
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()
_A						      =				logging.get_logger(__name__)

_A						      =				{
    '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',
}
_A						      =				[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def 		lowercase_    (     A__	)							->							Optional[Any]:
    """simple docstring"""

    snake_case				  =      {}
    with open(_lowerCamelCase				,	"r"	) as file:
        for line_number, line in enumerate(_lowerCamelCase	):
            snake_case				  =      line.strip()
            if line:
                snake_case				  =      line.split()
                snake_case				  =      line_number
                snake_case				  =      words[0]
                snake_case				  =      value
    return result



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

    for attribute in key.split("."	):
        snake_case				  =      getattr(_lowerCamelCase				,	_lowerCamelCase	)

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

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

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

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



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							Any:
    """simple docstring"""

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

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

    snake_case				  =      value if "lm_head" in full_key else value[0]


_A						      =				{
    '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 		lowercase_    (     A__				,	A__				,	A__=None				,	A__=None	)							->							List[str]:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__				,	A__	)							->							str:
    """simple docstring"""

    snake_case				  =      []
    snake_case				  =      fairseq_model.state_dict()

    snake_case				  =      hf_model.wavaveca.feature_extractor

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

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



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							str:
    """simple docstring"""

    snake_case				  =      full_name.split("conv_layers."	)[-1]
    snake_case				  =      name.split("."	)
    snake_case				  =      int(items[0]	)
    snake_case				  =      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.'	)
            snake_case				  =      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.'	)
            snake_case				  =      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.'	)
            snake_case				  =      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.'	)
            snake_case				  =      value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
    else:
        unused_weights.append(_lowerCamelCase	)



@torch.no_grad()
def 		lowercase_    (     A__				,	A__				,	A__=None				,	A__=None				,	A__=True				,	A__=False	)							->							Tuple:
    """simple docstring"""

    if config_path is not None:
        snake_case				  =      WavaVecaConfig.from_pretrained(_lowerCamelCase	)
    else:
        snake_case				  =      WavaVecaConfig()

    if is_seq_class:
        snake_case				  =      read_txt_into_dict(_lowerCamelCase	)
        snake_case				  =      idalabel
        snake_case				  =      WavaVecaForSequenceClassification(_lowerCamelCase	)
        snake_case				  =      WavaVecaFeatureExtractor(
            feature_size=1				,	sampling_rate=1_6000				,	padding_value=0				,	do_normalize=_lowerCamelCase				,	return_attention_mask=_lowerCamelCase				,	)
        feature_extractor.save_pretrained(_lowerCamelCase	)

    elif is_finetuned:
        if dict_path:
            snake_case				  =      Dictionary.load(_lowerCamelCase	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            snake_case				  =      target_dict.pad_index
            snake_case				  =      target_dict.bos_index
            snake_case				  =      target_dict.eos_index
            snake_case				  =      len(target_dict.symbols	)
            snake_case				  =      os.path.join(_lowerCamelCase				,	"vocab.json"	)
            if not os.path.isdir(_lowerCamelCase	):
                logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCamelCase	)	)
                return
            os.makedirs(_lowerCamelCase				,	exist_ok=_lowerCamelCase	)
            snake_case				  =      target_dict.indices

            # fairseq has the <pad> and <s> switched
            snake_case				  =      0
            snake_case				  =      1
            with open(_lowerCamelCase				,	"w"				,	encoding="utf-8"	) as vocab_handle:
                json.dump(_lowerCamelCase				,	_lowerCamelCase	)
            snake_case				  =      WavaVecaCTCTokenizer(
                _lowerCamelCase				,	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=_lowerCamelCase				,	)
            snake_case				  =      True if config.feat_extract_norm == "layer" else False
            snake_case				  =      WavaVecaFeatureExtractor(
                feature_size=1				,	sampling_rate=1_6000				,	padding_value=0				,	do_normalize=_lowerCamelCase				,	return_attention_mask=_lowerCamelCase				,	)
            snake_case				  =      WavaVecaProcessor(feature_extractor=_lowerCamelCase				,	tokenizer=_lowerCamelCase	)
            processor.save_pretrained(_lowerCamelCase	)

        snake_case				  =      WavaVecaForCTC(_lowerCamelCase	)
    else:
        snake_case				  =      WavaVecaForPreTraining(_lowerCamelCase	)

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

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

    snake_case				  =      model[0].eval()

    recursively_load_weights(_lowerCamelCase				,	_lowerCamelCase				,	not is_finetuned	)

    hf_wavavec.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
   _A						      =				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",
   )
   _A						      =				parser.parse_args()

   _A						      =				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,
   )



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A						      =				"▁"

