
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							=			logging.get_logger(__name__)

__UpperCAmelCase							=			{
    "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							=			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]


def       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	{}
   with open(snake_case__,   '''r'''		) as file:
      for line_number, line in enumerate(snake_case__		):
         SCREAMING_SNAKE_CASE_     =	line.strip()
         if line:
            SCREAMING_SNAKE_CASE_     =	line.split()
            SCREAMING_SNAKE_CASE_     =	line_number
            SCREAMING_SNAKE_CASE_     =	words[0]
            SCREAMING_SNAKE_CASE_     =	value
   return result


def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase		):
   for attribute in key.split('''.'''		):
      SCREAMING_SNAKE_CASE_     =	getattr(snake_case__,   snake_case__		)

   SCREAMING_SNAKE_CASE_     =	None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(snake_case__		):
         SCREAMING_SNAKE_CASE_     =	PARAM_MAPPING[full_name.split('''.'''		)[-1]]
         SCREAMING_SNAKE_CASE_     =	'param'

   if weight_type is not None and weight_type != "param":
      SCREAMING_SNAKE_CASE_     =	getattr(snake_case__,   snake_case__		).shape
   elif weight_type is not None and weight_type == "param":
      SCREAMING_SNAKE_CASE_     =	hf_pointer
      for attribute in hf_param_name.split('''.'''		):
         SCREAMING_SNAKE_CASE_     =	getattr(snake_case__,   snake_case__		)
      SCREAMING_SNAKE_CASE_     =	shape_pointer.shape

      # let's reduce dimension
      SCREAMING_SNAKE_CASE_     =	value[0]
   else:
      SCREAMING_SNAKE_CASE_     =	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":
      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
   elif weight_type == "param":
      for attribute in hf_param_name.split('''.'''		):
         SCREAMING_SNAKE_CASE_     =	getattr(snake_case__,   snake_case__		)
      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       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(snake_case__		):
         SCREAMING_SNAKE_CASE_     =	PARAM_MAPPING[full_name.split('''.'''		)[-1]]
         SCREAMING_SNAKE_CASE_     =	'param'

   if weight_type is not None and weight_type != "param":
      SCREAMING_SNAKE_CASE_     =	'.'.join([key, weight_type]		)
   elif weight_type is not None and weight_type == "param":
      SCREAMING_SNAKE_CASE_     =	'.'.join([key, hf_param_name]		)
   else:
      SCREAMING_SNAKE_CASE_     =	key

   SCREAMING_SNAKE_CASE_     =	value if 'lm_head' in full_key else value[0]


__UpperCAmelCase							=			{
    "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       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase=None,   __lowerCamelCase=None		):
   SCREAMING_SNAKE_CASE_     =	False
   for key, mapped_key in MAPPING.items():
      SCREAMING_SNAKE_CASE_     =	'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]:
         SCREAMING_SNAKE_CASE_     =	True
         if "*" in mapped_key:
            SCREAMING_SNAKE_CASE_     =	name.split(snake_case__		)[0].split('''.'''		)[-2]
            SCREAMING_SNAKE_CASE_     =	mapped_key.replace('''*''',   snake_case__		)
         if "weight_g" in name:
            SCREAMING_SNAKE_CASE_     =	'weight_g'
         elif "weight_v" in name:
            SCREAMING_SNAKE_CASE_     =	'weight_v'
         elif "bias" 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
         if hf_dict is not None:
            rename_dict(snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__		)
         else:
            set_recursively(snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__		)
         return is_used
   return is_used


def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	[]
   SCREAMING_SNAKE_CASE_     =	fairseq_model.state_dict()

   SCREAMING_SNAKE_CASE_     =	hf_model.wavaveca.feature_extractor

   for name, value in fairseq_dict.items():
      SCREAMING_SNAKE_CASE_     =	False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case__,   snake_case__,   snake_case__,   snake_case__,   hf_model.config.feat_extract_norm == '''group''',   )
         SCREAMING_SNAKE_CASE_     =	True
      else:
         SCREAMING_SNAKE_CASE_     =	load_wavaveca_layer(snake_case__,   snake_case__,   snake_case__		)
      if not is_used:
         unused_weights.append(snake_case__		)

