





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       for attribute in key.split("."	):
              UpperCamelCase_ 							=	getattr(_A       ,		_A	)

       if weight_type is not None:
              UpperCamelCase_ 							=	getattr(_A       ,		_A	).shape
       else:
              UpperCamelCase_ 							=	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":
              UpperCamelCase_ 							=	value
       elif weight_type == "weight_g":
              UpperCamelCase_ 							=	value
       elif weight_type == "weight_v":
              UpperCamelCase_ 							=	value
       elif weight_type == "bias":
              UpperCamelCase_ 							=	value
       else:
              UpperCamelCase_ 							=	value

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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       UpperCamelCase_ 							=	[]
       UpperCamelCase_ 							=	fairseq_model.state_dict()

       UpperCamelCase_ 							=	hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              UpperCamelCase_ 							=	False
              if "conv_layers" in name:
                     load_conv_layer(
                         _A       ,		_A       ,		_A       ,		_A       ,		hf_model.config.feat_extract_norm == "group"       ,		)
                     UpperCamelCase_ 							=	True
              else:
                     for key, mapped_key in MAPPING.items():
                            UpperCamelCase_ 							=	"""sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

                            if key in name or key.split("w2v_model."	)[-1] == name.split("."	)[0]:
                                   UpperCamelCase_ 							=	True
                                   if "*" in mapped_key:
                                          UpperCamelCase_ 							=	name.split(_A	)[0].split("."	)[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          UpperCamelCase_ 							=	mapped_key.replace("*"       ,		_A	)
                                   if "weight_g" in name:
                                          UpperCamelCase_ 							=	"""weight_g"""
                                   elif "weight_v" in name:
                                          UpperCamelCase_ 							=	"""weight_v"""
                                   elif "weight" in name:
                                          UpperCamelCase_ 							=	"""weight"""
                                   elif "bias" in name:
                                          UpperCamelCase_ 							=	"""bias"""
                                   else:
                                          UpperCamelCase_ 							=	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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       UpperCamelCase_ 							=	full_name.split("conv_layers."	)[-1]
       UpperCamelCase_ 							=	name.split("."	)
       UpperCamelCase_ 							=	int(items[0]	)
       UpperCamelCase_ 							=	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.'''
                     )
                     UpperCamelCase_ 							=	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.'''
                     )
                     UpperCamelCase_ 							=	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."
                     )
                     UpperCamelCase_ 							=	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.'''
                     )
                     UpperCamelCase_ 							=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
       else:
              unused_weights.append(_A	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       UpperCamelCase_ 							=	SEWDConfig()
       if is_finetuned:
              UpperCamelCase_ 							=	model.wav_encoder.wav_model.cfg
       else:
              UpperCamelCase_ 							=	model.cfg

       UpperCamelCase_ 							=	fs_config.conv_bias
       UpperCamelCase_ 							=	eval(fs_config.conv_feature_layers	)
       UpperCamelCase_ 							=	[x[0] for x in conv_layers]
       UpperCamelCase_ 							=	[x[1] for x in conv_layers]
       UpperCamelCase_ 							=	[x[2] for x in conv_layers]
       UpperCamelCase_ 							=	"""gelu"""
       UpperCamelCase_ 							=	"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
       UpperCamelCase_ 							=	0.0
       UpperCamelCase_ 							=	fs_config.activation_fn.name
       UpperCamelCase_ 							=	fs_config.encoder_embed_dim
       UpperCamelCase_ 							=	0.02
       UpperCamelCase_ 							=	fs_config.encoder_ffn_embed_dim
       UpperCamelCase_ 							=	1e-5
       UpperCamelCase_ 							=	fs_config.encoder_layerdrop
       UpperCamelCase_ 							=	fs_config.encoder_attention_heads
       UpperCamelCase_ 							=	fs_config.conv_pos_groups
       UpperCamelCase_ 							=	fs_config.conv_pos
       UpperCamelCase_ 							=	len(_A	)
       UpperCamelCase_ 							=	fs_config.encoder_layers
       UpperCamelCase_ 							=	fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       UpperCamelCase_ 							=	fs_config.max_position_embeddings
       UpperCamelCase_ 							=	fs_config.position_buckets
       UpperCamelCase_ 							=	fs_config.share_att_key
       UpperCamelCase_ 							=	fs_config.relative_attention
       UpperCamelCase_ 							=	fs_config.position_biased_input
       UpperCamelCase_ 							=	tuple(fs_config.pos_att_type.split("|"	)	)
       UpperCamelCase_ 							=	fs_config.norm_rel_ebd

