



'''simple docstring'''






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


lowerCAmelCase:      Dict		 =		logging.get_logger(__name__)
lowerCAmelCase:      Optional[Any]		 =		{'vocab_file': 'spiece.model'}

lowerCAmelCase:      List[str]		 =		{
    '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',
    }
}

lowerCAmelCase:      Optional[int]		 =		{
    'AI-Sweden/gpt-sw3-126m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-350m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-1.6b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-6.7b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-20b': 2_0_4_8,
}

class 							a__(      lowerCamelCase__			):
    lowercase__				=					VOCAB_FILES_NAMES
    lowercase__				=					PRETRAINED_VOCAB_FILES_MAP
    lowercase__				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__				=					['''input_ids''', '''attention_mask''']







    def __init__(   self				:						int		,    __snake_case				:						Dict		,    __snake_case				:						Dict=False		,    __snake_case				:						Any=False		,    __snake_case				:						List[Any]=False		,    __snake_case				:						Optional[Any]=None		,    __snake_case				:						str=None		,    __snake_case				:						Any=None		,    __snake_case				:						Optional[Any]=None		,    __snake_case				:						Any = None		,    **__snake_case				:						str		,    ):
      a		:      Optional[Any]       =  {} if sp_model_kwargs is None else sp_model_kwargs

      a		:      str       =  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		:      Optional[Any]       =  """None"""

      # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
      a		:      List[Any]       =  """<|endoftext|>""" if eos_token is None else eos_token
      a		:      Optional[Any]       =  """<unk>""" if unk_token is None else unk_token
      if "gpt-sw3-7b" in name_or_path:
        a		:      Dict       =  unk_token if pad_token is None else pad_token
        a		:      Union[str, Any]       =  eos_token if bos_token is None else bos_token
      else:
        a		:      Optional[int]       =  """<pad>""" if pad_token is None else pad_token
        a		:      int       =  """<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		:      Union[str, Any]       =  do_lower_case
      a		:      int       =  remove_space
      a		:      Any       =  keep_accents
      a		:      Dict       =  vocab_file

      a		:      List[Any]       =  spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(A__			)

      # Used for whitespace normalization in input texts
      # fmt : off
      a		:      Union[str, Any]       =  {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
      # fmt : on

      # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
      a		:      int       =  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				:						Tuple			):
      a		:      Tuple       =  self.__dict__.copy()
      a		:      Union[str, Any]       =  None
      return state







    def __setstate__(   self				:						Tuple		,    __snake_case				:						Any			):
      a		:      List[str]       =  d

      # for backward compatibility
      if not hasattr(self		,    'sp_model_kwargs'			):
        a		:      List[str]       =  {}

      a		:      Union[str, Any]       =  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     lowercase_ (   self				:						Tuple			):
      return len(self.sp_model			)







    def     lowercase_ (   self				:						str		,    __snake_case				:						Tuple			):
      a		:      Any       =  self.non_printing_characters_re.sub(''		,    A__			)

      # Normalize whitespaces
      a		:      Any       =  """""".join([char if char not in self.whitespaces else ' ' for char in text]			)

      # NFC Unicode normalization
      a		:      Tuple       =  unicodedata.normalize('NFC'		,    A__			)
      return text







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						List[Any]		,    **__snake_case				:						Tuple			):
      a		:      Tuple       =  self.preprocess_text(A__			)
      return self.sp_model.encode(A__		,    out_type=A__			)







    def     lowercase_ (   self				:						int		,    __snake_case				:						Dict			):
      return self.sp_model.PieceToId(A__			)







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						Any			):
      return self.sp_model.IdToPiece(A__			)







    @staticmethod
    def     lowercase_ (   __snake_case				:						List[Any]			):
      return out_string







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						str			):
      a		:      int       =  []
      a		:      Dict       =  """"""
      a		:      List[str]       =  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		:      Optional[int]       =  True
          a		:      Optional[Any]       =  []
        else:
          current_sub_tokens.append(A__			)
          a		:      List[Any]       =  False
      out_string += self.sp_model.decode(A__			)

      return out_string







    def     lowercase_ (   self				:						Tuple			):
      a		:      List[Any]       =  {self.convert_ids_to_tokens(A__			): i for i in range(self.vocab_size			)}
      vocab.update(self.added_tokens_encoder			)
      return vocab