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


def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase		):
   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:
         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.'''		)
         SCREAMING_SNAKE_CASE_     =	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.'''		)
         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:
         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.'''		)
         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:
         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.'''		)
         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(snake_case__		)


@torch.no_grad()
def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase=None,   __lowerCamelCase=None,   __lowerCamelCase=True,   __lowerCamelCase=False		):
   if config_path is not None:
      SCREAMING_SNAKE_CASE_     =	WavaVecaConfig.from_pretrained(snake_case__		)
   else:
      SCREAMING_SNAKE_CASE_     =	WavaVecaConfig()

   if is_seq_class:
      SCREAMING_SNAKE_CASE_     =	read_txt_into_dict(snake_case__		)
      SCREAMING_SNAKE_CASE_     =	idalabel
      SCREAMING_SNAKE_CASE_     =	WavaVecaForSequenceClassification(snake_case__		)
      SCREAMING_SNAKE_CASE_     =	WavaVecaFeatureExtractor(
          feature_size=1,   sampling_rate=1_60_00,   padding_value=0,   do_normalize=snake_case__,   return_attention_mask=snake_case__,   )
      feature_extractor.save_pretrained(snake_case__		)

   elif is_finetuned:
      if dict_path:
         SCREAMING_SNAKE_CASE_     =	Dictionary.load(snake_case__		)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         SCREAMING_SNAKE_CASE_     =	target_dict.pad_index
         SCREAMING_SNAKE_CASE_     =	target_dict.bos_index
         SCREAMING_SNAKE_CASE_     =	target_dict.eos_index
         SCREAMING_SNAKE_CASE_     =	len(target_dict.symbols		)
         SCREAMING_SNAKE_CASE_     =	os.path.join(snake_case__,   '''vocab.json'''		)
         if not os.path.isdir(snake_case__		):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__		)		)
            return
         os.makedirs(snake_case__,   exist_ok=snake_case__		)
         SCREAMING_SNAKE_CASE_     =	target_dict.indices

         # fairseq has the <pad> and <s> switched
         SCREAMING_SNAKE_CASE_     =	0
         SCREAMING_SNAKE_CASE_     =	1
         with open(snake_case__,   '''w''',   encoding='''utf-8'''		) as vocab_handle:
            json.dump(snake_case__,   snake_case__		)
         SCREAMING_SNAKE_CASE_     =	WavaVecaCTCTokenizer(
             snake_case__,   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=snake_case__,   )
         SCREAMING_SNAKE_CASE_     =	True if config.feat_extract_norm == 'layer' else False
         SCREAMING_SNAKE_CASE_     =	WavaVecaFeatureExtractor(
             feature_size=1,   sampling_rate=1_60_00,   padding_value=0,   do_normalize=snake_case__,   return_attention_mask=snake_case__,   )
         SCREAMING_SNAKE_CASE_     =	WavaVecaProcessor(feature_extractor=snake_case__,   tokenizer=snake_case__		)
         processor.save_pretrained(snake_case__		)

      SCREAMING_SNAKE_CASE_     =	WavaVecaForCTC(snake_case__		)
   else:
      SCREAMING_SNAKE_CASE_     =	WavaVecaForPreTraining(snake_case__		)

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

      SCREAMING_SNAKE_CASE_     =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],   task=snake_case__		)

   SCREAMING_SNAKE_CASE_     =	model[0].eval()

   recursively_load_weights(snake_case__,   snake_case__,   not is_finetuned		)

   hf_wavavec.save_pretrained(snake_case__		)


if __name__ == "__main__":
   __UpperCAmelCase							=			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							=			parser.parse_args()

   __UpperCAmelCase							=			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,
   )







import functools
from typing import Any
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    list[str]	)	->     bool:

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

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

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

   for word in words:
      UpperCamelCase    :  List[str]							=	trie
      for c in word:
         if c not in trie_node:
            UpperCamelCase    :  int							=	{}

         UpperCamelCase    :  str							=	trie_node[c]

      UpperCamelCase    :  Tuple							=	True

   UpperCamelCase    :  List[Any]							=	len(snake_case__	)

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

      UpperCamelCase    :  Dict							=	trie
      for i in range(snake_case__       ,     snake_case__	):
         UpperCamelCase    :  List[Any]							=	trie_node.get(string[i]       ,     snake_case__	)

         if trie_node is None:
            return False

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

      return False

   return is_breakable(0	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



