
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)

lowercase  : Any						   =							{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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""",
}
lowercase  : Union[str, Any]						   =							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:

       for attribute in key.split("""."""    ):
              lowercase							:					List[str]	     =				getattr(__a ,     __a    )

       if weight_type is not None:
              lowercase							:					Optional[Any]	     =				getattr(__a ,     __a    ).shape
       else:
              lowercase							:					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":
              lowercase							:					Tuple	     =				value
       elif weight_type == "weight_g":
              lowercase							:					Optional[Any]	     =				value
       elif weight_type == "weight_v":
              lowercase							:					Tuple	     =				value
       elif weight_type == "bias":
              lowercase							:					str	     =				value
       elif weight_type == "running_mean":
              lowercase							:					List[Any]	     =				value
       elif weight_type == "running_var":
              lowercase							:					str	     =				value
       elif weight_type == "num_batches_tracked":
              lowercase							:					List[str]	     =				value
       elif weight_type == "inv_freq":
              lowercase							:					Optional[int]	     =				value
       else:
              lowercase							:					Dict	     =				value

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       lowercase							:					str	     =				[]
       lowercase							:					Optional[int]	     =				fairseq_model.state_dict()

       lowercase							:					Optional[Any]	     =				hf_model.wavaveca_conformer.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase							:					Union[str, Any]	     =				False
              if "conv_layers" in name:
                     load_conv_layer(
                         __a ,     __a ,     __a ,     __a ,     hf_model.config.feat_extract_norm == """group""" ,     )
                     lowercase							:					List[str]	     =				True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase							:					int	     =				'wav2vec2_conformer.' + 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]:
                                   lowercase							:					Tuple	     =				True
                                   if "*" in mapped_key:
                                          lowercase							:					List[str]	     =				name.split(__a    )[0].split("""."""    )[-2]
                                          lowercase							:					int	     =				mapped_key.replace("""*""" ,     __a    )
                                   if "pos_bias_u" in name:
                                          lowercase							:					List[Any]	     =				None
                                   elif "pos_bias_v" in name:
                                          lowercase							:					str	     =				None
                                   elif "weight_g" in name:
                                          lowercase							:					Optional[Any]	     =				'weight_g'
                                   elif "weight_v" in name:
                                          lowercase							:					Optional[int]	     =				'weight_v'
                                   elif "bias" in name:
                                          lowercase							:					Tuple	     =				'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase							:					List[str]	     =				'weight'
                                   elif "running_mean" in name:
                                          lowercase							:					Optional[int]	     =				'running_mean'
                                   elif "inv_freq" in name:
                                          lowercase							:					Dict	     =				'inv_freq'
                                   elif "running_var" in name:
                                          lowercase							:					Any	     =				'running_var'
                                   elif "num_batches_tracked" in name:
                                          lowercase							:					List[str]	     =				'num_batches_tracked'
                                   else:
                                          lowercase							:					int	     =				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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Any:
       lowercase							:					Tuple	     =				full_name.split("""conv_layers."""    )[-1]
       lowercase							:					Optional[Any]	     =				name.split("""."""    )
       lowercase							:					Optional[int]	     =				int(items[0]    )
       lowercase							:					Optional[int]	     =				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."    )
                     lowercase							:					Dict	     =				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."    )
                     lowercase							:					Dict	     =				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."    )
                     lowercase							:					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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."    )
                     lowercase							:					Optional[int]	     =				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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=None ,     SCREAMING_SNAKE_CASE__=None ,     SCREAMING_SNAKE_CASE__=True    )			->   int:

       if config_path is not None:
              lowercase							:					int	     =				WavaVecaConformerConfig.from_pretrained(__a ,     hidden_act="""swish"""    )
       else:
              lowercase							:					Tuple	     =				WavaVecaConformerConfig()

       if "rope" in checkpoint_path:
              lowercase							:					Union[str, Any]	     =				'rotary'

