

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()
_lowerCamelCase      :					int					    =      logging.get_logger(__name__)

_lowerCamelCase      :					Optional[int]					    =      {
    '''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''',
}
_lowerCamelCase      :					str					    =      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]





def      A__					(			__A  :						Dict			)					->Optional[int]:
      __A    ={}
      with open(__A     ,				'''r'''			) as file:
            for line_number, line in enumerate(__A			):
                  __A    =line.strip()
                  if line:
                        __A    =line.split()
                        __A    =line_number
                        __A    =words[0]
                        __A    =value
      return result





def      A__					(			__A  :						Optional[int]     ,				__A  :						Dict     ,				__A  :						Tuple     ,				__A  :						List[str]     ,				__A  :						Optional[Any]			)					->Dict:
      for attribute in key.split('''.'''			):
            __A    =getattr(__A     ,				__A			)

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

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

            # let's reduce dimension
            __A    =value[0]
      else:
            __A    =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    =value
      elif weight_type == "weight_g":
            __A    =value
      elif weight_type == "weight_v":
            __A    =value
      elif weight_type == "bias":
            __A    =value
      elif weight_type == "param":
            for attribute in hf_param_name.split('''.'''			):
                  __A    =getattr(__A     ,				__A			)
            __A    =value
      else:
            __A    =value

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





def      A__					(			__A  :						Optional[int]     ,				__A  :						Optional[Any]     ,				__A  :						Dict     ,				__A  :						str     ,				__A  :						Optional[int]			)					->str:
      __A    =None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(__A			):
                  __A    =PARAM_MAPPING[full_name.split('''.'''			)[-1]]
                  __A    ='''param'''

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

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


_lowerCamelCase      :					str					    =      {
    '''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__					(			__A  :						List[Any]     ,				__A  :						Any     ,				__A  :						Optional[Any]=None     ,				__A  :						List[Any]=None			)					->Optional[Any]:
      __A    =False
      for key, mapped_key in MAPPING.items():
            __A    ='''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]:
                  __A    =True
                  if "*" in mapped_key:
                        __A    =name.split(__A			)[0].split('''.'''			)[-2]
                        __A    =mapped_key.replace('''*'''     ,				__A			)
                  if "weight_g" in name:
                        __A    ='''weight_g'''
                  elif "weight_v" in name:
                        __A    ='''weight_v'''
                  elif "bias" in name:
                        __A    ='''bias'''
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        __A    ='''weight'''
                  else:
                        __A    =None
                  if hf_dict is not None:
                        rename_dict(__A     ,				__A     ,				__A     ,				__A     ,				__A			)
                  else:
                        set_recursively(__A     ,				__A     ,				__A     ,				__A     ,				__A			)
                  return is_used
      return is_used





def      A__					(			__A  :						List[str]     ,				__A  :						Tuple     ,				__A  :						Optional[Any]			)					->str:
      __A    =[]
      __A    =fairseq_model.state_dict()

      __A    =hf_model.wavaveca.feature_extractor

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

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





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any     ,				__A  :						Union[str, Any]     ,				__A  :						Tuple     ,				__A  :						Dict			)					->List[str]:
      __A    =full_name.split('''conv_layers.'''			)[-1]
      __A    =name.split('''.'''			)
      __A    =int(items[0]			)
      __A    =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    =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    =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.'''			)
                  __A    =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.'''			)
                  __A    =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      A__					(			__A  :						List[str]     ,				__A  :						str     ,				__A  :						Dict=None     ,				__A  :						Tuple=None     ,				__A  :						Tuple=True     ,				__A  :						Union[str, Any]=False			)					->Optional[Any]:
      if config_path is not None:
            __A    =WavaVecaConfig.from_pretrained(__A			)
      else:
            __A    =WavaVecaConfig()

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

      elif is_finetuned:
            if dict_path:
                  __A    =Dictionary.load(__A			)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  __A    =target_dict.pad_index
                  __A    =target_dict.bos_index
                  __A    =target_dict.eos_index
                  __A    =len(target_dict.symbols			)
                  __A    =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			)
                  __A    =target_dict.indices

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

            __A    =WavaVecaForCTC(__A			)
      else:
            __A    =WavaVecaForPreTraining(__A			)

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

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

      __A    =model[0].eval()

      recursively_load_weights(__A     ,				__A     ,				not is_finetuned			)

      hf_wavavec.save_pretrained(__A			)


if __name__ == "__main__":
   _lowerCamelCase      :					Dict					    =      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''',
   )
   _lowerCamelCase      :					List[Any]					    =      parser.parse_args()

   _lowerCamelCase      :					Dict					    =      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,
   )





from typing import Dict, List, Optional

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


_lowerCamelCase      :					Any					    =      logging.get_logger(__name__)


_lowerCamelCase      :					Tuple					    =      {
    '''nielsr/canine-s''': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCamelCase      :					List[str]					    =      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_lowerCamelCase      :					List[str]					    =      0

_lowerCamelCase      :					Any					    =      0xe_0_0_0
_lowerCamelCase      :					Union[str, Any]					    =      0xe_0_0_1
_lowerCamelCase      :					Any					    =      0xe_0_0_2
_lowerCamelCase      :					List[str]					    =      0xe_0_0_3
_lowerCamelCase      :					Any					    =      0xe_0_0_4

# Maps special codepoints to human-readable names.
_lowerCamelCase      :					Dict[int, str]					    =      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_lowerCamelCase      :					Dict[str, int]					    =      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

       def __init__(							self ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=False ,   lowercase__=2_0_4_8 ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else bos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else eos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else sep_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else cls_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else mask_token

             super().__init__(
                 bos_token=lowercase__ ,   eos_token=lowercase__ ,   sep_token=lowercase__ ,   cls_token=lowercase__ ,   pad_token=lowercase__ ,   mask_token=lowercase__ ,   add_prefix_space=lowercase__ ,   model_max_length=lowercase__ ,   **lowercase__ ,   )

             # Creates a mapping for looking up the IDs of special symbols.
             __A    ={}
             for codepoint, name in SPECIAL_CODEPOINTS.items():
                   __A    =codepoint

             # Creates a mapping for looking up the string forms of special symbol IDs.
             __A    ={
                 codepoint: name for name, codepoint in self._special_codepoints.items()
             }

             __A    =UNICODE_VOCAB_SIZE
             __A    =len(self._special_codepoints     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self._unicode_vocab_size

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return list(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   return ord(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid token: \'{token}\''''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   if index in SPECIAL_CODEPOINTS:
                         return SPECIAL_CODEPOINTS[index]
                   return chr(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid id: {index}'''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return "".join(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =cls + token_ids_a + sep
             if token_ids_a is not None:
                   result += token_ids_a + sep
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=lowercase__ ,   token_ids_a=lowercase__ ,   already_has_special_tokens=lowercase__     )

             __A    =[1] + ([0] * len(lowercase__     )) + [1]
             if token_ids_a is not None:
                   result += ([0] * len(lowercase__     )) + [1]
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =len(cls + token_ids_a + sep     ) * [0]
             if token_ids_a is not None:
                   result += len(token_ids_a + sep     ) * [1]
             return result


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             return ()


