

"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

_lowerCAmelCase						=		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
_lowerCAmelCase						=		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    {}
    with open(_lowerCamelCase ,   'r'   ) as file:
        for line_number, line in enumerate(_lowerCamelCase   ):
            _lowerCAmelCase       :   Optional[int]  =    line.strip()
            if line:
                _lowerCAmelCase       :   Dict  =    line.split()
                _lowerCAmelCase       :   Union[str, Any]  =    line_number
                _lowerCAmelCase       :   Dict  =    words[0]
                _lowerCAmelCase       :   Any  =    value
    return result

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    for attribute in key.split('.'   ):
        _lowerCAmelCase       :   Any  =    getattr(_lowerCamelCase ,   _lowerCamelCase   )

    _lowerCAmelCase       :   List[str]  =    None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(_lowerCamelCase   ):
            _lowerCAmelCase       :   Optional[Any]  =    PARAM_MAPPING[full_name.split('.'   )[-1]]
            _lowerCAmelCase       :   Dict  =    'param'

    if weight_type is not None and weight_type != "param":
        _lowerCAmelCase       :   Union[str, Any]  =    getattr(_lowerCamelCase ,   _lowerCamelCase   ).shape
    elif weight_type is not None and weight_type == "param":
        _lowerCAmelCase       :   Any  =    hf_pointer
        for attribute in hf_param_name.split('.'   ):
            _lowerCAmelCase       :   Dict  =    getattr(_lowerCamelCase ,   _lowerCamelCase   )
        _lowerCAmelCase       :   Optional[int]  =    shape_pointer.shape

        # let's reduce dimension
        _lowerCAmelCase       :   List[str]  =    value[0]
    else:
        _lowerCAmelCase       :   List[Any]  =    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":
        _lowerCAmelCase       :   Any  =    value
    elif weight_type == "weight_g":
        _lowerCAmelCase       :   List[Any]  =    value
    elif weight_type == "weight_v":
        _lowerCAmelCase       :   Any  =    value
    elif weight_type == "bias":
        _lowerCAmelCase       :   str  =    value
    elif weight_type == "param":
        for attribute in hf_param_name.split('.'   ):
            _lowerCAmelCase       :   Union[str, Any]  =    getattr(_lowerCamelCase ,   _lowerCamelCase   )
        _lowerCAmelCase       :   Any  =    value
    else:
        _lowerCAmelCase       :   Optional[Any]  =    value

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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(_lowerCamelCase   ):
            _lowerCAmelCase       :   List[Any]  =    PARAM_MAPPING[full_name.split('.'   )[-1]]
            _lowerCAmelCase       :   List[Any]  =    'param'

    if weight_type is not None and weight_type != "param":
        _lowerCAmelCase       :   List[str]  =    '.'.join([key, weight_type]   )
    elif weight_type is not None and weight_type == "param":
        _lowerCAmelCase       :   List[str]  =    '.'.join([key, hf_param_name]   )
    else:
        _lowerCAmelCase       :   str  =    key

    _lowerCAmelCase       :   List[Any]  =    value if 'lm_head' in full_key else value[0]


_lowerCAmelCase						=		{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=None ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    False
    for key, mapped_key in MAPPING.items():
        _lowerCAmelCase       :   str  =    'wav2vec2.' + 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]:
            _lowerCAmelCase       :   Optional[Any]  =    True
            if "*" in mapped_key:
                _lowerCAmelCase       :   Tuple  =    name.split(_lowerCamelCase   )[0].split('.'   )[-2]
                _lowerCAmelCase       :   List[Any]  =    mapped_key.replace('*' ,   _lowerCamelCase   )
            if "weight_g" in name:
                _lowerCAmelCase       :   List[str]  =    'weight_g'
            elif "weight_v" in name:
                _lowerCAmelCase       :   str  =    'weight_v'
            elif "bias" in name:
                _lowerCAmelCase       :   Union[str, Any]  =    'bias'
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                _lowerCAmelCase       :   List[Any]  =    'weight'
            else:
                _lowerCAmelCase       :   Tuple  =    None
            if hf_dict is not None:
                rename_dict(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
            else:
                set_recursively(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
            return is_used
    return is_used

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []
    _lowerCAmelCase       :   Any  =    fairseq_model.state_dict()

    _lowerCAmelCase       :   int  =    hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        _lowerCAmelCase       :   Optional[Any]  =    False
        if "conv_layers" in name:
            load_conv_layer(
                _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   hf_model.config.feat_extract_norm == 'group' ,   )
            _lowerCAmelCase       :   List[Any]  =    True
        else:
            _lowerCAmelCase       :   Union[str, Any]  =    load_wavaveca_layer(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
        if not is_used:
            unused_weights.append(_lowerCamelCase   )

