"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_:					Any     =					logging.get_logger(__name__)

lowerCAmelCase_:					Optional[int]     =					{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "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",
}
lowerCAmelCase_:					List[str]     =					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     for attribute in key.split("."      ):
          lowercase__				 =							getattr(A    ,				A      )

     if weight_type is not None:
          lowercase__				 =							getattr(A    ,				A      ).shape
     else:
          lowercase__				 =							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":
          lowercase__				 =							value
     elif weight_type == "weight_g":
          lowercase__				 =							value
     elif weight_type == "weight_v":
          lowercase__				 =							value
     elif weight_type == "bias":
          lowercase__				 =							value
     elif weight_type == "running_mean":
          lowercase__				 =							value
     elif weight_type == "running_var":
          lowercase__				 =							value
     elif weight_type == "num_batches_tracked":
          lowercase__				 =							value
     elif weight_type == "inv_freq":
          lowercase__				 =							value
     else:
          lowercase__				 =							value

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







def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							[]
     lowercase__				 =							fairseq_model.state_dict()

     lowercase__				 =							hf_model.wavaveca_conformer.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase__				 =							False
          if "conv_layers" in name:
               load_conv_layer(
                   A    ,				A    ,				A    ,				A    ,				hf_model.config.feat_extract_norm == "group"    ,				)
               lowercase__				 =							True
          else:
               for key, mapped_key in MAPPING.items():
                    lowercase__				 =							"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                         lowercase__				 =							True
                         if "*" in mapped_key:
                              lowercase__				 =							name.split(A      )[0].split("."      )[-2]
                              lowercase__				 =							mapped_key.replace("*"    ,				A      )
                         if "pos_bias_u" in name:
                              lowercase__				 =							None
                         elif "pos_bias_v" in name:
                              lowercase__				 =							None
                         elif "weight_g" in name:
                              lowercase__				 =							"weight_g"
                         elif "weight_v" in name:
                              lowercase__				 =							"weight_v"
                         elif "bias" in name:
                              lowercase__				 =							"bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowercase__				 =							"weight"
                         elif "running_mean" in name:
                              lowercase__				 =							"running_mean"
                         elif "inv_freq" in name:
                              lowercase__				 =							"inv_freq"
                         elif "running_var" in name:
                              lowercase__				 =							"running_var"
                         elif "num_batches_tracked" in name:
                              lowercase__				 =							"num_batches_tracked"
                         else:
                              lowercase__				 =							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 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							full_name.split("conv_layers."      )[-1]
     lowercase__				 =							name.split("."      )
     lowercase__				 =							int(items[0]      )
     lowercase__				 =							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.'''      )
               lowercase__				 =							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.'''      )
               lowercase__				 =							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.'''      )
               lowercase__				 =							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.'''      )
               lowercase__				 =							value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
     else:
          unused_weights.append(A      )







@torch.no_grad()
def 					__a   (		A    ,				A    ,				A=None    ,				A=None    ,				A=True      ):






     '''simple docstring'''



     if config_path is not None:
          lowercase__				 =							WavaVecaConformerConfig.from_pretrained(A    ,				hidden_act="swish"      )
     else:
          lowercase__				 =							WavaVecaConformerConfig()

     if "rope" in checkpoint_path:
          lowercase__				 =							"rotary"

     if is_finetuned:
          if dict_path:
               lowercase__				 =							Dictionary.load(A      )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               lowercase__				 =							target_dict.pad_index
               lowercase__				 =							target_dict.bos_index
               lowercase__				 =							target_dict.eos_index
               lowercase__				 =							len(target_dict.symbols      )
               lowercase__				 =							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      )
               lowercase__				 =							target_dict.indices

               # fairseq has the <pad> and <s> switched
               lowercase__				 =							0
               lowercase__				 =							1
               with open(A    ,				"w"    ,				encoding="utf-8"      ) as vocab_handle:
                    json.dump(A    ,				A      )
               lowercase__				 =							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    ,				)
               lowercase__				 =							True if config.feat_extract_norm == "layer" else False
               lowercase__				 =							WavaVecaFeatureExtractor(
                   feature_size=1    ,				sampling_rate=1_60_00    ,				padding_value=0    ,				do_normalize=A    ,				return_attention_mask=A    ,				)
               lowercase__				 =							WavaVecaProcessor(feature_extractor=A    ,				tokenizer=A      )
               processor.save_pretrained(A      )

          lowercase__				 =							WavaVecaConformerForCTC(A      )
     else:
          lowercase__				 =							WavaVecaConformerForPreTraining(A      )

     if is_finetuned:
          lowercase__ ,						lowercase__ ,						lowercase__				 =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]    ,				arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     else:
          lowercase__				 =							argparse.Namespace(task="audio_pretraining"      )
          lowercase__				 =							fairseq.tasks.setup_task(A      )

          lowercase__ ,						lowercase__ ,						lowercase__				 =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,				task=A      )

     lowercase__				 =							model[0].eval()

     recursively_load_weights(A    ,				A    ,				not is_finetuned      )

     hf_wavavec.save_pretrained(A      )


if __name__ == "__main__":
 lowerCAmelCase_:					int     =					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(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 lowerCAmelCase_:					str     =					parser.parse_args()
 convert_wavaveca_conformer_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )





"""simple docstring"""


from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .notes_encoder import SpectrogramNotesEncoder
 from .continous_encoder import SpectrogramContEncoder
 from .pipeline_spectrogram_diffusion import (
     SpectrogramContEncoder,
     SpectrogramDiffusionPipeline,
     TaFilmDecoder,
 )

try:
 if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
 from .midi_utils import MidiProcessor