       if is_finetuned:
              if dict_path:
                     lowercase							:					Dict	     =				Dictionary.load(__a    )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowercase							:					Optional[int]	     =				target_dict.pad_index
                     lowercase							:					List[str]	     =				target_dict.bos_index
                     lowercase							:					int	     =				target_dict.eos_index
                     lowercase							:					Tuple	     =				len(target_dict.symbols    )
                     lowercase							:					List[str]	     =				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    )
                     lowercase							:					Union[str, Any]	     =				target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     lowercase							:					int	     =				0
                     lowercase							:					Optional[Any]	     =				1
                     with open(__a ,     """w""" ,     encoding="""utf-8"""    ) as vocab_handle:
                            json.dump(__a ,     __a    )
                     lowercase							:					Any	     =				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 ,     )
                     lowercase							:					Union[str, Any]	     =				True if config.feat_extract_norm == 'layer' else False
                     lowercase							:					Optional[Any]	     =				WavaVecaFeatureExtractor(
                         feature_size=1 ,     sampling_rate=16_000 ,     padding_value=0 ,     do_normalize=__a ,     return_attention_mask=__a ,     )
                     lowercase							:					Optional[Any]	     =				WavaVecaProcessor(feature_extractor=__a ,     tokenizer=__a    )
                     processor.save_pretrained(__a    )

              lowercase							:					int	     =				WavaVecaConformerForCTC(__a    )
       else:
              lowercase							:					Dict	     =				WavaVecaConformerForPreTraining(__a    )

       if is_finetuned:
              lowercase							:					str	     =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path] ,     arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    )}    )
       else:
              lowercase							:					Optional[int]	     =				argparse.Namespace(task="""audio_pretraining"""    )
              lowercase							:					Optional[int]	     =				fairseq.tasks.setup_task(__a    )

              lowercase							:					int	     =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,     task=__a    )

       lowercase							:					List[str]	     =				model[0].eval()

       recursively_load_weights(__a ,     __a ,     not is_finetuned    )

       hf_wavavec.save_pretrained(__a    )


if __name__ == "__main__":
 lowercase  : 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"""
 )
 lowercase  : Tuple						   =							parser.parse_args()
 convert_wavaveca_conformer_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )


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

import sentencepiece as spm

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


lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)

lowercase  : Union[str, Any]						   =							{"""vocab_file""": """spiece.model"""}

lowercase  : Any						   =							{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

lowercase  : List[Any]						   =							{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
lowercase  : Optional[int]						   =							0
lowercase  : Any						   =							1
lowercase  : Any						   =							2
lowercase  : str						   =							3
lowercase  : Optional[Any]						   =							4

class __snake_case		(				lowerCAmelCase					):
    _a		:						Optional[Any]=    VOCAB_FILES_NAMES
    _a		:						Optional[Any]=    PRETRAINED_VOCAB_FILES_MAP
    _a		:						List[str]=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						Union[str, Any]=    "left"
    def __init__(  self		,snake_case		,snake_case=False		,snake_case=True		,snake_case=False		,snake_case="<s>"		,snake_case="</s>"		,snake_case="<unk>"		,snake_case="<sep>"		,snake_case="<pad>"		,snake_case="<cls>"		,snake_case="<mask>"		,snake_case=["<eop>", "<eod>"]		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					str	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else mask_token

           lowercase							:					List[str]	     =				{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=snake_case		,remove_space=snake_case		,keep_accents=snake_case		,bos_token=snake_case		,eos_token=snake_case		,unk_token=snake_case		,sep_token=snake_case		,pad_token=snake_case		,cls_token=snake_case		,mask_token=snake_case		,additional_special_tokens=snake_case		,sp_model_kwargs=self.sp_model_kwargs		,**snake_case		,)

           lowercase							:					str	     =				3

           lowercase							:					str	     =				do_lower_case
           lowercase							:					List[Any]	     =				remove_space
           lowercase							:					Dict	     =				keep_accents
           lowercase							:					Union[str, Any]	     =				vocab_file

           lowercase							:					Any	     =				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.Load(snake_case				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return len(self.sp_model				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				{self.convert_ids_to_tokens(snake_case				): i for i in range(self.vocab_size				)}
           vocab.update(self.added_tokens_encoder				)
           return vocab
    def __getstate__(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.__dict__.copy()
           lowercase							:					List[str]	     =				None
           return state
    def __setstate__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				d

