





import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
A       :   Union[str, Any]					= logging.get_logger(__name__)

A       :   Any					= {
    '''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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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''',
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
A       :   Optional[Any]					= [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def   __lowerCamelCase   (				__a  :int       ,	__a  :Optional[int]       ,	__a  :Union[str, Any]       ,	__a  :str       ,	__a  :Tuple       )						->							Optional[int]:






		"""simple docstring"""

		for attribute in key.split("""."""       ):
				A__     =  getattr(__a       ,	__a       )

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

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







def   __lowerCamelCase   (				__a  :Any       ,	__a  :Any       )						->							Dict:






		"""simple docstring"""

		A__     =  []
		A__     =  fairseq_model.state_dict()

		A__     =  hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				A__     =  False
				if "conv_layers" in name:
						load_conv_layer(
						    __a       ,	__a       ,	__a       ,	__a       ,	hf_model.config.feat_extract_norm == """group"""       ,	)
						A__     =  True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
										A__     =  True
										if "*" in mapped_key:
												A__     =  name.split(__a       )[0].split("""."""       )[-2]
												A__     =  mapped_key.replace("""*"""       ,	__a       )
										if "weight_g" in name:
												A__     =  """weight_g"""
										elif "weight_v" in name:
												A__     =  """weight_v"""
										elif "bias" in name and "relative_attention_bias" not in name:
												A__     =  """bias"""
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												A__     =  """weight"""
										else:
												A__     =  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   (				__a  :str       ,	__a  :Optional[int]       ,	__a  :int       ,	__a  :str       ,	__a  :int       )						->							Dict:






		"""simple docstring"""

		A__     =  full_name.split("""conv_layers."""       )[-1]
		A__     =  name.split("""."""       )
		A__     =  int(items[0]       )
		A__     =  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.'
						)
						A__     =  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.'
						)
						A__     =  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."
						)
						A__     =  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.'
						)
						A__     =  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   __lowerCamelCase   (				__a  :Dict       ,	__a  :Dict       ,	__a  :Optional[int]=None       )						->							Tuple:






		"""simple docstring"""

		A__     =  torch.load(__a       )
		A__     =  WavLMConfigOrig(checkpoint["""cfg"""]       )
		A__     =  WavLMOrig(__a       )
		model.load_state_dict(checkpoint["""model"""]       )
		model.eval()

		if config_path is not None:
				A__     =  WavLMConfig.from_pretrained(__a       )
		else:
				A__     =  WavLMConfig()

		A__     =  WavLMModel(__a       )

		recursively_load_weights(__a       ,	__a       )

		hf_wavlm.save_pretrained(__a       )


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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					A       :   List[str]					= parser.parse_args()
					convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
					import sqlitea

					import sqlalchemy


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			def __init__(     self  :       Any				,      __lowerCAmelCase  :       Union[str, "sqlalchemy.sql.Selectable"]				,      __lowerCAmelCase  :       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,      __lowerCAmelCase  :       Optional[Features] = None				,      __lowerCAmelCase  :       str = None				,      __lowerCAmelCase  :       bool = False				,      **__lowerCAmelCase  :       Dict				,      )      ->      Any:







					"""simple docstring"""






					super().__init__(features=__lowerCAmelCase				,      cache_dir=__lowerCAmelCase				,      keep_in_memory=__lowerCAmelCase				,      **__lowerCAmelCase  )
					A__     =  Sql(
					    cache_dir=__lowerCAmelCase				,      features=__lowerCAmelCase				,      sql=__lowerCAmelCase				,      con=__lowerCAmelCase				,      **__lowerCAmelCase				,      )







			def   a_			(     self  :       Optional[int]  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  None
					A__     =  None
					A__     =  None
					A__     =  None

					self.builder.download_and_prepare(
					    download_config=__lowerCAmelCase				,      download_mode=__lowerCAmelCase				,      verification_mode=__lowerCAmelCase				,      base_path=__lowerCAmelCase				,      )

					# Build dataset for splits
					A__     =  self.builder.as_dataset(
					    split="""train"""				,      verification_mode=__lowerCAmelCase				,      in_memory=self.keep_in_memory  )
					return dataset


class     A     :
			'''simple docstring'''




			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Dataset				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,      __lowerCAmelCase  :       Optional[int] = None				,      __lowerCAmelCase  :       Optional[int] = None				,      **__lowerCAmelCase  :       List[Any]				,      )      ->      Any:







					"""simple docstring"""






					if num_proc is not None and num_proc <= 0:
							raise ValueError(f'num_proc {num_proc} must be an integer > 0.'  )

					A__     =  dataset
					A__     =  name
					A__     =  con
					A__     =  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					A__     =  num_proc
					A__     =  to_sql_kwargs






			def   a_			(     self  :       Any  )      ->      int:







					"""simple docstring"""






					A__     =  self.to_sql_kwargs.pop("""sql"""				,      __lowerCAmelCase  )
					A__     =  self.to_sql_kwargs.pop("""con"""				,      __lowerCAmelCase  )
					A__     =  self.to_sql_kwargs.pop("""index"""				,      __lowerCAmelCase  )

					A__     =  self._write(index=__lowerCAmelCase				,      **self.to_sql_kwargs  )
					return written






			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					A__ ,     A__ ,     A__     =  args
					A__     =  {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
					A__     =  query_table(
					    table=self.dataset.data				,      key=slice(__lowerCAmelCase				,      offset + self.batch_size  )				,      indices=self.dataset._indices				,      )
					A__     =  batch.to_pandas()
					A__     =  df.to_sql(self.name				,      self.con				,      index=__lowerCAmelCase				,      **__lowerCAmelCase  )
					return num_rows or len(__lowerCAmelCase  )







			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       Tuple				,      **__lowerCAmelCase  :       Dict  )      ->      int:







					"""simple docstring"""






					A__     =  0

					if self.num_proc is None or self.num_proc == 1:
							for offset in logging.tqdm(
							    range(0				,      len(self.dataset  )				,      self.batch_size  )				,      unit="""ba"""				,      disable=not logging.is_progress_bar_enabled()				,      desc="""Creating SQL from Arrow format"""				,      ):
									written += self._batch_sql((offset, index, to_sql_kwargs)  )
					else:
							A__ ,     A__     =  len(self.dataset  ), self.batch_size
							with multiprocessing.Pool(self.num_proc  ) as pool:
									for num_rows in logging.tqdm(
									    pool.imap(
									        self._batch_sql				,      [(offset, index, to_sql_kwargs) for offset in range(0				,      __lowerCAmelCase				,      __lowerCAmelCase  )]				,      )				,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,      unit="""ba"""				,      disable=not logging.is_progress_bar_enabled()				,      desc="""Creating SQL from Arrow format"""				,      ):
											written += num_rows

					return written



