


'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

a_						:       Union[str, Any]      			=				{
    """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""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Any	):
 for attribute in key.split("."	):
  lowerCamelCase_  				=						getattr(UpperCAmelCase_      ,       UpperCAmelCase_	)

 if weight_type is not None:
  lowerCamelCase_  				=						getattr(UpperCAmelCase_      ,       UpperCAmelCase_	).shape
 else:
  lowerCamelCase_  				=						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":
  lowerCamelCase_  				=						value
 elif weight_type == "weight_g":
  lowerCamelCase_  				=						value
 elif weight_type == "weight_v":
  lowerCamelCase_  				=						value
 elif weight_type == "bias":
  lowerCamelCase_  				=						value
 else:
  lowerCamelCase_  				=						value

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





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Any	):
 lowerCamelCase_  				=						[]
 lowerCamelCase_  				=						fairseq_model.state_dict()

 lowerCamelCase_  				=						hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  lowerCamelCase_  				=						False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       hf_model.config.feat_extract_norm == "group"      ,       )
   lowerCamelCase_  				=						True
  else:
   for key, mapped_key in MAPPING.items():
    lowerCamelCase_  				=						"hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

    if key in name or (key.split("w2v_model."	)[-1] == name.split("."	)[0] and not is_finetuned):
     lowerCamelCase_  				=						True
     if "*" in mapped_key:
      lowerCamelCase_  				=						name.split(UpperCAmelCase_	)[0].split("."	)[-2]
      lowerCamelCase_  				=						mapped_key.replace("*"      ,       UpperCAmelCase_	)
     if "weight_g" in name:
      lowerCamelCase_  				=						"weight_g"
     elif "weight_v" in name:
      lowerCamelCase_  				=						"weight_v"
     elif "weight" in name:
      lowerCamelCase_  				=						"weight"
     elif "bias" in name:
      lowerCamelCase_  				=						"bias"
     else:
      lowerCamelCase_  				=						None
     set_recursively(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)
    continue
  if not is_used:
   unused_weights.append(UpperCAmelCase_	)

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





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





@torch.no_grad()
def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Dict=None      ,       UpperCAmelCase_		:		List[Any]=None      ,       UpperCAmelCase_		:		Optional[int]=True	):
 if config_path is not None:
  lowerCamelCase_  				=						HubertConfig.from_pretrained(UpperCAmelCase_	)
 else:
  lowerCamelCase_  				=						HubertConfig()

 if is_finetuned:
  if dict_path:
   lowerCamelCase_  				=						Dictionary.load(UpperCAmelCase_	)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   lowerCamelCase_  				=						target_dict.pad_index
   lowerCamelCase_  				=						target_dict.bos_index
   lowerCamelCase_  				=						target_dict.eos_index
   lowerCamelCase_  				=						len(target_dict.symbols	)
   lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "vocab.json"	)
   if not os.path.isdir(UpperCAmelCase_	):
    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase_	)	)
    return
   os.makedirs(UpperCAmelCase_      ,       exist_ok=UpperCAmelCase_	)
   with open(UpperCAmelCase_      ,       "w"      ,       encoding="utf-8"	) as vocab_handle:
    json.dump(target_dict.indices      ,       UpperCAmelCase_	)
   lowerCamelCase_  				=						WavaVecaCTCTokenizer(
       UpperCAmelCase_      ,       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=UpperCAmelCase_      ,       )
   lowerCamelCase_  				=						True if config.feat_extract_norm == "layer" else False
   lowerCamelCase_  				=						WavaVecaFeatureExtractor(
       feature_size=1      ,       sampling_rate=16000      ,       padding_value=0      ,       do_normalize=UpperCAmelCase_      ,       return_attention_mask=UpperCAmelCase_      ,       )
   lowerCamelCase_  				=						WavaVecaProcessor(feature_extractor=UpperCAmelCase_      ,       tokenizer=UpperCAmelCase_	)
   processor.save_pretrained(UpperCAmelCase_	)

  lowerCamelCase_  				=						HubertForCTC(UpperCAmelCase_	)
 else:
  lowerCamelCase_  				=						HubertModel(UpperCAmelCase_	)

 if is_finetuned:
  lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]      ,       arg_overrides={"data": "/".join(dict_path.split("/"	)[:-1]	)}	)
 else:
  lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

 lowerCamelCase_  				=						model[0].eval()

 recursively_load_weights(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)

 hf_wavavec.save_pretrained(UpperCAmelCase_	)


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







'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


a_						:       Dict      			=				logging.get_logger(__name__)

a_						:       List[Any]      			=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_						:       Optional[Any]      			=				{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

a_						:       str      			=				{
    """gpt-neox-20b""": 2048,
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						["input_ids", "attention_mask"]






  def __init__(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase=False    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(
       UpperCamelCase    ,						UpperCamelCase    ,						tokenizer_file=UpperCamelCase    ,						unk_token=UpperCamelCase    ,						bos_token=UpperCamelCase    ,						eos_token=UpperCamelCase    ,						add_prefix_space=UpperCamelCase    ,						**UpperCamelCase    ,						)

   lowerCamelCase_  				=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
   if pre_tok_state.get("add_prefix_space"    ,						UpperCamelCase  ) != add_prefix_space:
    lowerCamelCase_  				=						getattr(UpperCamelCase    ,						pre_tok_state.pop("type"  )  )
    lowerCamelCase_  				=						add_prefix_space
    lowerCamelCase_  				=						pre_tok_class(**UpperCamelCase  )

   lowerCamelCase_  				=						add_prefix_space






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._tokenizer.model.save(UpperCamelCase    ,						name=UpperCamelCase  )
   return tuple(UpperCamelCase  )







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  ) + [self.eos_token_id]  )

   if len(UpperCamelCase  ) > self.model_max_length:
    lowerCamelCase_  				=						input_ids[-self.model_max_length :]
   return input_ids