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

           lowercase							:					List[str]	     =				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.Load(self.vocab_file				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.remove_space:
                  lowercase							:					Optional[int]	     =				""" """.join(inputs.strip().split()				)
           else:
                  lowercase							:					Optional[int]	     =				inputs
           lowercase							:					Dict	     =				outputs.replace("""``"""		,"""\""""				).replace("""''"""		,"""\""""				)

           if not self.keep_accents:
                  lowercase							:					int	     =				unicodedata.normalize("""NFKD"""		,snake_case				)
                  lowercase							:					Any	     =				"""""".join([c for c in outputs if not unicodedata.combining(snake_case				)]				)
           if self.do_lower_case:
                  lowercase							:					Any	     =				outputs.lower()

           return outputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.preprocess_text(snake_case				)
           lowercase							:					Any	     =				self.sp_model.encode(snake_case		,out_type=snake_case				)
           lowercase							:					Tuple	     =				[]
           for piece in pieces:
                  if len(snake_case				) > 1 and piece[-1] == str(""","""				) and piece[-2].isdigit():
                         lowercase							:					Dict	     =				self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case		,""""""				)				)
                         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                if len(cur_pieces[0]				) == 1:
                                       lowercase							:					Any	     =				cur_pieces[1:]
                                else:
                                       lowercase							:					List[str]	     =				cur_pieces[0][1:]
                         cur_pieces.append(piece[-1]				)
                         new_pieces.extend(snake_case				)
                  else:
                         new_pieces.append(snake_case				)

           return new_pieces
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.sp_model.PieceToId(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.sp_model.IdToPiece(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				"""""".join(snake_case				).replace(snake_case		,""" """				).strip()
           return out_string
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = False		,snake_case = None		,snake_case = True		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					List[str]	     =				kwargs.pop("""use_source_tokenizer"""		,snake_case				)

           lowercase							:					List[str]	     =				self.convert_ids_to_tokens(snake_case		,skip_special_tokens=snake_case				)

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           lowercase							:					Optional[int]	     =				[]
           lowercase							:					Optional[int]	     =				[]
           for token in filtered_tokens:
                  if skip_special_tokens and token in self.all_special_ids:
                         continue
                  if token in self.added_tokens_encoder:
                         if current_sub_text:
                                sub_texts.append(self.convert_tokens_to_string(snake_case				)				)
                                lowercase							:					Union[str, Any]	     =				[]
                         sub_texts.append(snake_case				)
                  else:
                         current_sub_text.append(snake_case				)
           if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(snake_case				)				)

           # Mimic the behavior of the Rust tokenizer:
           # By default, there are no spaces between special tokens
           lowercase							:					Any	     =				"""""".join(snake_case				)

           lowercase							:					str	     =				(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                  lowercase							:					int	     =				self.clean_up_tokenization(snake_case				)
                  return clean_text
           else:
                  return text
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					int	     =				[self.sep_token_id]
           lowercase							:					str	     =				[self.cls_token_id]
           if token_ids_a is None:
                  return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = False				):



           '''simple docstring'''







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

           if token_ids_a is not None:
                  return ([0] * len(snake_case				)) + [1] + ([0] * len(snake_case				)) + [1, 1]
           return ([0] * len(snake_case				)) + [1, 1]
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				[self.sep_token_id]
           lowercase							:					Any	     =				[2]

           if token_ids_a is None:
                  return len(token_ids_a + sep				) * [0] + cls_segment_id
           return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







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

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

           return (out_vocab_file,)
