



'''simple docstring'''






import functools
from typing import Any


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						list[str]  )      ->   bool:

							# Validation
							if not isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  ) or len(_lowerCAmelCase  ) == 0:
														raise ValueError('''the string should be not empty string'''  )

							if not isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  ) or not all(
							    isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  ) and len(_lowerCAmelCase  ) > 0 for item in words  ):
														raise ValueError('''the words should be a list of non-empty strings'''  )

							# Build trie
							UpperCAmelCase :       dict[str, Any] 							=		{}
							UpperCAmelCase :       str 							=		'''WORD_KEEPER'''

							for word in words:
														UpperCAmelCase :       int 							=		trie
														for c in word:
																					if c not in trie_node:
																												UpperCAmelCase :       str 							=		{}

																					UpperCAmelCase :       Dict 							=		trie_node[c]

														UpperCAmelCase :       str 							=		True

							UpperCAmelCase :       Dict 							=		len(_lowerCAmelCase  )

							# Dynamic programming method
							@functools.cache
							def is_breakable(_lowerCAmelCase  :						int  ) -> bool:
														if index == len_string:
																					return True

														UpperCAmelCase :       int 							=		trie
														for i in range(_lowerCAmelCase   ,      _lowerCAmelCase  ):
																					UpperCAmelCase :       Tuple 							=		trie_node.get(string[i]   ,      _lowerCAmelCase  )

																					if trie_node is None:
																												return False

																					if trie_node.get(_lowerCAmelCase   ,      _lowerCAmelCase  ) and is_breakable(i + 1  ):
																												return True

														return False

							return is_breakable(0  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()










'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

UpperCamelCase__:   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_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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
UpperCamelCase__:   Optional[Any]								=			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   Union[str, Any]:
							UpperCAmelCase :       Optional[int] 							=		{}
							with open(_lowerCAmelCase   ,      '''r'''  ) as file:
														for line_number, line in enumerate(_lowerCAmelCase  ):
																					UpperCAmelCase :       List[str] 							=		line.strip()
																					if line:
																												UpperCAmelCase :       str 							=		line.split()
																												UpperCAmelCase :       Union[str, Any] 							=		line_number
																												UpperCAmelCase :       List[Any] 							=		words[0]
																												UpperCAmelCase :       Union[str, Any] 							=		value
							return result


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						str  )      ->   int:
							for attribute in key.split('''.'''  ):
														UpperCAmelCase :       Dict 							=		getattr(_lowerCAmelCase   ,      _lowerCAmelCase  )

							UpperCAmelCase :       Dict 							=		None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_lowerCAmelCase  ):
																					UpperCAmelCase :       Any 							=		PARAM_MAPPING[full_name.split('''.'''  )[-1]]
																					UpperCAmelCase :       Dict 							=		'''param'''

							if weight_type is not None and weight_type != "param":
														UpperCAmelCase :       Optional[int] 							=		getattr(_lowerCAmelCase   ,      _lowerCAmelCase  ).shape
							elif weight_type is not None and weight_type == "param":
														UpperCAmelCase :       List[Any] 							=		hf_pointer
														for attribute in hf_param_name.split('''.'''  ):
																					UpperCAmelCase :       Optional[Any] 							=		getattr(_lowerCAmelCase   ,      _lowerCAmelCase  )
														UpperCAmelCase :       int 							=		shape_pointer.shape

														# let's reduce dimension
														UpperCAmelCase :       Union[str, Any] 							=		value[0]
							else:
														UpperCAmelCase :       List[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":
														UpperCAmelCase :       int 							=		value
							elif weight_type == "weight_g":
														UpperCAmelCase :       str 							=		value
							elif weight_type == "weight_v":
														UpperCAmelCase :       Dict 							=		value
							elif weight_type == "bias":
														UpperCAmelCase :       str 							=		value
							elif weight_type == "param":
														for attribute in hf_param_name.split('''.'''  ):
																					UpperCAmelCase :       int 							=		getattr(_lowerCAmelCase   ,      _lowerCAmelCase  )
														UpperCAmelCase :       Optional[int] 							=		value
							else:
														UpperCAmelCase :       Tuple 							=		value

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


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						List[Any]  )      ->   List[Any]:
							UpperCAmelCase :       List[str] 							=		None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_lowerCAmelCase  ):
																					UpperCAmelCase :       List[str] 							=		PARAM_MAPPING[full_name.split('''.'''  )[-1]]
																					UpperCAmelCase :       Any 							=		'''param'''

							if weight_type is not None and weight_type != "param":
														UpperCAmelCase :       Optional[int] 							=		'''.'''.join([key, weight_type]  )
							elif weight_type is not None and weight_type == "param":
														UpperCAmelCase :       Optional[int] 							=		'''.'''.join([key, hf_param_name]  )
							else:
														UpperCAmelCase :       List[Any] 							=		key

							UpperCAmelCase :       Tuple 							=		value if '''lm_head''' in full_key else value[0]


UpperCamelCase__:   Tuple								=			{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Any=None   ,      _lowerCAmelCase  :						Optional[Any]=None  )      ->   int:
							UpperCAmelCase :       List[Any] 							=		False
							for key, mapped_key in MAPPING.items():
														UpperCAmelCase :       int 							=		'''wav2vec2.''' + 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]:
																					UpperCAmelCase :       Optional[Any] 							=		True
																					if "*" in mapped_key:
																												UpperCAmelCase :       Tuple 							=		name.split(_lowerCAmelCase  )[0].split('''.'''  )[-2]
																												UpperCAmelCase :       List[Any] 							=		mapped_key.replace('''*'''   ,      _lowerCAmelCase  )
																					if "weight_g" in name:
																												UpperCAmelCase :       str 							=		'''weight_g'''
																					elif "weight_v" in name:
																												UpperCAmelCase :       int 							=		'''weight_v'''
																					elif "bias" in name:
																												UpperCAmelCase :       int 							=		'''bias'''
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												UpperCAmelCase :       List[str] 							=		'''weight'''
																					else:
																												UpperCAmelCase :       Dict 							=		None
																					if hf_dict is not None:
																												rename_dict(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					else:
																												set_recursively(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					return is_used
							return is_used


