
'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case_ :  Dict     =					logging.get_logger(__name__)

snake_case_ :  Any     =					{
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}



def  __snake_case     (    _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       List[Any]):
   for attribute in key.split('''.'''):
      UpperCamelCase       =  getattr(_UpperCAmelCase,      _UpperCAmelCase)

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

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



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       str):
   UpperCamelCase       =  []
   UpperCamelCase       =  fairseq_model.state_dict()

   UpperCamelCase       =  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       List[str]):
   UpperCamelCase       =  full_name.split('''conv_layers.''')[-1]
   UpperCamelCase       =  name.split('''.''')
   UpperCamelCase       =  int(items[0])
   UpperCamelCase       =  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.'
         )
         UpperCamelCase       =  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.'
         )
         UpperCamelCase       =  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."
         )
         UpperCamelCase       =  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.'
         )
         UpperCamelCase       =  value
         logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
   else:
      unused_weights.append(_UpperCAmelCase)



def  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       Optional[int]):
   UpperCamelCase       =  SEWConfig()
   if is_finetuned:
      UpperCamelCase       =  model.wav_encoder.wav_model.cfg
   else:
      UpperCamelCase       =  model.cfg

   UpperCamelCase       =  fs_config.conv_bias
   UpperCamelCase       =  eval(fs_config.conv_feature_layers)
   UpperCamelCase       =  [x[0] for x in conv_layers]
   UpperCamelCase       =  [x[1] for x in conv_layers]
   UpperCamelCase       =  [x[2] for x in conv_layers]
   UpperCamelCase       =  '''gelu'''
   UpperCamelCase       =  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   UpperCamelCase       =  0.0
   UpperCamelCase       =  fs_config.activation_fn.name
   UpperCamelCase       =  fs_config.encoder_embed_dim
   UpperCamelCase       =  0.0_2
   UpperCamelCase       =  fs_config.encoder_ffn_embed_dim
   UpperCamelCase       =  1E-5
   UpperCamelCase       =  fs_config.encoder_layerdrop
   UpperCamelCase       =  fs_config.encoder_attention_heads
   UpperCamelCase       =  fs_config.conv_pos_groups
   UpperCamelCase       =  fs_config.conv_pos
   UpperCamelCase       =  len(_UpperCAmelCase)
   UpperCamelCase       =  fs_config.encoder_layers
   UpperCamelCase       =  fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      UpperCamelCase       =  model.cfg
      UpperCamelCase       =  fs_config.final_dropout
      UpperCamelCase       =  fs_config.layerdrop
   UpperCamelCase       =  fs_config.activation_dropout
   UpperCamelCase       =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   UpperCamelCase       =  fs_config.attention_dropout
   UpperCamelCase       =  fs_config.dropout_input
   UpperCamelCase       =  fs_config.dropout
   UpperCamelCase       =  fs_config.mask_channel_length
   UpperCamelCase       =  fs_config.mask_channel_prob
   UpperCamelCase       =  fs_config.mask_length
   UpperCamelCase       =  fs_config.mask_prob

   UpperCamelCase       =  '''Wav2Vec2FeatureExtractor'''
   UpperCamelCase       =  '''Wav2Vec2CTCTokenizer'''

   return config



@torch.no_grad()
def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Union[str, Any]=None,      _UpperCAmelCase					:       Dict=None,      _UpperCAmelCase					:       Dict=True):

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

   if config_path is not None:
      UpperCamelCase       =  SEWConfig.from_pretrained(_UpperCAmelCase)
   else:
      UpperCamelCase       =  convert_config(model[0],      _UpperCAmelCase)
   UpperCamelCase       =  model[0].eval()

   UpperCamelCase       =  True if config.feat_extract_norm == '''layer''' else False
   UpperCamelCase       =  WavaVecaFeatureExtractor(
       feature_size=1,      sampling_rate=1_6000,      padding_value=0,      do_normalize=_UpperCAmelCase,      return_attention_mask=_UpperCAmelCase,      )

