
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
A__							: Optional[Any]     =       logging.get_logger(__name__)

A__							: Optional[int]     =       {
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
A__							: Union[str, Any]     =       [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]



def   _a  ( __UpperCamelCase							:	Dict						,__UpperCamelCase							:	Tuple						,__UpperCamelCase							:	Tuple						,__UpperCamelCase							:	Any						,__UpperCamelCase							:	Optional[Any]     ):

			for attribute in key.split('''.'''     ):
						lowerCAmelCase__			:     Any					   =      getattr(__a						,__a     )

			if weight_type is not None:
						lowerCAmelCase__			:     Union[str, Any]					   =      getattr(__a						,__a     ).shape
			else:
						lowerCAmelCase__			:     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__			:     Tuple					   =      value
			elif weight_type == "weight_g":
						lowerCAmelCase__			:     str					   =      value
			elif weight_type == "weight_v":
						lowerCAmelCase__			:     int					   =      value
			elif weight_type == "bias":
						lowerCAmelCase__			:     Union[str, Any]					   =      value
			else:
						lowerCAmelCase__			:     Tuple					   =      value

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



def   _a  ( __UpperCamelCase							:	Tuple						,__UpperCamelCase							:	Tuple     ):
			lowerCAmelCase__			:     List[Any]					   =      []
			lowerCAmelCase__			:     int					   =      fairseq_model.state_dict()

			lowerCAmelCase__			:     Any					   =      hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCAmelCase__			:     Union[str, Any]					   =      False
						if "conv_layers" in name:
									load_conv_layer(
									    __a						,__a						,__a						,__a						,hf_model.config.feat_extract_norm == '''group'''						,)
									lowerCAmelCase__			:     Any					   =      True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCAmelCase__			:     Optional[Any]					   =      '''unispeech_sat.''' + 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]:
															if "layer_norm_for_extract" in name and (".".join(name.split('''.'''     )[:-1]     ) != key):
																		# special case since naming is very similar
																		continue
															lowerCAmelCase__			:     List[Any]					   =      True
															if "*" in mapped_key:
																		lowerCAmelCase__			:     int					   =      name.split(__a     )[0].split('''.'''     )[-2]
																		lowerCAmelCase__			:     Any					   =      mapped_key.replace('''*'''						,__a     )
															if "weight_g" in name:
																		lowerCAmelCase__			:     Optional[int]					   =      '''weight_g'''
															elif "weight_v" in name:
																		lowerCAmelCase__			:     List[str]					   =      '''weight_v'''
															elif "bias" in name:
																		lowerCAmelCase__			:     List[Any]					   =      '''bias'''
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		lowerCAmelCase__			:     Dict					   =      '''weight'''
															else:
																		lowerCAmelCase__			:     Optional[Any]					   =      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  ( __UpperCamelCase							:	Optional[Any]						,__UpperCamelCase							:	Optional[int]						,__UpperCamelCase							:	Optional[Any]						,__UpperCamelCase							:	Tuple						,__UpperCamelCase							:	Union[str, Any]     ):
			lowerCAmelCase__			:     Dict					   =      full_name.split('''conv_layers.'''     )[-1]
			lowerCAmelCase__			:     Optional[int]					   =      name.split('''.'''     )
			lowerCAmelCase__			:     Any					   =      int(items[0]     )
			lowerCAmelCase__			:     List[Any]					   =      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__			:     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__			:     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[layer_id].layer_norm.bias.data.shape} was found.'''     )
									lowerCAmelCase__			:     List[str]					   =      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[layer_id].layer_norm.weight.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}.'''     )
			else:
						unused_weights.append(__a     )



@torch.no_grad()
def   _a  ( __UpperCamelCase							:	int						,__UpperCamelCase							:	Union[str, Any]						,__UpperCamelCase							:	Optional[int]=None						,__UpperCamelCase							:	Optional[Any]=None						,__UpperCamelCase							:	Tuple=True     ):

			if config_path is not None:
						lowerCAmelCase__			:     Optional[Any]					   =      UniSpeechSatConfig.from_pretrained(__a     )
			else:
						lowerCAmelCase__			:     Any					   =      UniSpeechSatConfig()

			lowerCAmelCase__			:     List[Any]					   =      ''''''

			if is_finetuned:
						lowerCAmelCase__			:     Tuple					   =      UniSpeechSatForCTC(__a     )
			else:
						lowerCAmelCase__			:     List[str]					   =      UniSpeechSatForPreTraining(__a     )

			lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__			:     Optional[Any]					   =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]						,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
			lowerCAmelCase__			:     Tuple					   =      model[0].eval()

			recursively_load_weights(__a						,__a     )

			hf_wavavec.save_pretrained(__a     )


if __name__ == "__main__":
							A__							: List[str]     =       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__							: Union[str, Any]     =       parser.parse_args()
							convert_unispeech_sat_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)










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



