






"""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()
_SCREAMING_SNAKE_CASE					:				int					    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:				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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		Any )      ->				Optional[Any]:



  for attribute in key.split('.' ):
    lowerCamelCase_         =						getattr(a_		,				a_ )

  if weight_type is not None:
    lowerCamelCase_         =						getattr(a_		,				a_ ).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__       (							_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		Any )      ->				Optional[Any]:
  lowerCamelCase_         =						[]
  lowerCamelCase_         =						fairseq_model.state_dict()

  lowerCamelCase_         =						hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Any		,				_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		List[Any] )      ->				str:
  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(a_ )







@torch.no_grad()
def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		int		,				_lowerCamelCase       :		Optional[int]=None		,				_lowerCamelCase       :		Union[str, Any]=None		,				_lowerCamelCase       :		Optional[int]=True )      ->				Tuple:



  if config_path is not None:
    lowerCamelCase_         =						HubertConfig.from_pretrained(a_ )
  else:
    lowerCamelCase_         =						HubertConfig()

  if is_finetuned:
    if dict_path:
      lowerCamelCase_         =						Dictionary.load(a_ )

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

    lowerCamelCase_         =						HubertForCTC(a_ )
  else:
    lowerCamelCase_         =						HubertModel(a_ )

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

  lowerCamelCase_         =						model[0].eval()

  recursively_load_weights(a_		,				a_		,				a_ )

  hf_wavavec.save_pretrained(a_ )


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[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'''
  )
  _SCREAMING_SNAKE_CASE					:				List[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
  )






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
					import torch


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE							:List[str]							 =				logging.get_logger(__name__)
SCREAMING_SNAKE_CASE							:List[str]							 =				{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE							:Dict							 =				{
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				{
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






	def __init__(       self					: Optional[int]							,A					: Optional[Any]							,A					: Optional[int]=False							,A					: int=False							,A					: Union[str, Any]=False							,A					: int=None							,A					: Optional[Any]=None							,A					: Union[str, Any]=None							,A					: Optional[Any]=None							,A					: Optional[Dict[str, Any]] = None							,**A					: Tuple							,):
					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					__A   			= kwargs.get("name_or_path" )
					if name_or_path is None:
									logger.warning(
									    "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
									    " you are testing the model, this can safely be ignored" )
									__A   			= "None"

					# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
					__A   			= "<|endoftext|>" if eos_token is None else eos_token
					__A   			= "<unk>" if unk_token is None else unk_token
					if "gpt-sw3-7b" in name_or_path:
									__A   			= unk_token if pad_token is None else pad_token
									__A   			= eos_token if bos_token is None else bos_token
					else:
									__A   			= "<pad>" if pad_token is None else pad_token
									__A   			= "<s>" if bos_token is None else bos_token

					super().__init__(
					    do_lower_case=A							,remove_space=A							,keep_accents=A							,bos_token=A							,eos_token=A							,unk_token=A							,pad_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A   			= do_lower_case
					__A   			= remove_space
					__A   			= keep_accents
					__A   			= vocab_file

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(A )

					# Used for whitespace normalization in input texts
					# fmt : off
					__A   			= {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
					# fmt : on

					# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
					__A   			= re.compile(
					    f'''[{''.join(map(A							,list(range(0							,9 ) ) + list(range(11							,32 ) ) + list(range(1_27							,1_60 ) ) + [1_60, 1_73, 82_03] ) )}]''' )






	def __getstate__(       self					: Optional[int] ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Optional[Any]							,A					: Union[str, Any] ):
					__A   			= d

					# for backward compatibility
					if not hasattr(self							,"sp_model_kwargs" ):
									__A   			= {}

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )






	@property
	# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
	def 				UpperCamelCase_  (       self					: List[str] ):
					return len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int							,A					: str ):
					__A   			= self.non_printing_characters_re.sub(""							,A )

					# Normalize whitespaces
					__A   			= "".join([char if char not in self.whitespaces else " " for char in text] )

					# NFC Unicode normalization
					__A   			= unicodedata.normalize("NFC"							,A )
					return text






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: str							,**A					: Optional[int] ):
					__A   			= self.preprocess_text(A )
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: int ):
					return self.sp_model.IdToPiece(A )






	@staticmethod
	def 				UpperCamelCase_  (       A					: str ):
					return out_string






	def 				UpperCamelCase_  (       self					: str							,A					: List[str] ):
					__A   			= []
					__A   			= ""
					__A   			= False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
													if not prev_is_special:
																	out_string += " "

													out_string += self.sp_model.decode(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )

					return out_string






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: List[str]							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
									copyfile(self.vocab_file							,A )
					elif not os.path.isfile(self.vocab_file ):
									with open(A							,"wb" ) as fi:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: Union[str, List[str]]							,A					: Union[str, bool] = False ):

					if isinstance(A							,A ):
									__A   			= self.preprocess_text(A )
									__A   			= self.sp_model.encode(A )
					else:
									__A   			= [self.preprocess_text(A ) for t in text]
									__A   			= self.sp_model.encode(A )

					if return_tensors is True or return_tensors == "pt":
									__A   			= torch.tensor(A )

					return token_ids






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Union[int, List[int]] ):
					return self.sp_model.decode(A )







	def 				UpperCamelCase_  (       self					: List[str]							,A					: "Conversation" ):
					__A   			= [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
					__A   			= (
					    f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
					)
					return self.encode(text=A )