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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    full_name.split('conv_layers.'   )[-1]
    _lowerCAmelCase       :   Optional[Any]  =    name.split('.'   )
    _lowerCAmelCase       :   Union[str, Any]  =    int(items[0]   )
    _lowerCAmelCase       :   str  =    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."""   )
            _lowerCAmelCase       :   List[str]  =    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."""   )
            _lowerCAmelCase       :   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."""   )
            _lowerCAmelCase       :   Union[str, Any]  =    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."""   )
            _lowerCAmelCase       :   Dict  =    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__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=None ,   _lowerCamelCase=None ,   _lowerCamelCase=True ,   _lowerCamelCase=False   ):

    '''simple docstring'''



    if config_path is not None:
        _lowerCAmelCase       :   Union[str, Any]  =    WavaVecaConfig.from_pretrained(_lowerCamelCase   )
    else:
        _lowerCAmelCase       :   int  =    WavaVecaConfig()

    if is_seq_class:
        _lowerCAmelCase       :   List[Any]  =    read_txt_into_dict(_lowerCamelCase   )
        _lowerCAmelCase       :   List[str]  =    idalabel
        _lowerCAmelCase       :   Optional[Any]  =    WavaVecaForSequenceClassification(_lowerCamelCase   )
        _lowerCAmelCase       :   Dict  =    WavaVecaFeatureExtractor(
            feature_size=1 ,   sampling_rate=16000 ,   padding_value=0 ,   do_normalize=_lowerCamelCase ,   return_attention_mask=_lowerCamelCase ,   )
        feature_extractor.save_pretrained(_lowerCamelCase   )

    elif is_finetuned:
        if dict_path:
            _lowerCAmelCase       :   Union[str, Any]  =    Dictionary.load(_lowerCamelCase   )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            _lowerCAmelCase       :   int  =    target_dict.pad_index
            _lowerCAmelCase       :   Union[str, Any]  =    target_dict.bos_index
            _lowerCAmelCase       :   Union[str, Any]  =    target_dict.eos_index
            _lowerCAmelCase       :   Any  =    len(target_dict.symbols   )
            _lowerCAmelCase       :   Union[str, Any]  =    os.path.join(_lowerCamelCase ,   'vocab.json'   )
            if not os.path.isdir(_lowerCamelCase   ):
                logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_lowerCamelCase   )   )
                return
            os.makedirs(_lowerCamelCase ,   exist_ok=_lowerCamelCase   )
            _lowerCAmelCase       :   Dict  =    target_dict.indices

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

        _lowerCAmelCase       :   Any  =    WavaVecaForCTC(_lowerCamelCase   )
    else:
        _lowerCAmelCase       :   Optional[Any]  =    WavaVecaForPreTraining(_lowerCamelCase   )

    if is_finetuned or is_seq_class:
        _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   int  =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path] ,   arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
    else:
        _lowerCAmelCase       :   Tuple  =    argparse.Namespace(task='audio_pretraining'   )
        _lowerCAmelCase       :   str  =    fairseq.tasks.setup_task(_lowerCamelCase   )

        _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,   task=_lowerCamelCase   )

    _lowerCAmelCase       :   List[str]  =    model[0].eval()

    recursively_load_weights(_lowerCamelCase ,   _lowerCamelCase ,   not is_finetuned   )

    hf_wavavec.save_pretrained(_lowerCamelCase   )


if __name__ == "__main__":
 _lowerCAmelCase						=		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"""
 )
 parser.add_argument(
     """--is_seq_class""",
     action="""store_true""",
     help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
 )
 _lowerCAmelCase						=		parser.parse_args()

 _lowerCAmelCase						=		not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )








"""simple docstring"""






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    inspect.getfile(accelerate.test_utils      )
      _lowerCAmelCase       :   Union[str, Any]  =    os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_script.py']      )
      _lowerCAmelCase       :   int  =    os.path.sep.join(
          mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_distributed_data_loop.py']      )
      _lowerCAmelCase       :   Optional[Any]  =    os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_ops.py']      )

  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      print(F"""Found {torch.cuda.device_count()} devices."""      )
      _lowerCAmelCase       :   int  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )

  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      print(F"""Found {torch.cuda.device_count()} devices."""      )
      _lowerCAmelCase       :   str  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(F"""Command: {cmd}"""      )
      with patch_environment(omp_num_threads=1      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )

  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
      with patch_environment(omp_num_threads=1      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )







  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""      )
      _lowerCAmelCase       :   Tuple  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1    ,cuda_visible_devices='0,1'      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )


if __name__ == "__main__":
 _lowerCAmelCase						=		Accelerator()
 _lowerCAmelCase						=		(accelerator.state.process_index + 2, 1_0)
 _lowerCAmelCase						=		torch.randint(0, 1_0, shape).to(accelerator.device)

 _lowerCAmelCase						=		""""""

 _lowerCAmelCase						=		accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 _lowerCAmelCase						=		accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 _lowerCAmelCase						=		accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)





