






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {
    """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""",
    """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""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCamelCase__       		=  [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 	UpperCamelCase	(							snake_case__  : Dict						,snake_case__  : Dict						,snake_case__  : str						,snake_case__  : str						,snake_case__  : Any						,snake_case__  : str	):




     '''simple docstring'''






     for attribute in key.split("""."""	):
          if is_finetuned:
               if attribute in ["quantizer", "project_q", "project_hid"]:
                    # those layers are only relevant for pretraining and should be dropped
                    return

               if attribute == "ctc_proj":
                    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                    __snake_case      :Any 							=    """lm_head"""

          __snake_case      :Optional[int] 							=    getattr(snake_case__						,snake_case__	)

     if weight_type is not None:
          __snake_case      :Tuple 							=    getattr(snake_case__						,snake_case__	).shape
     else:
          __snake_case      :int 							=    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":
          __snake_case      :Any 							=    value
     elif weight_type == "weight_g":
          __snake_case      :List[str] 							=    value
     elif weight_type == "weight_v":
          __snake_case      :Optional[int] 							=    value
     elif weight_type == "bias":
          __snake_case      :List[Any] 							=    value
     else:
          __snake_case      :Optional[int] 							=    value

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






def 	UpperCamelCase	(							snake_case__  : int						,snake_case__  : List[str]						,snake_case__  : int	):




     '''simple docstring'''






     __snake_case      :List[Any] 							=    []
     __snake_case      :List[str] 							=    fairseq_model.state_dict()

     __snake_case      :Optional[int] 							=    hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          __snake_case      :Optional[int] 							=    False
          if "conv_layers" in name:
               load_conv_layer(
                   snake_case__						,snake_case__						,snake_case__						,snake_case__						,hf_model.config.feat_extract_norm == """group"""						,)
               __snake_case      :Union[str, Any] 							=    True
          else:
               for key, mapped_key in MAPPING.items():
                    __snake_case      :Optional[Any] 							=    """unispeech.""" + 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      :Tuple 							=    True
                         if "*" in mapped_key:
                              __snake_case      :Dict 							=    name.split(snake_case__	)[0].split("""."""	)[-2]
                              __snake_case      :List[str] 							=    mapped_key.replace("""*"""						,snake_case__	)
                         if "weight_g" in name:
                              __snake_case      :Any 							=    """weight_g"""
                         elif "weight_v" in name:
                              __snake_case      :str 							=    """weight_v"""
                         elif "bias" in name:
                              __snake_case      :Optional[Any] 							=    """bias"""
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __snake_case      :List[str] 							=    """weight"""
                         else:
                              __snake_case      :List[str] 							=    None
                         set_recursively(snake_case__						,snake_case__						,snake_case__						,snake_case__						,snake_case__						,snake_case__	)
                    continue
          if not is_used:
               unused_weights.append(snake_case__	)

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






def 	UpperCamelCase	(							snake_case__  : int						,snake_case__  : str						,snake_case__  : Union[str, Any]						,snake_case__  : Union[str, Any]						,snake_case__  : int	):




     '''simple docstring'''






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






@torch.no_grad()
def 	UpperCamelCase	(							snake_case__  : Optional[Any]						,snake_case__  : Union[str, Any]						,snake_case__  : str=None						,snake_case__  : Optional[int]=None						,snake_case__  : List[Any]=True	):




     '''simple docstring'''






     if config_path is not None:
          __snake_case      :Tuple 							=    UniSpeechConfig.from_pretrained(snake_case__	)
     else:
          __snake_case      :Any 							=    UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               __snake_case      :Dict 							=    Dictionary.load_from_json(snake_case__	)

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

               # fairseq has the <pad> and <s> switched
               __snake_case      :Any 							=    42
               __snake_case      :Optional[Any] 							=    43
               with open(snake_case__						,"""w"""						,encoding="""utf-8"""	) as vocab_handle:
                    json.dump(snake_case__						,snake_case__	)
               __snake_case      :List[str] 							=    WavaVecaPhonemeCTCTokenizer(
                   snake_case__						,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=snake_case__						,)
               __snake_case      :Tuple 							=    True if config.feat_extract_norm == """layer""" else False
               __snake_case      :Any 							=    WavaVecaFeatureExtractor(
                   feature_size=1						,sampling_rate=1_6000						,padding_value=0						,do_normalize=snake_case__						,return_attention_mask=snake_case__						,)
               __snake_case      :int 							=    WavaVecaProcessor(feature_extractor=snake_case__						,tokenizer=snake_case__	)
               processor.save_pretrained(snake_case__	)

