





'''simple docstring'''






import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


A__				:   str      =re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
A__				:   Any      =10
A__				:   Optional[int]      =2_56
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      if len(lowerCAmelCase  ) < MIN_NUM_TOKENS:
            return None
      _lowerCAmelCase							     =							MinHash(num_perm=lowerCAmelCase  )
      for token in set(lowerCAmelCase  ):
            min_hash.update(token.encode()  )
      return min_hash
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      return {t for t in NON_ALPHA.split(lowerCAmelCase  ) if len(t.strip()  ) > 0}




class  UpperCAmelCase     :




      def __init__(			self			: Any			,			*,
          __snake_case			: float = 0.85			,			)				->      List[Any]:
            _lowerCAmelCase							     =							duplication_jaccard_threshold
            _lowerCAmelCase							     =							NUM_PERM
            _lowerCAmelCase							     =							MinHashLSH(threshold=self._duplication_jaccard_threshold			,			num_perm=self._num_perm  )

            _lowerCAmelCase							     =							defaultdict(__snake_case  )




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: Tuple			,			__snake_case			: MinHash  )				->      None:
            _lowerCAmelCase							     =							self._index.query(__snake_case  )
            if code_key in self._index.keys:
                  print(f"Duplicate key {code_key}"  )
                  return

            self._index.insert(__snake_case			,			__snake_case  )
            if len(__snake_case  ) > 0:
                  for base_duplicate in close_duplicates:
                        if base_duplicate in self._duplicate_clusters:
                              self._duplicate_clusters[base_duplicate].add(__snake_case  )
                              break
                  else:
                        self._duplicate_clusters[close_duplicates[0]].add(__snake_case  )




      def 				lowercase__    (			self			: Optional[Any]  )				->      List[List[Dict]]:
            _lowerCAmelCase							     =							[]
            for base, duplicates in self._duplicate_clusters.items():
                  _lowerCAmelCase							     =							[base] + list(__snake_case  )
                  # reformat the cluster to be a list of dict
                  _lowerCAmelCase							     =							[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
                  duplicate_clusters.append(__snake_case  )
            return duplicate_clusters




      def 				lowercase__    (			self			: str			,			__snake_case			: Any  )				->      None:
            _lowerCAmelCase							     =							self.get_duplicate_clusters()
            with open(__snake_case			,			"""w"""  ) as f:
                  json.dump(__snake_case			,			__snake_case  )




def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase     ,							_lowerCAmelCase							     =							element
      _lowerCAmelCase							     =							get_min_hash([t for t in NON_ALPHA.split(data["""content"""]  ) if len(t.strip()  ) > 0]  )
      if min_hash is not None:
            return (index, data["repo_name"], data["path"]), min_hash
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash							,	ThreadedIterator(lowerCAmelCase							,	max_queue_size=1_00_00  )							,	chunksize=1_00							,	):
                  if data is not None:
                        yield data
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase  )

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase  )  )							,	max_queue_size=1_00  )  ):
            di.add(lowerCAmelCase							,	lowerCAmelCase  )

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							get_tokens(lowerCAmelCase  )
      _lowerCAmelCase							     =							get_tokens(lowerCAmelCase  )
      return len(tokensa & tokensa  ) / len(tokensa | tokensa  )


A__				:   Tuple      =None
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							[]
      for elementa in cluster:
            _lowerCAmelCase							     =							_shared_dataset[elementa["""base_index"""]]["""content"""]
            for elementa in extremes:
                  _lowerCAmelCase							     =							_shared_dataset[elementa["""base_index"""]]["""content"""]
                  if jaccard_similarity(lowerCAmelCase							,	lowerCAmelCase  ) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  _lowerCAmelCase							     =							1
                  extremes.append(lowerCAmelCase  )
      return extremes
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      global _shared_dataset
      _lowerCAmelCase							     =							dataset
      _lowerCAmelCase							     =							[]
      _lowerCAmelCase							     =							partial(_find_cluster_extremes_shared							,	jaccard_threshold=lowerCAmelCase  )
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    lowerCAmelCase							,	lowerCAmelCase							,	)							,	total=len(lowerCAmelCase  )							,	):
                  extremes_list.append(lowerCAmelCase  )
      return extremes_list





def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase = 0.85  ):