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						Optional[Any]		,    __snake_case				:						Optional[Any] = None			):
      if not os.path.isdir(A__			):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
        return
      a		:      Union[str, Any]       =  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		:      Optional[Any]       =  self.sp_model.serialized_model_proto()
          fi.write(A__			)

      return (out_vocab_file,)







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						List[Any]		,    __snake_case				:						Optional[int] = False			):

      if isinstance(A__		,    A__			):
        a		:      Optional[Any]       =  self.preprocess_text(A__			)
        a		:      Optional[int]       =  self.sp_model.encode(A__			)
      else:
        a		:      Union[str, Any]       =  [self.preprocess_text(A__			) for t in text]
        a		:      int       =  self.sp_model.encode(A__			)

      if return_tensors is True or return_tensors == "pt":
        a		:      Union[str, Any]       =  torch.tensor(A__			)

      return token_ids







    def     lowercase_ (   self				:						str		,    __snake_case				:						Tuple			):
      return self.sp_model.decode(A__			)







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						Tuple			):
      a		:      List[Any]       =  [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
      a		:      List[str]       =  (
          F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(A__			) + F"""{self.bos_token}Bot:"""
      )
      return self.encode(text=A__			)




'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCAmelCase:      Optional[int]		 =		logging.get_logger(__name__)

lowerCAmelCase:      Union[str, 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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
lowerCAmelCase:      int		 =		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  for attribute in key.split('.'       ):
    a		:      Tuple       =  getattr(_A				,    _A       )

  if weight_type is not None:
    a		:      Union[str, Any]       =  getattr(_A				,    _A       ).shape
  else:
    a		:      Optional[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":
    a		:      str       =  value
  elif weight_type == "weight_g":
    a		:      Optional[int]       =  value
  elif weight_type == "weight_v":
    a		:      List[Any]       =  value
  elif weight_type == "bias":
    a		:      Any       =  value
  else:
    a		:      Optional[Any]       =  value

  logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      Optional[int]       =  []
  a		:      Tuple       =  fairseq_model.state_dict()

  a		:      List[Any]       =  hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    a		:      Optional[Any]       =  False
    if "conv_layers" in name:
      load_conv_layer(
          _A				,    _A				,    _A				,    _A				,    hf_model.config.feat_extract_norm == 'group'				,    )
      a		:      int       =  True
    else:
      for key, mapped_key in MAPPING.items():
        a		:      Any       =  'unispeech_sat.' + 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]:
          if "layer_norm_for_extract" in name and (".".join(name.split('.'       )[:-1]       ) != key):
            # special case since naming is very similar
            continue
          a		:      str       =  True
          if "*" in mapped_key:
            a		:      Union[str, Any]       =  name.split(_A       )[0].split('.'       )[-2]
            a		:      List[str]       =  mapped_key.replace('*'				,    _A       )
          if "weight_g" in name:
            a		:      Optional[int]       =  'weight_g'
          elif "weight_v" in name:
            a		:      Any       =  'weight_v'
          elif "bias" in name:
            a		:      str       =  'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a		:      List[Any]       =  'weight'
          else:
            a		:      Any       =  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__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  a		:      List[str]       =  full_name.split('conv_layers.'       )[-1]
  a		:      Any       =  name.split('.'       )
  a		:      Optional[Any]       =  int(items[0]       )
  a		:      Union[str, Any]       =  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."""       )
      a		:      Optional[int]       =  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."""       )
      a		:      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:
      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[layer_id].layer_norm.bias.data.shape} was found."""       )
      a		:      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[layer_id].layer_norm.weight.data.shape} was found."""       )
      a		:      Union[str, Any]       =  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__ (		_A				,    _A				,    _A=None				,    _A=None				,    _A=True       ):
  if config_path is not None:
    a		:      str       =  UniSpeechSatConfig.from_pretrained(_A       )
  else:
    a		:      Dict       =  UniSpeechSatConfig()

  a		:      Any       =  ''

  if is_finetuned:
    a		:      Tuple       =  UniSpeechSatForCTC(_A       )
  else:
    a		:      List[str]       =  UniSpeechSatForPreTraining(_A       )

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

  recursively_load_weights(_A				,    _A       )

  hf_wavavec.save_pretrained(_A       )


if __name__ == "__main__":
     lowerCAmelCase:      Tuple		 =		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'
     )
     lowerCAmelCase:      Optional[int]		 =		parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )