

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase							:						int     			=		logging.get_logger(__name__)

lowerCAmelCase							:						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""",
    """mask_emb""": """masked_spec_embed""",
}







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						for attribute in key.split("."					):
												SCREAMING_SNAKE_CASE_:				Any							= getattr(_UpperCAmelCase       ,			_UpperCAmelCase					)

						if weight_type is not None:
												SCREAMING_SNAKE_CASE_:				Tuple							= getattr(_UpperCAmelCase       ,			_UpperCAmelCase					).shape
						else:
												SCREAMING_SNAKE_CASE_:				Dict							= 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_:				Dict							= value
						elif weight_type == "weight_g":
												SCREAMING_SNAKE_CASE_:				Any							= value
						elif weight_type == "weight_v":
												SCREAMING_SNAKE_CASE_:				List[str]							= value
						elif weight_type == "bias":
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= value
						else:
												SCREAMING_SNAKE_CASE_:				Optional[int]							= value

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







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Any							= []
						SCREAMING_SNAKE_CASE_:				str							= fairseq_model.state_dict()

						SCREAMING_SNAKE_CASE_:				List[str]							= hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												SCREAMING_SNAKE_CASE_:				str							= False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			hf_model.config.feat_extract_norm == "group"       ,			)
																		SCREAMING_SNAKE_CASE_:				str							= True
												else:
																		for key, mapped_key in MAPPING.items():
																								SCREAMING_SNAKE_CASE_:				Any							= mapped_key

																								if key in name:
																														SCREAMING_SNAKE_CASE_:				Optional[int]							= True
																														if "*" in mapped_key:
																																				SCREAMING_SNAKE_CASE_:				str							= name.split(_UpperCAmelCase					)[0].split("."					)[-2]
																																				SCREAMING_SNAKE_CASE_:				Any							= mapped_key.replace("*"       ,			_UpperCAmelCase					)
																														if "weight_g" in name:
																																				SCREAMING_SNAKE_CASE_:				List[str]							= "weight_g"
																														elif "weight_v" in name:
																																				SCREAMING_SNAKE_CASE_:				Any							= "weight_v"
																														elif "weight" in name:
																																				SCREAMING_SNAKE_CASE_:				List[str]							= "weight"
																														elif "bias" in name:
																																				SCREAMING_SNAKE_CASE_:				Dict							= "bias"
																														else:
																																				SCREAMING_SNAKE_CASE_:				List[str]							= None
																														set_recursively(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					)
																								continue
												if not is_used:
																		unused_weights.append(_UpperCAmelCase					)

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







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= full_name.split("conv_layers."					)[-1]
						SCREAMING_SNAKE_CASE_:				Optional[int]							= name.split("."					)
						SCREAMING_SNAKE_CASE_:				Dict							= int(items[0]					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 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_:				List[str]							= 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_:				Dict							= 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_:				Any							= 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_:				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(_UpperCAmelCase					)







def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Any							= HubertConfig()
						SCREAMING_SNAKE_CASE_:				List[Any]							= model.config

						SCREAMING_SNAKE_CASE_:				List[Any]							= fs_config.activation_dropout
						SCREAMING_SNAKE_CASE_:				Dict							= False
						SCREAMING_SNAKE_CASE_:				Any							= fs_config.attention_dropout
						SCREAMING_SNAKE_CASE_:				str							= False
						SCREAMING_SNAKE_CASE_:				List[str]							= eval(fs_config.extractor_conv_feature_layers					)
						SCREAMING_SNAKE_CASE_:				str							= [x[0] for x in conv_layers]
						SCREAMING_SNAKE_CASE_:				Dict							= [x[1] for x in conv_layers]
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= [x[2] for x in conv_layers]
						SCREAMING_SNAKE_CASE_:				Dict							= "gelu"
						SCREAMING_SNAKE_CASE_:				str							= "layer" if fs_config.extractor_mode == "layer_norm" else "group"
						SCREAMING_SNAKE_CASE_:				int							= False
						SCREAMING_SNAKE_CASE_:				Any							= 0.0
						SCREAMING_SNAKE_CASE_:				str							= 0.0
						SCREAMING_SNAKE_CASE_:				List[Any]							= fs_config.activation_fn
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= fs_config.dropout
						SCREAMING_SNAKE_CASE_:				Any							= fs_config.encoder_embed_dim
						SCREAMING_SNAKE_CASE_:				List[Any]							= 0.0_2
						SCREAMING_SNAKE_CASE_:				List[str]							= fs_config.encoder_ffn_embed_dim
						SCREAMING_SNAKE_CASE_:				Tuple							= 1e-5
						SCREAMING_SNAKE_CASE_:				Dict							= 0.0
						SCREAMING_SNAKE_CASE_:				Dict							= fs_config.encoder_attention_heads
						SCREAMING_SNAKE_CASE_:				Optional[int]							= fs_config.conv_pos_groups
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= fs_config.conv_pos
						SCREAMING_SNAKE_CASE_:				Tuple							= len(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Optional[int]							= fs_config.encoder_layers

						return config







@torch.no_grad()
def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=None					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= distilhubert().model.model

						if config_path is not None:
												SCREAMING_SNAKE_CASE_:				Tuple							= HubertConfig.from_pretrained(_UpperCAmelCase					)
						else:
												SCREAMING_SNAKE_CASE_:				Optional[int]							= convert_config(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				List[str]							= model.eval()