_A						      =				{"vocab_file": "spiece.model"}

_A						      =				{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_A						      =				{
    "google/pegasus-xsum": 5_12,
}


_A						      =				logging.get_logger(__name__)







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Tuple     =	VOCAB_FILES_NAMES

     UpperCAmelCase__       :							Tuple     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							List[str]     =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							Optional[int]     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							Optional[Any]     =	["input_ids", "attention_mask"]





     def __init__(self   :					Optional[Any]       ,					_A   :					Any       ,					_A   :					List[Any]="<pad>"       ,					_A   :					int="</s>"       ,					_A   :					Dict="<unk>"       ,					_A   :					str="<mask_2>"       ,					_A   :					Optional[int]="<mask_1>"       ,					_A   :					Optional[Any]=None       ,					_A   :					Tuple=1_0_3       ,					_A   :					Optional[Dict[str, Any]] = None       ,					**_A   :					List[str]       ,					)		->		None:
         snake_case				  =      offset
         if additional_special_tokens is not None:
             if not isinstance(_A       ,					_A     ):
                 raise TypeError(
                     f'additional_special_tokens should be of type {type(_A     )}, but is'
                     f' {type(_A     )}'     )

             snake_case				  =      (
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 f'<unk_{i}>' for i in range(len(_A     )       ,					self.offset - 1     )
             ]

             if len(set(_A     )     ) != len(_A     ):
                 raise ValueError(
                     "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                     f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'     )
             snake_case				  =      additional_special_tokens_extended
         else:
             snake_case				  =      [mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [f'<unk_{i}>' for i in range(2       ,					self.offset     )]

         snake_case				  =      {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=_A       ,					unk_token=_A       ,					mask_token=_A       ,					pad_token=_A       ,					mask_token_sent=_A       ,					offset=_A       ,					additional_special_tokens=_A       ,					sp_model_kwargs=self.sp_model_kwargs       ,					**_A       ,					)
         snake_case				  =      mask_token_sent
         snake_case				  =      vocab_file
         snake_case				  =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(_A     )

         # add special tokens to encoder dict
         snake_case				  =      {
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }     )

         if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,					self.offset - 1     )}     )

         snake_case				  =      {v: k for k, v in self.encoder.items()}





     @property
     def 	UpperCAmelCase(self   :					str     )		->		int:
         return len(self.sp_model     ) + self.offset





     def 	UpperCAmelCase(self   :					List[str]     )		->		Dict[str, int]:
         snake_case				  =      {self.convert_ids_to_tokens(_A     ): i for i in range(self.vocab_size     )}
         vocab.update(self.added_tokens_encoder     )
         return vocab





     def __getstate__(self   :					Any     )		->		List[Any]:
         snake_case				  =      self.__dict__.copy()
         snake_case				  =      None
         return state





     def __setstate__(self   :					str       ,					_A   :					Union[str, Any]     )		->		Tuple:
         snake_case				  =      d

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

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





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					str     )		->		List[str]:
         return self.sp_model.encode(_A       ,					out_type=_A     )





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					str     )		->		int:
         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
         snake_case				  =      self.sp_model.piece_to_id(_A     )
         return sp_id + self.offset





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					int     )		->		str:
         if index in self.encoder:
             return self.encoder[index]
         elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
         else:
             snake_case				  =      self.sp_model.IdToPiece(index - self.offset     )
         return token





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					Tuple     )		->		Tuple:
         snake_case				  =      []
         snake_case				  =      ""
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(_A     ) + token
                 snake_case				  =      []
             else:
                 current_sub_tokens.append(_A     )
         out_string += self.sp_model.decode(_A     )
         return out_string.strip()





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					Tuple=False     )		->		Tuple:
         return 1





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[int]     )		->		Tuple:
         snake_case				  =      set(self.all_special_ids     )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

         return [1 if x in all_special_ids else 0 for x in seq]





     def 	UpperCAmelCase(self   :					str       ,					_A   :					List       ,					_A   :					Optional[List] = None       ,					_A   :					bool = False     )		->		List[int]:
         if already_has_special_tokens:
             return self._special_token_mask(_A     )
         elif token_ids_a is None:
             return self._special_token_mask(_A     ) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]





     def 	UpperCAmelCase(self   :					int       ,					_A   :					Dict       ,					_A   :					List[Any]=None     )		->		List[int]:
         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





     def 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					str       ,					_A   :					Optional[str] = None     )		->		Tuple[str]:
         if not os.path.isdir(_A     ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
             return
         snake_case				  =      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:
                 snake_case				  =      self.sp_model.serialized_model_proto()
                 fi.write(_A     )

         return (out_vocab_file,)