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              UpperCamelCase_ 							=	model.cfg
              UpperCamelCase_ 							=	fs_config.final_dropout
              UpperCamelCase_ 							=	fs_config.layerdrop
       UpperCamelCase_ 							=	fs_config.activation_dropout
       UpperCamelCase_ 							=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       UpperCamelCase_ 							=	fs_config.attention_dropout
       UpperCamelCase_ 							=	fs_config.dropout_input
       UpperCamelCase_ 							=	fs_config.dropout
       UpperCamelCase_ 							=	fs_config.mask_channel_length
       UpperCamelCase_ 							=	fs_config.mask_channel_prob
       UpperCamelCase_ 							=	fs_config.mask_length
       UpperCamelCase_ 							=	fs_config.mask_prob

       UpperCamelCase_ 							=	"""Wav2Vec2FeatureExtractor"""
       UpperCamelCase_ 							=	"""Wav2Vec2CTCTokenizer"""

       return config




@torch.no_grad()
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=None       ,		UpperCamelCase_=None       ,		UpperCamelCase_=True	)  ->		Dict:

       if is_finetuned:
              UpperCamelCase_ 							=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]       ,		arg_overrides={"data": "/".join(dict_path.split("/"	)[:-1]	)}	)
       else:
              UpperCamelCase_ 							=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

       if config_path is not None:
              UpperCamelCase_ 							=	SEWDConfig.from_pretrained(_A	)
       else:
              UpperCamelCase_ 							=	convert_config(model[0]       ,		_A	)
       UpperCamelCase_ 							=	model[0].eval()

       UpperCamelCase_ 							=	True if config.feat_extract_norm == """layer""" else False
       UpperCamelCase_ 							=	WavaVecaFeatureExtractor(
           feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=_A       ,		return_attention_mask=_A       ,		)

       if is_finetuned:
              if dict_path:
                     UpperCamelCase_ 							=	Dictionary.load(_A	)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     UpperCamelCase_ 							=	target_dict.pad_index
                     UpperCamelCase_ 							=	target_dict.bos_index
                     UpperCamelCase_ 							=	target_dict.pad_index
                     UpperCamelCase_ 							=	target_dict.bos_index
                     UpperCamelCase_ 							=	target_dict.eos_index
                     UpperCamelCase_ 							=	len(target_dict.symbols	)
                     UpperCamelCase_ 							=	os.path.join(_A       ,		"vocab.json"	)
                     if not os.path.isdir(_A	):
                            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_A	)	)
                            return
                     os.makedirs(_A       ,		exist_ok=_A	)
                     with open(_A       ,		"w"       ,		encoding="utf-8"	) as vocab_handle:
                            json.dump(target_dict.indices       ,		_A	)
                     UpperCamelCase_ 							=	WavaVecaCTCTokenizer(
                         _A       ,		unk_token=target_dict.unk_word       ,		pad_token=target_dict.pad_word       ,		bos_token=target_dict.bos_word       ,		eos_token=target_dict.eos_word       ,		word_delimiter_token="|"       ,		do_lower_case=_A       ,		)
                     UpperCamelCase_ 							=	WavaVecaProcessor(feature_extractor=_A       ,		tokenizer=_A	)
                     processor.save_pretrained(_A	)

              UpperCamelCase_ 							=	SEWDForCTC(_A	)
       else:
              UpperCamelCase_ 							=	SEWDModel(_A	)
              feature_extractor.save_pretrained(_A	)

       recursively_load_weights(_A       ,		_A       ,		_A	)

       hf_model.save_pretrained(_A	)


if __name__ == "__main__":
     _UpperCAmelCase										=					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_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
     )
     _UpperCAmelCase										=					parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__			:  Optional[Any]					     =     {
    """configuration_musicgen""": [
        """MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MusicgenConfig""",
        """MusicgenDecoderConfig""",
    ],
    """processing_musicgen""": ["""MusicgenProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Any					     =     [
      """MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MusicgenForConditionalGeneration""",
      """MusicgenForCausalLM""",
      """MusicgenModel""",
      """MusicgenPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_musicgen import (
      MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MusicgenConfig,
      MusicgenDecoderConfig,
  )
  from .processing_musicgen import MusicgenProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_musicgen import (
        MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
        MusicgenForCausalLM,
        MusicgenForConditionalGeneration,
        MusicgenModel,
        MusicgenPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__			:  Dict					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




