





"""simple docstring"""

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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_barthez import BarthezTokenizer
else:
     lowercase__					:						List[Any]												=			None

lowercase__					:						Dict												=			logging.get_logger(__name__)

lowercase__					:						Optional[int]												=			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__					:						Union[str, Any]												=			{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

lowercase__					:						Optional[Any]												=			{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

lowercase__					:						Optional[int]												=			'''▁'''




class  _UpperCAmelCase (    __UpperCAmelCase):
 _lowerCAmelCase     :    Tuple	      =	VOCAB_FILES_NAMES
 _lowerCAmelCase     :    Tuple	      =	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase     :    str	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase     :    str	      =	['''input_ids''', '''attention_mask''']
 _lowerCAmelCase     :    Dict	      =	BarthezTokenizer

 def __init__(  self	: Optional[int]						,  lowercase_	: Optional[int]=None						,  lowercase_	: Union[str, Any]=None						,  lowercase_	: Tuple="<s>"						,  lowercase_	: List[str]="</s>"						,  lowercase_	: Tuple="</s>"						,  lowercase_	: int="<s>"						,  lowercase_	: Any="<unk>"						,  lowercase_	: int="<pad>"						,  lowercase_	: Any="<mask>"						,  **lowercase_	: Any						,  ):
     snake_case_				:  Tuple       =			AddedToken(__SCREAMING_SNAKE_CASE						,  lstrip=__SCREAMING_SNAKE_CASE						,  rstrip=__SCREAMING_SNAKE_CASE       ) if isinstance(__SCREAMING_SNAKE_CASE						,  __SCREAMING_SNAKE_CASE       ) else mask_token

     super().__init__(
         __SCREAMING_SNAKE_CASE						,  tokenizer_file=__SCREAMING_SNAKE_CASE						,  bos_token=__SCREAMING_SNAKE_CASE						,  eos_token=__SCREAMING_SNAKE_CASE						,  unk_token=__SCREAMING_SNAKE_CASE						,  sep_token=__SCREAMING_SNAKE_CASE						,  cls_token=__SCREAMING_SNAKE_CASE						,  pad_token=__SCREAMING_SNAKE_CASE						,  mask_token=__SCREAMING_SNAKE_CASE						,  **__SCREAMING_SNAKE_CASE						,  )

     snake_case_				:  Union[str, Any]       =			vocab_file
     snake_case_				:  str       =			False if not self.vocab_file else True

 def 		_snake_case      (  self	: int						,  lowercase_	: List[int]						,  lowercase_	: Optional[List[int]] = None       ):

     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     snake_case_				:  List[Any]       =			[self.cls_token_id]
     snake_case_				:  Optional[Any]       =			[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: List[int]						,  lowercase_	: Optional[List[int]] = None       ):
     snake_case_				:  Dict       =			[self.sep_token_id]
     snake_case_				:  Union[str, Any]       =			[self.cls_token_id]

     if token_ids_a is None:
         return len(cls + token_ids_a + sep       ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






 def 		_snake_case      (  self	: List[str]						,  lowercase_	: str						,  lowercase_	: Optional[str] = None       ):

     if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.'''       )

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

     if os.path.abspath(self.vocab_file       ) != os.path.abspath(__SCREAMING_SNAKE_CASE       ):
         copyfile(self.vocab_file						,  __SCREAMING_SNAKE_CASE       )

     return (out_vocab_file,)










"""simple docstring"""

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowercase__					:						List[str]												=			{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
lowercase__					:						Dict												=			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    for attribute in key.split('''.'''					):
        snake_case_				:  Optional[Any]       =			getattr(_a	,    _a					)

    if weight_type is not None:
        snake_case_				:  Optional[int]       =			getattr(_a	,    _a					).shape
    else:
        snake_case_				:  Optional[int]       =			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_				:  Dict       =			value
    elif weight_type == "weight_g":
        snake_case_				:  Tuple       =			value
    elif weight_type == "weight_v":
        snake_case_				:  Tuple       =			value
    elif weight_type == "bias":
        snake_case_				:  int       =			value
    else:
        snake_case_				:  Optional[Any]       =			value

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




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Optional[int]       =			[]
    snake_case_				:  Optional[Any]       =			fairseq_model.state_dict()

    snake_case_				:  Optional[int]       =			hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case_				:  int       =			False
        if "conv_layers" in name:
            load_conv_layer(
                _a	,    _a	,    _a	,    _a	,    hf_model.config.feat_extract_norm == '''group'''	,    )
            snake_case_				:  Any       =			True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case_				:  Tuple       =			'''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split('''w2v_model.'''					)[-1] == name.split('''.'''					)[0]:
                    if "layer_norm_for_extract" in name and (".".join(name.split('''.'''					)[:-1]					) != key):
                        # special case since naming is very similar
                        continue
                    snake_case_				:  List[str]       =			True
                    if "*" in mapped_key:
                        snake_case_				:  List[Any]       =			name.split(_a					)[0].split('''.'''					)[-2]
                        snake_case_				:  Dict       =			mapped_key.replace('''*'''	,    _a					)
                    if "weight_g" in name:
                        snake_case_				:  Any       =			'''weight_g'''
                    elif "weight_v" in name:
                        snake_case_				:  List[str]       =			'''weight_v'''
                    elif "bias" in name:
                        snake_case_				:  List[str]       =			'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        snake_case_				:  Optional[int]       =			'''weight'''
                    else:
                        snake_case_				:  List[Any]       =			None
                    set_recursively(_a	,    _a	,    _a	,    _a	,    _a					)
                continue
        if not is_used:
            unused_weights.append(_a					)

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




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			full_name.split('''conv_layers.'''					)[-1]
    snake_case_				:  int       =			name.split('''.'''					)
    snake_case_				:  int       =			int(items[0]					)
    snake_case_				:  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."					)
            snake_case_				:  int       =			value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."					)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."					)
            snake_case_				:  str       =			value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."					)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."					)
            snake_case_				:  Dict       =			value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."					)
            snake_case_				:  Optional[Any]       =			value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
    else:
        unused_weights.append(_a					)




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a=None	,    _a=None	,    _a=True					):
    if config_path is not None:
        snake_case_				:  int       =			UniSpeechSatConfig.from_pretrained(_a					)
    else:
        snake_case_				:  Tuple       =			UniSpeechSatConfig()

    snake_case_				:  List[Any]       =			''''''

    if is_finetuned:
        snake_case_				:  str       =			UniSpeechSatForCTC(_a					)
    else:
        snake_case_				:  Dict       =			UniSpeechSatForPreTraining(_a					)

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

    recursively_load_weights(_a	,    _a					)

    hf_wavavec.save_pretrained(_a					)


if __name__ == "__main__":
     lowercase__					:						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(
         '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
     )
     lowercase__					:						Optional[int]												=			parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )