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Any  )      ->   Any:
							UpperCAmelCase :       Dict 							=		[]
							UpperCAmelCase :       Dict 							=		fairseq_model.state_dict()

							UpperCAmelCase :       Union[str, Any] 							=		hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														UpperCAmelCase :       Dict 							=		False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      hf_model.config.feat_extract_norm == '''group'''   ,      )
																					UpperCAmelCase :       Any 							=		True
														else:
																					UpperCAmelCase :       Optional[Any] 							=		load_wavaveca_layer(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														if not is_used:
																					unused_weights.append(_lowerCAmelCase  )

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


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Optional[int]  )      ->   Union[str, Any]:
							UpperCAmelCase :       Any 							=		full_name.split('''conv_layers.'''  )[-1]
							UpperCAmelCase :       Optional[int] 							=		name.split('''.'''  )
							UpperCAmelCase :       Tuple 							=		int(items[0]  )
							UpperCAmelCase :       Tuple 							=		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."""  )
																					UpperCAmelCase :       Tuple 							=		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."""  )
																					UpperCAmelCase :       Union[str, 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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""  )
																					UpperCAmelCase :       Union[str, Any] 							=		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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""  )
																					UpperCAmelCase :       List[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  :						Union[str, Any]   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Dict=None   ,      _lowerCAmelCase  :						List[Any]=None   ,      _lowerCAmelCase  :						int=True   ,      _lowerCAmelCase  :						Optional[int]=False  )      ->   Dict:
							if config_path is not None:
														UpperCAmelCase :       List[str] 							=		WavaVecaConfig.from_pretrained(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       List[Any] 							=		WavaVecaConfig()

							if is_seq_class:
														UpperCAmelCase :       Optional[Any] 							=		read_txt_into_dict(_lowerCAmelCase  )
														UpperCAmelCase :       Optional[int] 							=		idalabel
														UpperCAmelCase :       Optional[Any] 							=		WavaVecaForSequenceClassification(_lowerCAmelCase  )
														UpperCAmelCase :       Dict 							=		WavaVecaFeatureExtractor(
														    feature_size=1   ,      sampling_rate=16000   ,      padding_value=0   ,      do_normalize=_lowerCAmelCase   ,      return_attention_mask=_lowerCAmelCase   ,      )
														feature_extractor.save_pretrained(_lowerCAmelCase  )

							elif is_finetuned:
														if dict_path:
																					UpperCAmelCase :       Dict 							=		Dictionary.load(_lowerCAmelCase  )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					UpperCAmelCase :       Any 							=		target_dict.pad_index
																					UpperCAmelCase :       Tuple 							=		target_dict.bos_index
																					UpperCAmelCase :       Optional[int] 							=		target_dict.eos_index
																					UpperCAmelCase :       Union[str, Any] 							=		len(target_dict.symbols  )
																					UpperCAmelCase :       Dict 							=		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  )
																					UpperCAmelCase :       List[Any] 							=		target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					UpperCAmelCase :       List[str] 							=		0
																					UpperCAmelCase :       List[str] 							=		1
																					with open(_lowerCAmelCase   ,      '''w'''   ,      encoding='''utf-8'''  ) as vocab_handle:
																												json.dump(_lowerCAmelCase   ,      _lowerCAmelCase  )
																					UpperCAmelCase :       Optional[int] 							=		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   ,      )
																					UpperCAmelCase :       int 							=		True if config.feat_extract_norm == '''layer''' else False
																					UpperCAmelCase :       int 							=		WavaVecaFeatureExtractor(
																					    feature_size=1   ,      sampling_rate=16000   ,      padding_value=0   ,      do_normalize=_lowerCAmelCase   ,      return_attention_mask=_lowerCAmelCase   ,      )
																					UpperCAmelCase :       str 							=		WavaVecaProcessor(feature_extractor=_lowerCAmelCase   ,      tokenizer=_lowerCAmelCase  )
																					processor.save_pretrained(_lowerCAmelCase  )

														UpperCAmelCase :       Union[str, Any] 							=		WavaVecaForCTC(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Dict 							=		WavaVecaForPreTraining(_lowerCAmelCase  )

							if is_finetuned or is_seq_class:
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]   ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''  )[:-1]  )}  )
							else:
														UpperCAmelCase :       Optional[Any] 							=		argparse.Namespace(task='''audio_pretraining'''  )
														UpperCAmelCase :       List[Any] 							=		fairseq.tasks.setup_task(_lowerCAmelCase  )

														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       int 							=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,      task=_lowerCAmelCase  )

							UpperCAmelCase :       Optional[int] 							=		model[0].eval()

							recursively_load_weights(_lowerCAmelCase   ,      _lowerCAmelCase   ,      not is_finetuned  )

							hf_wavavec.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
						UpperCamelCase__:   Dict								=			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"
						)
						parser.add_argument(
						    "--is_seq_class",
						    action="store_true",
						    help="Whether the model to convert is a fine-tuned sequence classification model or not",
						)
						UpperCamelCase__:   Any								=			parser.parse_args()

						UpperCamelCase__:   int								=			not args.not_finetuned and not args.is_seq_class
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.dict_path,
						    is_finetuned,
						    args.is_seq_class,
						)





