





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()
snake_case								=							logging.get_logger(__name__)

snake_case								=							{
    """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""",
}
snake_case								=							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any  )   ->    Tuple:
  _lowercase						    =			{}
  with open(snake_case__      ,				'r'  ) as file:
    for line_number, line in enumerate(snake_case__  ):
      _lowercase						    =			line.strip()
      if line:
        _lowercase						    =			line.split()
        _lowercase						    =			line_number
        _lowercase						    =			words[0]
        _lowercase						    =			value
  return result

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Optional[Any]      ,				snake_case__	:List[str]      ,				snake_case__	:List[Any]      ,				snake_case__	:Optional[int]  )   ->    List[str]:
  for attribute in key.split('.'  ):
    _lowercase						    =			getattr(snake_case__      ,				snake_case__  )

  _lowercase						    =			None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(snake_case__  ):
      _lowercase						    =			PARAM_MAPPING[full_name.split('.'  )[-1]]
      _lowercase						    =			'param'

  if weight_type is not None and weight_type != "param":
    _lowercase						    =			getattr(snake_case__      ,				snake_case__  ).shape
  elif weight_type is not None and weight_type == "param":
    _lowercase						    =			hf_pointer
    for attribute in hf_param_name.split('.'  ):
      _lowercase						    =			getattr(snake_case__      ,				snake_case__  )
    _lowercase						    =			shape_pointer.shape

    # let's reduce dimension
    _lowercase						    =			value[0]
  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 == "param":
    for attribute in hf_param_name.split('.'  ):
      _lowercase						    =			getattr(snake_case__      ,				snake_case__  )
    _lowercase						    =			value
  else:
    _lowercase						    =			value

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]      ,				snake_case__	:str      ,				snake_case__	:Dict      ,				snake_case__	:List[str]      ,				snake_case__	:Dict  )   ->    List[Any]:
  _lowercase						    =			None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(snake_case__  ):
      _lowercase						    =			PARAM_MAPPING[full_name.split('.'  )[-1]]
      _lowercase						    =			'param'

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

  _lowercase						    =			value if 'lm_head' in full_key else value[0]


snake_case								=							{
    """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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict      ,				snake_case__	:List[str]      ,				snake_case__	:Optional[int]=None      ,				snake_case__	:Tuple=None  )   ->    Any:
  _lowercase						    =			False
  for key, mapped_key in MAPPING.items():
    _lowercase						    =			'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]:
      _lowercase						    =			True
      if "*" in mapped_key:
        _lowercase						    =			name.split(snake_case__  )[0].split('.'  )[-2]
        _lowercase						    =			mapped_key.replace('*'      ,				snake_case__  )
      if "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'
      else:
        _lowercase						    =			None
      if hf_dict is not None:
        rename_dict(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__  )
      else:
        set_recursively(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__  )
      return is_used
  return is_used

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]      ,				snake_case__	:int      ,				snake_case__	:Dict  )   ->    Tuple:
  _lowercase						    =			[]
  _lowercase						    =			fairseq_model.state_dict()

  _lowercase						    =			hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _lowercase						    =			False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				hf_model.config.feat_extract_norm == 'group'      ,				)
      _lowercase						    =			True
    else:
      _lowercase						    =			load_wavaveca_layer(snake_case__      ,				snake_case__      ,				snake_case__  )
    if not is_used:
      unused_weights.append(snake_case__  )

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:List[str]      ,				snake_case__	:Optional[Any]      ,				snake_case__	:List[str]      ,				snake_case__	:List[Any]  )   ->    Union[str, Any]:
  _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(snake_case__  )

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Dict      ,				snake_case__	:Optional[Any]=None      ,				snake_case__	:int=None      ,				snake_case__	:Dict=True      ,				snake_case__	:str=False  )   ->    int:
  if config_path is not None:
    _lowercase						    =			WavaVecaConfig.from_pretrained(snake_case__  )
  else:
    _lowercase						    =			WavaVecaConfig()

  if is_seq_class:
    _lowercase						    =			read_txt_into_dict(snake_case__  )
    _lowercase						    =			idalabel
    _lowercase						    =			WavaVecaForSequenceClassification(snake_case__  )
    _lowercase						    =			WavaVecaFeatureExtractor(
        feature_size=1      ,				sampling_rate=1_6000      ,				padding_value=0      ,				do_normalize=snake_case__      ,				return_attention_mask=snake_case__      ,				)
    feature_extractor.save_pretrained(snake_case__  )

  elif is_finetuned:
    if dict_path:
      _lowercase						    =			Dictionary.load(snake_case__  )

      # 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(snake_case__      ,				'vocab.json'  )
      if not os.path.isdir(snake_case__  ):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(snake_case__  )  )
        return
      os.makedirs(snake_case__      ,				exist_ok=snake_case__  )
      _lowercase						    =			target_dict.indices

      # fairseq has the <pad> and <s> switched
      _lowercase						    =			0
      _lowercase						    =			1
      with open(snake_case__      ,				'w'      ,				encoding='utf-8'  ) as vocab_handle:
        json.dump(snake_case__      ,				snake_case__  )
      _lowercase						    =			WavaVecaCTCTokenizer(
          snake_case__      ,				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=snake_case__      ,				)
      _lowercase						    =			True if config.feat_extract_norm == 'layer' else False
      _lowercase						    =			WavaVecaFeatureExtractor(
          feature_size=1      ,				sampling_rate=1_6000      ,				padding_value=0      ,				do_normalize=snake_case__      ,				return_attention_mask=snake_case__      ,				)
      _lowercase						    =			WavaVecaProcessor(feature_extractor=snake_case__      ,				tokenizer=snake_case__  )
      processor.save_pretrained(snake_case__  )

    _lowercase						    =			WavaVecaForCTC(snake_case__  )
  else:
    _lowercase						    =			WavaVecaForPreTraining(snake_case__  )

  if is_finetuned or is_seq_class:
    _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(snake_case__  )

    _lowercase    ,		_lowercase    ,		_lowercase						    =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,				task=snake_case__  )

  _lowercase						    =			model[0].eval()

  recursively_load_weights(snake_case__      ,				snake_case__      ,				not is_finetuned  )

  hf_wavavec.save_pretrained(snake_case__  )


if __name__ == "__main__":
      snake_case								=							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""",
      )
      snake_case								=							parser.parse_args()

      snake_case								=							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 pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''					,		[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42					,		dataset_name='''my_dataset''' )} ),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42 )} ),
        SplitDict({'''train''': SplitInfo()} ),
    ]					,		)
def 						snake_case_       (UpperCamelCase			:    SplitDict ):





							'''simple docstring'''


							_a      	=							split_dict._to_yaml_list()
							assert len(UpperCamelCase ) == len(UpperCamelCase )
							_a      	=							SplitDict._from_yaml_list(UpperCamelCase )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														_a      	=							None
														# the split name of split_dict takes over the name of the split info object
														_a      	=							split_name
							assert split_dict == reloaded

@pytest.mark.parametrize(
    '''split_info'''					,		[SplitInfo(), SplitInfo(dataset_name=UpperCamelCase ), SplitInfo(dataset_name='''my_dataset''' )] )
def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							asdict(SplitDict({'''train''': split_info} ) )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