						SCREAMING_SNAKE_CASE_:				int							= WavaVecaFeatureExtractor(
						    feature_size=1       ,			sampling_rate=1_60_00       ,			padding_value=0       ,			do_normalize=_UpperCAmelCase       ,			return_attention_mask=_UpperCAmelCase       ,			)
						SCREAMING_SNAKE_CASE_:				str							= HubertModel(_UpperCAmelCase					)

						recursively_load_weights(_UpperCAmelCase       ,			_UpperCAmelCase					)

						feature_extractor.save_pretrained(_UpperCAmelCase					)
						hf_model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
						lowerCAmelCase							:						Optional[int]     			=		argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						lowerCAmelCase							:						List[str]     			=		parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)









import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


lowerCAmelCase							:						Tuple     			=		datasets.utils.logging.get_logger(__name__)



@dataclass
class 						__lowercase       (				datasets.BuilderConfig			):




			"""simple docstring"""







			_UpperCAmelCase     :							Optional[datasets.Features]    							=  None
			_UpperCAmelCase     :							str    							=  "utf-8"
			_UpperCAmelCase     :							Optional[str]    							=  None
			_UpperCAmelCase     :							int    							=  10 << 20  # 10MB
			_UpperCAmelCase     :							bool    							=  False
			_UpperCAmelCase     :							str    							=  "line"







class 						__lowercase       (				datasets.ArrowBasedBuilder			):




			"""simple docstring"""







			_UpperCAmelCase     :							List[Any]    							=  TextConfig



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									return datasets.DatasetInfo(features=self.config.features)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  Tuple):
									if not self.config.data_files:
															raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}")
									SCREAMING_SNAKE_CASE_:				Dict							= dl_manager.download_and_extract(self.config.data_files)
									if isinstance(lowerCAmelCase__     ,		(str, list, tuple)):
															SCREAMING_SNAKE_CASE_:				Dict							= data_files
															if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_:				Dict							= [files]
															SCREAMING_SNAKE_CASE_:				List[str]							= [dl_manager.iter_files(lowerCAmelCase__) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,		gen_kwargs={"files": files})]
									SCREAMING_SNAKE_CASE_:				Any							= []
									for split_name, files in data_files.items():
															if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_:				List[Any]							= [files]
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= [dl_manager.iter_files(lowerCAmelCase__) for file in files]
															splits.append(datasets.SplitGenerator(name=lowerCAmelCase__     ,		gen_kwargs={"files": files}))
									return splits



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]     ,		lowerCAmelCase__						:  pa.Table):
									if self.config.features is not None:
															SCREAMING_SNAKE_CASE_:				Optional[int]							= self.config.features.arrow_schema
															if all(not require_storage_cast(lowerCAmelCase__) for feature in self.config.features.values()):
																					# cheaper cast
																					SCREAMING_SNAKE_CASE_:				List[str]							= pa_table.cast(lowerCAmelCase__)
															else:
																					# more expensive cast; allows str <-> int/float or str to Audio for example
																					SCREAMING_SNAKE_CASE_:				List[Any]							= table_cast(lowerCAmelCase__     ,		lowerCAmelCase__)
															return pa_table
									else:
															return pa_table.cast(pa.schema({"text": pa.string()}))




			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Dict):
									SCREAMING_SNAKE_CASE_:				str							= list(self.config.features) if self.config.features is not None else ["text"]
									for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__)):
															# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
															with open(lowerCAmelCase__     ,		encoding=self.config.encoding     ,		errors=self.config.errors) as f:
																					if self.config.sample_by == "line":
																											SCREAMING_SNAKE_CASE_:				Any							= 0
																											while True:
																																	SCREAMING_SNAKE_CASE_:				str							= f.read(self.config.chunksize)
																																	if not batch:
																																							break
																																	batch += f.readline()  # finish current line
																																	# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																																	SCREAMING_SNAKE_CASE_:				Optional[Any]							= StringIO(lowerCAmelCase__).readlines()
																																	if not self.config.keep_linebreaks:
																																							SCREAMING_SNAKE_CASE_:				Optional[Any]							= [line.rstrip("\n") for line in batch]
																																	SCREAMING_SNAKE_CASE_:				Any							= pa.Table.from_arrays([pa.array(lowerCAmelCase__)]     ,		names=lowerCAmelCase__)
																																	# Uncomment for debugging (will print the Arrow table size and elements)
																																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																	yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__)
																																	batch_idx += 1
																					elif self.config.sample_by == "paragraph":
																											SCREAMING_SNAKE_CASE_:				List[str]							= 0
																											SCREAMING_SNAKE_CASE_:				Tuple							= ""
																											while True:
																																	SCREAMING_SNAKE_CASE_:				Dict							= f.read(self.config.chunksize)
																																	if not new_batch:
																																							break
																																	batch += new_batch
																																	batch += f.readline()  # finish current line
																																	SCREAMING_SNAKE_CASE_:				List[str]							= batch.split("\n\n")
																																	SCREAMING_SNAKE_CASE_:				Optional[int]							= pa.Table.from_arrays(
																																	    [pa.array([example for example in batch[:-1] if example])]     ,		names=lowerCAmelCase__)
																																	# Uncomment for debugging (will print the Arrow table size and elements)
																																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																	yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__)
																																	batch_idx += 1
																																	SCREAMING_SNAKE_CASE_:				Optional[Any]							= batch[-1]
																											if batch:
																																	SCREAMING_SNAKE_CASE_:				int							= pa.Table.from_arrays([pa.array([batch])]     ,		names=lowerCAmelCase__)
																																	yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__)
																					elif self.config.sample_by == "document":
																											SCREAMING_SNAKE_CASE_:				List[Any]							= f.read()
																											SCREAMING_SNAKE_CASE_:				Optional[int]							= pa.Table.from_arrays([pa.array([text])]     ,		names=lowerCAmelCase__)
																											yield file_idx, self._cast_table(lowerCAmelCase__)






