





import os
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 logging


__UpperCAmelCase       :   str				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   int				       =							"▁"

__UpperCAmelCase       :   Tuple				       =							{"vocab_file": "sentencepiece.bpe.model"}

__UpperCAmelCase       :   List[Any]				       =							{
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

__UpperCAmelCase       :   Optional[Any]				       =							{
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







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




       def __init__(  self :    List[Any]							,				A :    Any							,				A :    Optional[int]="<s>"							,				A :    Optional[Any]="</s>"							,				A :    Dict="</s>"							,				A :    Optional[int]="<s>"							,				A :    Optional[int]="<unk>"							,				A :    Optional[Any]="<pad>"							,				A :    Optional[Any]="<mask>"							,				A :    Optional[Dict[str, Any]] = None							,				**A :    Dict							,				):
         # Mask token behave like a normal word, i.e. include the space before it
         __snake_case:						List[str]   =						AddedToken(A							,				lstrip=A							,				rstrip=A		) if isinstance(A							,				A		) else mask_token

         __snake_case:						Optional[int]   =						{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=A							,				eos_token=A							,				unk_token=A							,				sep_token=A							,				cls_token=A							,				pad_token=A							,				mask_token=A							,				sp_model_kwargs=self.sp_model_kwargs							,				**A							,				)

         __snake_case:						Union[str, Any]   =						spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(str(A		)		)
         __snake_case:						List[Any]   =						vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         __snake_case:						Optional[int]   =						{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         __snake_case:						Tuple   =						1

         __snake_case:						List[Any]   =						len(self.sp_model		) + self.fairseq_offset
         __snake_case:						int   =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}




       def __getstate__(  self :    Optional[int]		):
         __snake_case:						List[Any]   =						self.__dict__.copy()
         __snake_case:						List[Any]   =						None
         __snake_case:						Tuple   =						self.sp_model.serialized_model_proto()
         return state




       def __setstate__(  self :    str							,				A :    List[str]		):
         __snake_case:						Tuple   =						d

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

         __snake_case:						Dict   =						spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)




       def 							UpperCAmelCase__ (  self :    Dict							,				A :    List[int]							,				A :    Optional[List[int]] = None		):

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __snake_case:						str   =						[self.cls_token_id]
         __snake_case:						Optional[int]   =						[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 							UpperCAmelCase__ (  self :    Dict							,				A :    List[int]							,				A :    Optional[List[int]] = None							,				A :    bool = False		):

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

         if token_ids_a is None:
           return [1] + ([0] * len(A		)) + [1]
         return [1] + ([0] * len(A		)) + [1, 1] + ([0] * len(A		)) + [1]




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    List[int]							,				A :    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]




       @property
       def 							UpperCAmelCase__ (  self :    int		):
         return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						List[Any]   =						{self.convert_ids_to_tokens(A		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    str		):
         return self.sp_model.encode(A							,				out_type=A		)




       def 							UpperCAmelCase__ (  self :    int							,				A :    List[str]		):
         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         __snake_case:						str   =						self.sp_model.PieceToId(A		)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




       def 							UpperCAmelCase__ (  self :    int							,				A :    Optional[int]		):
         if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset		)




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Optional[Any]		):
         __snake_case:						Union[str, Any]   =						"""""".join(A		).replace(A							,				""" """		).strip()
         return out_string






       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    str							,				A :    Optional[str] = None		):
         if not os.path.isdir(A		):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
           return
         __snake_case:						Any   =						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:						str   =						self.sp_model.serialized_model_proto()
             fi.write(A		)

         return (out_vocab_file,)













import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCAmelCase       :   Any				       =							{
    "text_branch": "text_model",
    "audio_branch": "audio_model.audio_encoder",
    "attn": "attention.self",
    "self.proj": "output.dense",
    "attention.self_mask": "attn_mask",
    "mlp.fc1": "intermediate.dense",
    "mlp.fc2": "output.dense",
    "norm1": "layernorm_before",
    "norm2": "layernorm_after",
    "bn0": "batch_norm",
}

__UpperCAmelCase       :   List[str]				       =							AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    Union[str, Any]:
  __snake_case  ,   __snake_case:						int   =						create_model(
      """HTSAT-tiny"""					,					"""roberta"""					,					SCREAMING_SNAKE_CASE__					,					precision="""fp32"""					,					device="""cuda:0""" if torch.cuda.is_available() else """cpu"""					,					enable_fusion=SCREAMING_SNAKE_CASE__					,					fusion_type="""aff_2d""" if enable_fusion else None					,					)
  return model, model_cfg







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						Optional[Any]   =						{}

  __snake_case:						int   =						r""".*sequential.(\d+).*"""
  __snake_case:						List[str]   =						r""".*_projection.(\d+).*"""

  for key, value in state_dict.items():
    # check if any key needs to be modified
    for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
      if key_to_modify in key:
        __snake_case:						Tuple   =						key.replace(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

    if re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__):
      # replace sequential layers with list
      __snake_case:						Optional[int]   =						re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__).group(1)

      __snake_case:						str   =						key.replace(F'''sequential.{sequential_layer}.'''					,					F'''layers.{int(SCREAMING_SNAKE_CASE__)//3}.linear.''')
    elif re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__):
      __snake_case:						Any   =						int(re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__).group(1))

      # Because in CLAP they use `nn.Sequential`...
      __snake_case:						Dict   =						1 if projecton_layer == 0 else 2

      __snake_case:						Any   =						key.replace(F'''_projection.{projecton_layer}.'''					,					F'''_projection.linear{transformers_projection_layer}.''')

    if "audio" and "qkv" in key:
      # split qkv into query key and value
      __snake_case:						List[str]   =						value
      __snake_case:						Optional[Any]   =						mixed_qkv.size(0) // 3

      __snake_case:						Union[str, Any]   =						mixed_qkv[:qkv_dim]
      __snake_case:						Dict   =						mixed_qkv[qkv_dim : qkv_dim * 2]
      __snake_case:						int   =						mixed_qkv[qkv_dim * 2 :]

      __snake_case:						Optional[Any]   =						query_layer
      __snake_case:						str   =						key_layer
      __snake_case:						int   =						value_layer
    else:
      __snake_case:						Dict   =						value

  return model_state_dict







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    Optional[Any]:
  __snake_case  ,   __snake_case:						List[str]   =						init_clap(SCREAMING_SNAKE_CASE__					,					enable_fusion=SCREAMING_SNAKE_CASE__)

  clap_model.eval()
  __snake_case:						List[str]   =						clap_model.state_dict()
  __snake_case:						Optional[int]   =						rename_state_dict(SCREAMING_SNAKE_CASE__)

  __snake_case:						Any   =						ClapConfig()
  __snake_case:						Dict   =						enable_fusion
  __snake_case:						List[str]   =						ClapModel(SCREAMING_SNAKE_CASE__)

  # ignore the spectrogram embedding layer
  model.load_state_dict(SCREAMING_SNAKE_CASE__					,					strict=SCREAMING_SNAKE_CASE__)

  model.save_pretrained(SCREAMING_SNAKE_CASE__)
  transformers_config.save_pretrained(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      __UpperCAmelCase       :   Union[str, 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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
      __UpperCAmelCase       :   Tuple				       =							parser.parse_args()

      convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)






