



"""simple docstring"""




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()
lowerCAmelCase__		:					Any							=logging.get_logger(__name__)

lowerCAmelCase__		:					Dict							={
    '''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''',
}
lowerCAmelCase__		:					List[str]							=[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       a__       )    ->			List[Any]:
							for attribute in key.split('.'       ):
														__SCREAMING_SNAKE_CASE									= getattr(a__							,       a__       )

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

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






def      __lowercase       (			a__							,       a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= []
							__SCREAMING_SNAKE_CASE									= fairseq_model.state_dict()

							__SCREAMING_SNAKE_CASE									= hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														__SCREAMING_SNAKE_CASE									= False
														if "conv_layers" in name:
																					load_conv_layer(
																					    a__							,       a__							,       a__							,       a__							,       hf_model.config.feat_extract_norm == 'group'							,       )
																					__SCREAMING_SNAKE_CASE									= True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
																																			__SCREAMING_SNAKE_CASE									= True
																																			if "*" in mapped_key:
																																										__SCREAMING_SNAKE_CASE									= name.split(a__       )[0].split('.'       )[-2]
																																										__SCREAMING_SNAKE_CASE									= mapped_key.replace('*'							,       a__       )
																																			if "weight_g" in name:
																																										__SCREAMING_SNAKE_CASE									= 'weight_g'
																																			elif "weight_v" in name:
																																										__SCREAMING_SNAKE_CASE									= 'weight_v'
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										__SCREAMING_SNAKE_CASE									= 'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										__SCREAMING_SNAKE_CASE									= 'weight'
																																			else:
																																										__SCREAMING_SNAKE_CASE									= 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      __lowercase       (			a__							,       a__							,       a__							,       a__							,       a__       )    ->			Dict:
							__SCREAMING_SNAKE_CASE									= full_name.split('conv_layers.'       )[-1]
							__SCREAMING_SNAKE_CASE									= name.split('.'       )
							__SCREAMING_SNAKE_CASE									= int(items[0]       )
							__SCREAMING_SNAKE_CASE									= 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."""
																					)
																					__SCREAMING_SNAKE_CASE									= 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."""
																					)
																					__SCREAMING_SNAKE_CASE									= 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."
																					)
																					__SCREAMING_SNAKE_CASE									= 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."""
																					)
																					__SCREAMING_SNAKE_CASE									= 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      __lowercase       (			a__							,       a__							,       a__=None       )    ->			Union[str, Any]:
							# load the pre-trained checkpoints
							__SCREAMING_SNAKE_CASE									= torch.load(a__       )
							__SCREAMING_SNAKE_CASE									= WavLMConfigOrig(checkpoint['cfg']       )
							__SCREAMING_SNAKE_CASE									= WavLMOrig(a__       )
							model.load_state_dict(checkpoint['model']       )
							model.eval()

							if config_path is not None:
														__SCREAMING_SNAKE_CASE									= WavLMConfig.from_pretrained(a__       )
							else:
														__SCREAMING_SNAKE_CASE									= WavLMConfig()

							__SCREAMING_SNAKE_CASE									= WavLMModel(a__       )

							recursively_load_weights(a__							,       a__       )

							hf_wavlm.save_pretrained(a__       )


if __name__ == "__main__":
			lowerCAmelCase__		:					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''')
			lowerCAmelCase__		:					Optional[int]							=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        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A = None				,							_A = None				,							_A = False				,							**_A				,							):







													'''simple docstring'''

													super().__init__(features=_A				,							cache_dir=_A				,							keep_in_memory=_A				,							**_A		)
													__SCREAMING_SNAKE_CASE									= Sql(
													    cache_dir=_A				,							features=_A				,							sql=_A				,							con=_A				,							**_A				,							)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None

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

													# Build dataset for splits
													__SCREAMING_SNAKE_CASE									= self.builder.as_dataset(
													    split='train'				,							verification_mode=_A				,							in_memory=self.keep_in_memory		)
													return dataset







class        UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A				,							_A = None				,							_A = None				,							**_A				,							):







													'''simple docstring'''

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

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






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('sql'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('con'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('index'				,							_A		)

													__SCREAMING_SNAKE_CASE									= self._write(index=_A				,							**self.to_sql_kwargs		)
													return written






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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:
																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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				,							_A				,							_A		)]				,							)				,							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






