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

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


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  lowercase  :					List[Any]						    =							None


lowercase  :					str						    =							logging.get_logger(__name__)

lowercase  :					Union[str, Any]						    =							{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase  :					Optional[Any]						    =							{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowercase  :					str						    =							{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

class __snake_case		(      UpperCAmelCase_     ):
    _a		:						Any=				VOCAB_FILES_NAMES
    _a		:						Dict=				PRETRAINED_VOCAB_FILES_MAP
    _a		:						int=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						str=				['''input_ids''', '''attention_mask''']
    _a		:						Optional[int]=				TaTokenizer

    _a		:						List[int]=				[]
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case="</s>"		,snake_case="<unk>"		,snake_case="<pad>"		,snake_case=100		,snake_case=None		,**snake_case		,):



           '''simple docstring'''







           if extra_ids > 0 and additional_special_tokens is None:
                  lowercase							:     Optional[int]       	=      [f"<extra_id_{i}>" for i in range(lowerCAmelCase__      )]
           elif extra_ids > 0 and additional_special_tokens is not None:
                  # Check that we have the right number of extra special tokens
                  lowercase							:     List[str]       	=      len(set(filter(lambda snake_case      : bool("""extra_id_""" in str(lowerCAmelCase__      )      )		,lowerCAmelCase__      )      )      )
                  if extra_tokens != extra_ids:
                         raise ValueError(
                             f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                             """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
                             """ tokens"""      )

           super().__init__(
               lowerCAmelCase__		,tokenizer_file=lowerCAmelCase__		,eos_token=lowerCAmelCase__		,unk_token=lowerCAmelCase__		,pad_token=lowerCAmelCase__		,extra_ids=lowerCAmelCase__		,additional_special_tokens=lowerCAmelCase__		,**lowerCAmelCase__		,)

           lowercase							:     Dict       	=      vocab_file
           lowercase							:     Dict       	=      False if not self.vocab_file else True
           lowercase							:     List[str]       	=      extra_ids
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                  lowercase							:     List[str]       	=      TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                  if init_max_model_length is not None and init_max_model_length != max_model_length:
                         return init_max_model_length
                  elif init_max_model_length is None:
                         warnings.warn(
                             """This tokenizer was incorrectly instantiated with a model max length of"""
                             f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
                             """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
                             """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
                             f" {pretrained_model_name_or_path} automatically truncating your input to"
                             f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
                             f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
                             """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
                             """ instantiate this tokenizer with `model_max_length` set to your preferred value."""		,lowerCAmelCase__		,)

           return max_model_length
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           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(lowerCAmelCase__      ):
                  logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
                  return
           lowercase							:     Tuple       	=      os.path.join(
               lowerCAmelCase__		,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

           if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowerCAmelCase__      ):
                  copyfile(self.vocab_file		,lowerCAmelCase__      )
                  logger.info(f"Copy vocab file to {out_vocab_file}"      )

           return (out_vocab_file,)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                  return self.prefix_tokens + token_ids_a
           else:
                  lowercase							:     Optional[int]       	=      token_ids_a + [self.eos_token_id]
                  return self.prefix_tokens + token_ids_a + token_ids_a
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      [self.eos_token_id]

           if token_ids_a is None:
                  return len(token_ids_a + eos      ) * [0]
           return len(token_ids_a + eos + token_ids_a + eos      ) * [0]
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return list(
               set(filter(lambda snake_case      : bool(re.search(r"""<extra_id_\d+>"""		,lowerCAmelCase__      )      ) is not None		,self.additional_special_tokens      )      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return [self.convert_tokens_to_ids(lowerCAmelCase__      ) for token in self.get_sentinel_tokens()]



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  Union[str, 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""",
}

lowerCAmelCase							:  int   	=						AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       create_model(
						    "HTSAT-tiny"				,	"roberta"				,	_UpperCAmelCase				,	precision="fp32"				,	device="cuda:0" if torch.cuda.is_available() else "cpu"				,	enable_fusion=_UpperCAmelCase				,	fusion_type="aff_2d" if enable_fusion else None				,	)
						return model, model_cfg







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: Dict  		=       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:
																								SCREAMING_SNAKE_CASE_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

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

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"sequential.{sequential_layer}."				,	f"layers.{int(_UpperCAmelCase    )//3}.linear."    )
												elif re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		SCREAMING_SNAKE_CASE_: Any  		=       int(re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )    )

																		# Because in CLAP they use `nn.Sequential`...
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       1 if projecton_layer == 0 else 2

																		SCREAMING_SNAKE_CASE_: Dict  		=       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
																		SCREAMING_SNAKE_CASE_: Tuple  		=       value
																		SCREAMING_SNAKE_CASE_: List[str]  		=       mixed_qkv.size(0    ) // 3

																		SCREAMING_SNAKE_CASE_: Any  		=       mixed_qkv[:qkv_dim]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       mixed_qkv[qkv_dim : qkv_dim * 2]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       mixed_qkv[qkv_dim * 2 :]

																		SCREAMING_SNAKE_CASE_: str  		=       query_layer
																		SCREAMING_SNAKE_CASE_: int  		=       key_layer
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       value_layer
												else:
																		SCREAMING_SNAKE_CASE_: int  		=       value

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       ClapModel(_UpperCAmelCase    )

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

						model.save_pretrained(_UpperCAmelCase    )
						transformers_config.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
						lowerCAmelCase							:  Tuple   	=						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""")
						lowerCAmelCase							:  int   	=						parser.parse_args()

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