      """simple docstring"""


      _lowerCAmelCase							     =							make_duplicate_clusters(lowerCAmelCase							,	lowerCAmelCase  )
      _lowerCAmelCase							     =							{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
      _lowerCAmelCase							     =							{}
      _lowerCAmelCase							     =							find_extremes(lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  )
      for extremes in extremes_clusters:
            for element in extremes:
                  _lowerCAmelCase							     =							element
      _lowerCAmelCase							     =							duplicate_indices - set(extreme_dict.keys()  )
      _lowerCAmelCase							     =							dataset.filter(lambda lowerCAmelCase							,	lowerCAmelCase  : idx not in remove_indices							,	with_indices=lowerCAmelCase  )

      # update duplicate_clusters
      for cluster in duplicate_clusters:
            for element in cluster:
                  _lowerCAmelCase							     =							element["""base_index"""] in extreme_dict
                  if element["is_extreme"]:
                        _lowerCAmelCase							     =							extreme_dict[element["""base_index"""]]["""copies"""]

      print(f"Original dataset size: {len(lowerCAmelCase  )}"  )
      print(f"Number of duplicate clusters: {len(lowerCAmelCase  )}"  )
      print(f"Files in duplicate cluster: {len(lowerCAmelCase  )}"  )
      print(f"Unique files in duplicate cluster: {len(lowerCAmelCase  )}"  )
      print(f"Filtered dataset size: {len(lowerCAmelCase  )}"  )

      return ds_filter, duplicate_clusters













import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_a 						=      logging.get_logger(__name__)

_a 						=      {
    "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",
    "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",
}
_a 						=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[Any]:







	'''simple docstring'''







	for attribute in key.split('''.'''							):
		lowerCamelCase__								=		getattr(__snake_case       ,__snake_case							)

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

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





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      int:







	'''simple docstring'''







	lowerCamelCase__								=		[]
	lowerCamelCase__								=		fairseq_model.state_dict()

	lowerCamelCase__								=		hf_model.feature_extractor

	# if encoder has different dim to decoder -> use proj_weight
	lowerCamelCase__								=		None

	for name, value in fairseq_dict.items():
		lowerCamelCase__								=		False
		if "conv_layers" in name:
			load_conv_layer(
			    __snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,hf_model.config.feat_extract_norm == '''group'''       ,)
			lowerCamelCase__								=		True
		elif name.split('''.'''							)[0] == "proj":
			lowerCamelCase__								=		fairseq_model.proj
			lowerCamelCase__								=		True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
					lowerCamelCase__								=		True
					if "*" in mapped_key:
						lowerCamelCase__								=		name.split(__snake_case							)[0].split('''.'''							)[-2]
						lowerCamelCase__								=		mapped_key.replace('''*'''       ,__snake_case							)
					if "weight_g" in name:
						lowerCamelCase__								=		'''weight_g'''
					elif "weight_v" in name:
						lowerCamelCase__								=		'''weight_v'''
					elif "bias" in name:
						lowerCamelCase__								=		'''bias'''
					elif "weight" in name:
						lowerCamelCase__								=		'''weight'''
					else:
						lowerCamelCase__								=		None
					set_recursively(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)
				continue
		if not is_used:
			unused_weights.append(__snake_case							)

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

	return proj_weight





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[int]:







	'''simple docstring'''







	lowerCamelCase__								=		full_name.split('''conv_layers.'''							)[-1]
	lowerCamelCase__								=		name.split('''.'''							)
	lowerCamelCase__								=		int(items[0]							)
	lowerCamelCase__								=		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__								=		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__								=		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__								=		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__								=		value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
	else:
		unused_weights.append(__snake_case							)





def lowerCAmelCase__(__snake_case							)				->      Any:







	'''simple docstring'''







	lowerCamelCase__				,	lowerCamelCase__								=		emb.weight.shape
	lowerCamelCase__								=		nn.Linear(__snake_case       ,__snake_case       ,bias=__snake_case							)
	lowerCamelCase__								=		emb.weight.data
	return lin_layer





def lowerCAmelCase__(__snake_case							)				->      Any:







