



"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowercase			:			Dict										=						logging.get_logger(__name__)

_lowercase			:			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_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",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			for attribute in key.split('''.'''     ):
						lowerCamelCase__		:		List[Any]		     =getattr(__lowerCamelCase							,		__lowerCamelCase     )

			if weight_type is not None:
						lowerCamelCase__		:		Tuple		     =getattr(__lowerCamelCase							,		__lowerCamelCase     ).shape
			else:
						lowerCamelCase__		:		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":
						lowerCamelCase__		:		Dict		     =value
			elif weight_type == "weight_g":
						lowerCamelCase__		:		Tuple		     =value
			elif weight_type == "weight_v":
						lowerCamelCase__		:		Union[str, Any]		     =value
			elif weight_type == "bias":
						lowerCamelCase__		:		Union[str, Any]		     =value
			else:
						lowerCamelCase__		:		List[Any]		     =value

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






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple		     =[]
			lowerCamelCase__		:		Optional[Any]		     =fairseq_model.state_dict()

			lowerCamelCase__		:		int		     =hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCamelCase__		:		Optional[int]		     =False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		hf_model.config.feat_extract_norm == '''group'''							,		)
									lowerCamelCase__		:		int		     =True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCamelCase__		:		Optional[int]		     ='''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

												if key in name or (key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0] and not is_finetuned):
															lowerCamelCase__		:		int		     =True
															if "*" in mapped_key:
																		lowerCamelCase__		:		List[str]		     =name.split(__lowerCamelCase     )[0].split('''.'''     )[-2]
																		lowerCamelCase__		:		List[Any]		     =mapped_key.replace('''*'''							,		__lowerCamelCase     )
															if "weight_g" in name:
																		lowerCamelCase__		:		Optional[Any]		     ='''weight_g'''
															elif "weight_v" in name:
																		lowerCamelCase__		:		str		     ='''weight_v'''
															elif "weight" in name:
																		lowerCamelCase__		:		str		     ='''weight'''
															elif "bias" in name:
																		lowerCamelCase__		:		List[str]		     ='''bias'''
															else:
																		lowerCamelCase__		:		str		     =None
															set_recursively(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase     )
												continue
						if not is_used:
									unused_weights.append(__lowerCamelCase     )

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






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   List[Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =full_name.split('''conv_layers.'''     )[-1]
			lowerCamelCase__		:		Dict		     =name.split('''.'''     )
			lowerCamelCase__		:		List[Any]		     =int(items[0]     )
			lowerCamelCase__		:		Tuple		     =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.'''
									)
									lowerCamelCase__		:		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.'''
									)
									lowerCamelCase__		:		Optional[int]		     =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."
									)
									lowerCamelCase__		:		str		     =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.'''
									)
									lowerCamelCase__		:		str		     =value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
			else:
						unused_weights.append(__lowerCamelCase     )






@torch.no_grad()
def 					snake_case__       (		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   List[Any]=None							,		__lowerCamelCase    :   Optional[Any]=None							,		__lowerCamelCase    :   int=True     ):
			"""simple docstring"""
			if config_path is not None:
						lowerCamelCase__		:		Optional[int]		     =HubertConfig.from_pretrained(__lowerCamelCase     )
			else:
						lowerCamelCase__		:		Tuple		     =HubertConfig()

			if is_finetuned:
						if dict_path:
									lowerCamelCase__		:		str		     =Dictionary.load(__lowerCamelCase     )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									lowerCamelCase__		:		List[str]		     =target_dict.pad_index
									lowerCamelCase__		:		Optional[int]		     =target_dict.bos_index
									lowerCamelCase__		:		Optional[Any]		     =target_dict.eos_index
									lowerCamelCase__		:		Optional[Any]		     =len(target_dict.symbols     )
									lowerCamelCase__		:		List[str]		     =os.path.join(__lowerCamelCase							,		'''vocab.json'''     )
									if not os.path.isdir(__lowerCamelCase     ):
												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__lowerCamelCase     )     )
												return
									os.makedirs(__lowerCamelCase							,		exist_ok=__lowerCamelCase     )
									with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''     ) as vocab_handle:
												json.dump(target_dict.indices							,		__lowerCamelCase     )
									lowerCamelCase__		:		Tuple		     =WavaVecaCTCTokenizer(
									    __lowerCamelCase							,		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=__lowerCamelCase							,		)
									lowerCamelCase__		:		str		     =True if config.feat_extract_norm == '''layer''' else False
									lowerCamelCase__		:		Optional[Any]		     =WavaVecaFeatureExtractor(
									    feature_size=1							,		sampling_rate=16000							,		padding_value=0							,		do_normalize=__lowerCamelCase							,		return_attention_mask=__lowerCamelCase							,		)
									lowerCamelCase__		:		Optional[int]		     =WavaVecaProcessor(feature_extractor=__lowerCamelCase							,		tokenizer=__lowerCamelCase     )
									processor.save_pretrained(__lowerCamelCase     )

						lowerCamelCase__		:		List[Any]		     =HubertForCTC(__lowerCamelCase     )
			else:
						lowerCamelCase__		:		Any		     =HubertModel(__lowerCamelCase     )

			if is_finetuned:
						lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Union[str, Any]		     =fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]							,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
			else:
						lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Tuple		     =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

			lowerCamelCase__		:		Any		     =model[0].eval()

			recursively_load_weights(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase     )

			hf_wavavec.save_pretrained(__lowerCamelCase     )


if __name__ == "__main__":
		_lowercase			:			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("--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"
		)
		_lowercase			:			Any										=						parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)









"""simple docstring"""






from __future__ import annotations






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   list[str] | None = None     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =word_bank or []
			# create a table
			lowerCamelCase__		:		int		     =len(__lowerCamelCase     ) + 1

			lowerCamelCase__		:		list[list[list[str]]]		     =[]
			for _ in range(__lowerCamelCase     ):
						table.append([]     )
			# seed value
			lowerCamelCase__		:		Tuple		     =[[]]  # because empty string has empty combination

			# iterate through the indices
			for i in range(__lowerCamelCase     ):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(__lowerCamelCase     )] == word:
															lowerCamelCase__		:		list[list[str]]		     =[
															    [word, *way] for way in table[i]
															]
															# adds the word to every combination the current position holds
															# now,push that combination to the table[i+len(word)]
															table[i + len(__lowerCamelCase     )] += new_combinations

    # combinations are in reverse order so reverse for better output
			for combination in table[len(__lowerCamelCase     )]:
						combination.reverse()

			return table[len(__lowerCamelCase     )]


if __name__ == "__main__":
		print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
		print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
		print(
		    all_construct(
		        "hexagonosaurus",
		        ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
		    )
		)