   if is_finetuned:
      if dict_path:
         UpperCamelCase       =  Dictionary.load(_UpperCAmelCase)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         UpperCamelCase       =  target_dict.pad_index
         UpperCamelCase       =  target_dict.bos_index
         UpperCamelCase       =  target_dict.pad_index
         UpperCamelCase       =  target_dict.bos_index
         UpperCamelCase       =  target_dict.eos_index
         UpperCamelCase       =  len(target_dict.symbols)
         UpperCamelCase       =  os.path.join(_UpperCAmelCase,      '''vocab.json''')
         if not os.path.isdir(_UpperCAmelCase):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCAmelCase))
            return
         os.makedirs(_UpperCAmelCase,      exist_ok=_UpperCAmelCase)
         with open(_UpperCAmelCase,      '''w''',      encoding='''utf-8''') as vocab_handle:
            json.dump(target_dict.indices,      _UpperCAmelCase)
         UpperCamelCase       =  WavaVecaCTCTokenizer(
             _UpperCAmelCase,      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=_UpperCAmelCase,      )
         UpperCamelCase       =  WavaVecaProcessor(feature_extractor=_UpperCAmelCase,      tokenizer=_UpperCAmelCase)
         processor.save_pretrained(_UpperCAmelCase)

      UpperCamelCase       =  SEWForCTC(_UpperCAmelCase)
   else:
      UpperCamelCase       =  SEWModel(_UpperCAmelCase)
      feature_extractor.save_pretrained(_UpperCAmelCase)

   recursively_load_weights(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)

   hf_model.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
   snake_case_ :  List[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(
       '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
   )
   snake_case_ :  List[str]     =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )







'''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


snake_case_ :  Union[str, Any]     =					logging.get_logger(__name__)
snake_case_ :  Union[str, Any]     =					{'vocab_file': 'spiece.model'}

snake_case_ :  Any     =					{
    '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',
    }
}

snake_case_ :  Dict     =					{
    'AI-Sweden/gpt-sw3-126m': 2_048,
    'AI-Sweden/gpt-sw3-350m': 2_048,
    'AI-Sweden/gpt-sw3-1.6b': 2_048,
    'AI-Sweden/gpt-sw3-6.7b': 2_048,
    'AI-Sweden/gpt-sw3-20b': 2_048,
}


class 						lowercase__	(  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 ,   lowerCamelCase__ ,   lowerCamelCase__=False ,   lowerCamelCase__=False ,   lowerCamelCase__=False ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

      UpperCamelCase       =  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'''   )
         UpperCamelCase       =  '''None'''

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

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

      UpperCamelCase       =  do_lower_case
      UpperCamelCase       =  remove_space
      UpperCamelCase       =  keep_accents
      UpperCamelCase       =  vocab_file

      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(lowerCamelCase__   )

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

      # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
      UpperCamelCase       =  re.compile(
          f'[{"".join(map(lowerCamelCase__ ,   list(range(0 ,   9   )   ) + list(range(1_1 ,   3_2   )   ) + list(range(1_2_7 ,   1_6_0   )   ) + [1_6_0, 1_7_3, 8_2_0_3]   )   )}]'   )







   def __getstate__(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.__dict__.copy()
      UpperCamelCase       =  None
      return state







   def __setstate__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  d

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

      UpperCamelCase       =  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   ):

      '''simple docstring'''







      return len(self.sp_model   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.non_printing_characters_re.sub('''''' ,   lowerCamelCase__   )

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

      # NFC Unicode normalization
      UpperCamelCase       =  unicodedata.normalize('''NFC''' ,   lowerCamelCase__   )
      return text







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.preprocess_text(lowerCamelCase__   )
      return self.sp_model.encode(lowerCamelCase__ ,   out_type=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.PieceToId(lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.IdToPiece(lowerCamelCase__   )







   @staticmethod
   def       UpperCAmelCase				(			lowerCamelCase__   ):

      '''simple docstring'''







      return out_string







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  []
      UpperCamelCase       =  ''''''
      UpperCamelCase       =  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(lowerCamelCase__   ) + token
            UpperCamelCase       =  True
            UpperCamelCase       =  []
         else:
            current_sub_tokens.append(lowerCamelCase__   )
            UpperCamelCase       =  False
      out_string += self.sp_model.decode(lowerCamelCase__   )

      return out_string







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

      return (out_vocab_file,)







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







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

      if return_tensors is True or return_tensors == "pt":
         UpperCamelCase       =  torch.tensor(lowerCamelCase__   )

      return token_ids







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.decode(lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
      UpperCamelCase       =  (
          f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(lowerCamelCase__   ) + f'{self.bos_token}Bot:'
      )
      return self.encode(text=lowerCamelCase__   )