	'''simple docstring'''







	with open(__snake_case       ,'''r'''       ,encoding='''utf-8'''							) as f:
		lowerCamelCase__								=		f.readlines()
		lowerCamelCase__								=		[line.split(''' '''							)[0] for line in lines]

	lowerCamelCase__								=		len(__snake_case							)

	lowerCamelCase__								=		{
	    '''<s>''': 0,
	    '''<pad>''': 1,
	    '''</s>''': 2,
	    '''<unk>''': 3,
	}

	vocab_dict.update(dict(zip(__snake_case       ,range(4       ,num_words + 4							)							)							)							)
	return vocab_dict





@torch.no_grad()
def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,)				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		WavaVecaConfig.from_pretrained(__snake_case							)
	lowerCamelCase__								=		SpeechaTextaConfig.from_pretrained(
	    __snake_case       ,vocab_size=__snake_case       ,decoder_layers=__snake_case       ,do_stable_layer_norm=__snake_case							)

	lowerCamelCase__								=		WavaVecaFeatureExtractor(
	    feature_size=1       ,sampling_rate=16000       ,padding_value=0       ,do_normalize=__snake_case       ,return_attention_mask=__snake_case       ,)

	lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__								=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]       ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
	lowerCamelCase__								=		model[0].eval()

	# set weights for wav2vec2 encoder
	lowerCamelCase__								=		WavaVecaModel(__snake_case							)
	lowerCamelCase__								=		recursively_load_weights_wavaveca(model.encoder       ,__snake_case							)

	lowerCamelCase__								=		SpeechaTextaForCausalLM(__snake_case							)
	lowerCamelCase__				,	lowerCamelCase__								=		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,strict=__snake_case							)

	# set output linear layer
	unexpected_keys.remove('''embed_out'''							)
	lowerCamelCase__								=		nn.Parameter(model.decoder.embed_out.detach()							)

	# layer norm is init to identity matrix so leaving it is fine
	logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'							)
	logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'							)

	lowerCamelCase__								=		SpeechEncoderDecoderModel(encoder=__snake_case       ,decoder=__snake_case							)
	lowerCamelCase__								=		False

	# add projection layer
	lowerCamelCase__								=		nn.Parameter(projection_layer.weight							)
	lowerCamelCase__								=		nn.Parameter(projection_layer.bias							)

	lowerCamelCase__								=		create_vocab_dict(__snake_case							)

	with open(os.path.join(__snake_case       ,'''vocab.json'''							)       ,'''w'''							) as fp:
		json.dump(__snake_case       ,__snake_case							)

	lowerCamelCase__								=		SpeechaTextaTokenizer(os.path.join(__snake_case       ,'''vocab.json'''							)							)
	tokenizer.save_pretrained(__snake_case							)

	lowerCamelCase__								=		hf_wavavec.config.to_dict()
	lowerCamelCase__								=		tokenizer.pad_token_id
	lowerCamelCase__								=		tokenizer.bos_token_id
	lowerCamelCase__								=		tokenizer.eos_token_id
	lowerCamelCase__								=		'''speech_to_text_2'''
	lowerCamelCase__								=		'''wav2vec2'''

	lowerCamelCase__								=		SpeechEncoderDecoderConfig.from_dict(__snake_case							)

	hf_wavavec.save_pretrained(__snake_case							)
	feature_extractor.save_pretrained(__snake_case							)


if __name__ == "__main__":
			_a 						=      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(
			    "--encoder_config_path",
			    default="facebook/wav2vec2-large-lv60",
			    type=str,
			    help="Path to hf encoder wav2vec2 checkpoint config",
			)
			parser.add_argument(
			    "--decoder_config_path",
			    default="facebook/s2t-small-mustc-en-fr-st",
			    type=str,
			    help="Path to hf decoder s2t checkpoint config",
			)
			parser.add_argument("--vocab_size", default=10_224, type=int, help="Vocab size of decoder")
			parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

			_a 						=      parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    vocab_size=args.vocab_size,
			    num_decoder_layers=args.num_decoder_layers,
			)



