





'''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()
a__				:				Optional[Any]					=logging.get_logger(__name__)

a__				:				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',
}
a__				:				Dict					=[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 							lowercase__  (  __lowercase		:    Optional[Any]							,    __lowercase		:    List[str]							,    __lowercase		:    List[str]							,    __lowercase		:    List[str]							,    __lowercase		:    List[str]	)					-> str:



       """simple docstring"""






       for attribute in key.split('.'	):
              __UpperCamelCase			=    getattr(__lowercase							,    __lowercase	)

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

       logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''	)
def 							lowercase__  (  __lowercase		:    Any							,    __lowercase		:    List[str]							,    __lowercase		:    Union[str, Any]	)					-> List[str]:



       """simple docstring"""






       __UpperCamelCase			=    []
       __UpperCamelCase			=    fairseq_model.state_dict()

       __UpperCamelCase			=    hf_model.wavaveca_conformer.feature_extractor

       for name, value in fairseq_dict.items():
              __UpperCamelCase			=    False
              if "conv_layers" in name:
                     load_conv_layer(
                         __lowercase							,    __lowercase							,    __lowercase							,    __lowercase							,    hf_model.config.feat_extract_norm == 'group'							,    )
                     __UpperCamelCase			=    True
              else:
                     for key, mapped_key in MAPPING.items():
                            __UpperCamelCase			=    '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]:
                                   __UpperCamelCase			=    True
                                   if "*" in mapped_key:
                                          __UpperCamelCase			=    name.split(__lowercase	)[0].split('.'	)[-2]
                                          __UpperCamelCase			=    mapped_key.replace('*'							,    __lowercase	)
                                   if "pos_bias_u" in name:
                                          __UpperCamelCase			=    None
                                   elif "pos_bias_v" in name:
                                          __UpperCamelCase			=    None
                                   elif "weight_g" in name:
                                          __UpperCamelCase			=    'weight_g'
                                   elif "weight_v" in name:
                                          __UpperCamelCase			=    'weight_v'
                                   elif "bias" in name:
                                          __UpperCamelCase			=    'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          __UpperCamelCase			=    'weight'
                                   elif "running_mean" in name:
                                          __UpperCamelCase			=    'running_mean'
                                   elif "inv_freq" in name:
                                          __UpperCamelCase			=    'inv_freq'
                                   elif "running_var" in name:
                                          __UpperCamelCase			=    'running_var'
                                   elif "num_batches_tracked" in name:
                                          __UpperCamelCase			=    'num_batches_tracked'
                                   else:
                                          __UpperCamelCase			=    None
                                   set_recursively(__lowercase							,    __lowercase							,    __lowercase							,    __lowercase							,    __lowercase	)
                            continue
              if not is_used:
                     unused_weights.append(__lowercase	)

       logger.warning(F'''Unused weights: {unused_weights}'''	)
def 							lowercase__  (  __lowercase		:    int							,    __lowercase		:    List[str]							,    __lowercase		:    List[str]							,    __lowercase		:    Optional[int]							,    __lowercase		:    Union[str, Any]	)					-> str:



       """simple docstring"""






       __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:
                     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			=    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			=    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			=    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			=    value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
       else:
              unused_weights.append(__lowercase	)
@torch.no_grad()
def 							lowercase__  (  __lowercase		:    Tuple							,    __lowercase		:    Union[str, Any]							,    __lowercase		:    Union[str, Any]=None							,    __lowercase		:    Optional[int]=None							,    __lowercase		:    Optional[int]=True	)					-> List[str]:



       """simple docstring"""






       if config_path is not None:
              __UpperCamelCase			=    WavaVecaConformerConfig.from_pretrained(__lowercase							,    hidden_act='swish'	)
       else:
              __UpperCamelCase			=    WavaVecaConformerConfig()

       if "rope" in checkpoint_path:
              __UpperCamelCase			=    'rotary'

       if is_finetuned:
              if dict_path:
                     __UpperCamelCase			=    Dictionary.load(__lowercase	)

                     # 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.eos_index
                     __UpperCamelCase			=    len(target_dict.symbols	)
                     __UpperCamelCase			=    os.path.join(__lowercase							,    'vocab.json'	)
                     if not os.path.isdir(__lowercase	):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__lowercase	)	)
                            return
                     os.makedirs(__lowercase							,    exist_ok=__lowercase	)
                     __UpperCamelCase			=    target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     __UpperCamelCase			=    0
                     __UpperCamelCase			=    1
                     with open(__lowercase							,    'w'							,    encoding='utf-8'	) as vocab_handle:
                            json.dump(__lowercase							,    __lowercase	)
                     __UpperCamelCase			=    WavaVecaCTCTokenizer(
                         __lowercase							,    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=__lowercase							,    )
                     __UpperCamelCase			=    True if config.feat_extract_norm == 'layer' else False
                     __UpperCamelCase			=    WavaVecaFeatureExtractor(
                         feature_size=1							,    sampling_rate=16000							,    padding_value=0							,    do_normalize=__lowercase							,    return_attention_mask=__lowercase							,    )
                     __UpperCamelCase			=    WavaVecaProcessor(feature_extractor=__lowercase							,    tokenizer=__lowercase	)
                     processor.save_pretrained(__lowercase	)

              __UpperCamelCase			=    WavaVecaConformerForCTC(__lowercase	)
       else:
              __UpperCamelCase			=    WavaVecaConformerForPreTraining(__lowercase	)

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

              __UpperCamelCase			,		__UpperCamelCase			,		__UpperCamelCase			=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,    task=__lowercase	)

       __UpperCamelCase			=    model[0].eval()

       recursively_load_weights(__lowercase							,    __lowercase							,    not is_finetuned	)

       hf_wavavec.save_pretrained(__lowercase	)


if __name__ == "__main__":
    a__				:				List[Any]					=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'''
    )
    a__				:				Dict					=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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__		:     Tuple			=							{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:     int			=							['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   lowercase__		:     List[Any]			=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