          __snake_case      :Optional[Any] 							=    UniSpeechForCTC(snake_case__	)
     else:
          __snake_case      :Union[str, Any] 							=    UniSpeechForPreTraining(snake_case__	)

     if is_finetuned:
          __snake_case							,			__snake_case							,			__snake_case      :Union[str, Any] 							=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]						,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	), """w2v_path""": checkpoint_path}	)
     else:
          __snake_case							,			__snake_case							,			__snake_case      :Dict 							=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

     __snake_case      :Any 							=    model[0].eval()

     recursively_load_weights(snake_case__						,snake_case__						,snake_case__	)

     hf_unispeech.save_pretrained(snake_case__	)


if __name__ == "__main__":
     lowerCamelCase__       		=  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"""
     )
     lowerCamelCase__       		=  parser.parse_args()
     convert_unispeech_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )






'''simple docstring'''






import os
from typing import List, Optional, Union

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


__UpperCAmelCase       							=				logging.get_logger(__name__)

__UpperCAmelCase       							=				{'''vocab_file''': '''vocab.txt'''}

__UpperCAmelCase       							=				{
    '''vocab_file''': {
        '''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
        '''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
    },
}

__UpperCAmelCase       							=				{
    '''facebook/esm2_t6_8M_UR50D''': 1_024,
    '''facebook/esm2_t12_35M_UR50D''': 1_024,
}


def    _snake_case  (	A			)		->       Optional[Any]:
							with open(A					,       '''r'''			) as f:
														lowerCAmelCase__			  =      f.read().splitlines()
														return [l.strip() for l in lines]






class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= VOCAB_FILES_NAMES
							lowercase__     :  List[Any]      		= PRETRAINED_VOCAB_FILES_MAP
							lowercase__     :  Union[str, Any]      		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__     :  Union[str, Any]      		= ["input_ids", "attention_mask"]







							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_="<unk>"   ,       lowerCamelCase_="<cls>"   ,       lowerCamelCase_="<pad>"   ,       lowerCamelCase_="<mask>"   ,       lowerCamelCase_="<eos>"   ,       **lowerCamelCase_   ,       )			->				Tuple:
														super().__init__(**lowerCamelCase_  )
														lowerCAmelCase__			  =      load_vocab_file(lowerCamelCase_  )
														lowerCAmelCase__			  =      dict(enumerate(self.all_tokens  )  )
														lowerCAmelCase__			  =      {tok: ind for ind, tok in enumerate(self.all_tokens  )}
														lowerCAmelCase__			  =      unk_token
														lowerCAmelCase__			  =      cls_token
														lowerCAmelCase__			  =      pad_token
														lowerCAmelCase__			  =      mask_token
														lowerCAmelCase__			  =      eos_token
														lowerCAmelCase__			  =      self.all_tokens
														self._create_trie(self.unique_no_split_tokens  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return self._id_to_token.get(lowerCamelCase_   ,       self.unk_token  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return self._token_to_id.get(lowerCamelCase_   ,       self._token_to_id.get(self.unk_token  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       **lowerCamelCase_  )			->				Union[str, Any]:
														return text.split()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=False  )			->				Dict:
														return len(self._id_to_token  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return {token: i for i, token in enumerate(self.all_tokens  )}







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return self._token_to_id.get(lowerCamelCase_   ,       self._token_to_id.get(self.unk_token  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return self._id_to_token.get(lowerCamelCase_   ,       self.unk_token  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				List[int]:
														lowerCAmelCase__			  =      [self.cls_token_id]
														lowerCAmelCase__			  =      [self.eos_token_id]  # No sep token in ESM vocabulary
														if token_ids_a is None:
																					if self.eos_token_id is None:
																												return cls + token_ids_a
																					else:
																												return cls + token_ids_a + sep
														elif self.eos_token_id is None:
																					raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''  )
														return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = False  )			->				List[int]:
														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    '''You should not supply a second sequence if the provided sequence of '''
																												    '''ids is already formatted with special tokens for the model.'''  )

																					return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
														lowerCAmelCase__			  =      [1] + ([0] * len(lowerCamelCase_  )) + [1]
														if token_ids_a is not None:
																					mask += [0] * len(lowerCamelCase_  ) + [1]
														return mask







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      os.path.join(lowerCamelCase_   ,       (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''  )
														with open(lowerCamelCase_   ,       '''w'''  ) as f:
																					f.write('''\n'''.join(self.all_tokens  )  )
														return (vocab_file,)







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return self.get_vocab_size(with_added_tokens=lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = False  )			->				int:
														return super()._add_tokens(lowerCamelCase_   ,       special_tokens=lowerCamelCase_  )