec2ForPreTraining",
           "Wav2Vec2ForSequenceClassification",
           "Wav2Vec2ForXVector",
           "Wav2Vec2Model",
           "Wav2Vec2PreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : int						  =      [
           "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFWav2Vec2ForCTC",
           "TFWav2Vec2Model",
           "TFWav2Vec2PreTrainedModel",
           "TFWav2Vec2ForSequenceClassification",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Dict						  =      [
           "FlaxWav2Vec2ForCTC",
           "FlaxWav2Vec2ForPreTraining",
           "FlaxWav2Vec2Model",
           "FlaxWav2Vec2PreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
       from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
       from .processing_wavaveca import WavaVecaProcessor
       from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavaveca import (
                  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavaVecaForAudioFrameClassification,
                  WavaVecaForCTC,
                  WavaVecaForMaskedLM,
                  WavaVecaForPreTraining,
                  WavaVecaForSequenceClassification,
                  WavaVecaForXVector,
                  WavaVecaModel,
                  WavaVecaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWavaVecaForCTC,
                  TFWavaVecaForSequenceClassification,
                  TFWavaVecaModel,
                  TFWavaVecaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  FlaxWavaVecaForCTC,
                  FlaxWavaVecaForPreTraining,
                  FlaxWavaVecaModel,
                  FlaxWavaVecaPreTrainedModel,
              )


else:
       import sys

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






