






"""simple docstring"""







from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      '''EncodecFeatureExtractor'''
    SCREAMING_SNAKE_CASE		=      ('''T5Tokenizer''', '''T5TokenizerFast''')






    def __init__(			self     ,A     ,A ):
          super().__init__(A     ,A )
          UpperCAmelCase          =	self.feature_extractor
          UpperCAmelCase          =	False






    def     _UpperCamelCase							(			self     ,A=None     ,A=None     ,A=True ):
          return self.tokenizer.get_decoder_prompt_ids(task=A     ,language=A     ,no_timestamps=A )






    def __call__(			self     ,*A     ,**A ):
          # For backward compatibility
          if self._in_target_context_manager:
                return self.current_processor(*A     ,**A )

          UpperCAmelCase          =	kwargs.pop("""audio"""     ,A )
          UpperCAmelCase          =	kwargs.pop("""sampling_rate"""     ,A )
          UpperCAmelCase          =	kwargs.pop("""text"""     ,A )
          if len(A ) > 0:
                UpperCAmelCase          =	args[0]
                UpperCAmelCase          =	args[1:]

          if audio is None and text is None:
                raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )

          if text is not None:
                UpperCAmelCase          =	self.tokenizer(A     ,**A )

          if audio is not None:
                UpperCAmelCase          =	self.feature_extractor(A     ,*A     ,sampling_rate=A     ,**A )

          if audio is None:
                return inputs

          elif text is None:
                return audio_inputs

          else:
                UpperCAmelCase          =	audio_inputs["""input_values"""]
                if "padding_mask" in audio_inputs:
                      UpperCAmelCase          =	audio_inputs["""padding_mask"""]
                return inputs






    def     _UpperCamelCase							(			self     ,*A     ,**A ):
          UpperCAmelCase          =	kwargs.pop("""audio"""     ,A )
          UpperCAmelCase          =	kwargs.pop("""padding_mask"""     ,A )

          if len(A ) > 0:
                UpperCAmelCase          =	args[0]
                UpperCAmelCase          =	args[1:]

          if audio_values is not None:
                return self._decode_audio(A     ,padding_mask=A )
          else:
                return self.tokenizer.batch_decode(*A     ,**A )






    def     _UpperCamelCase							(			self     ,*A     ,**A ):
          return self.tokenizer.decode(*A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	to_numpy(A )
          UpperCAmelCase      , UpperCAmelCase      , UpperCAmelCase          =	audio_values.shape

          if padding_mask is None:
                return list(A )

          UpperCAmelCase          =	to_numpy(A )

          # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
          # token (so that the generated audio values are **not** treated as padded tokens)
          UpperCAmelCase          =	seq_len - padding_mask.shape[-1]
          UpperCAmelCase          =	1 - self.feature_extractor.padding_value
          UpperCAmelCase          =	np.pad(A     ,((0, 0), (0, difference))     ,"""constant"""     ,constant_values=A )

          UpperCAmelCase          =	audio_values.tolist()
          for i in range(A ):
                UpperCAmelCase          =	np.asarray(audio_values[i] )[
                    padding_mask[i][None, :] != self.feature_extractor.padding_value
                ]
                UpperCAmelCase          =	sliced_audio.reshape(A     ,-1 )

          return audio_values











"""simple docstring"""







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCamelCase					      =  {
    """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					      =  AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")

def _a			(		_snake_case				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	create_model(
          """HTSAT-tiny"""				,	"""roberta"""				,	_snake_case				,	precision="""fp32"""				,	device="""cuda:0""" if torch.cuda.is_available() else """cpu"""				,	enable_fusion=_snake_case				,	fusion_type="""aff_2d""" if enable_fusion else None				,	)
      return model, model_cfg

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	{}

      UpperCAmelCase          =	R""".*sequential.(\d+).*"""
      UpperCAmelCase          =	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:
                        UpperCAmelCase          =	key.replace(_snake_case				,	_snake_case							)

            if re.match(_snake_case				,	_snake_case							):
                  # replace sequential layers with list
                  UpperCAmelCase          =	re.match(_snake_case				,	_snake_case							).group(1							)

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

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

                  UpperCAmelCase          =	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
                  UpperCAmelCase          =	value
                  UpperCAmelCase          =	mixed_qkv.size(0							) // 3

                  UpperCAmelCase          =	mixed_qkv[:qkv_dim]
                  UpperCAmelCase          =	mixed_qkv[qkv_dim : qkv_dim * 2]
                  UpperCAmelCase          =	mixed_qkv[qkv_dim * 2 :]

                  UpperCAmelCase          =	query_layer
                  UpperCAmelCase          =	key_layer
                  UpperCAmelCase          =	value_layer
            else:
                  UpperCAmelCase          =	value

      return model_state_dict

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	init_clap(_snake_case				,	enable_fusion=_snake_case							)

      clap_model.eval()
      UpperCAmelCase          =	clap_model.state_dict()
      UpperCAmelCase          =	rename_state_dict(_snake_case							)

      UpperCAmelCase          =	ClapConfig()
      UpperCAmelCase          =	enable_fusion
      UpperCAmelCase          =	ClapModel(_snake_case							)

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

      model.save_pretrained(_snake_case							)
      transformers_config.save_pretrained(_snake_case							)


if __name__ == "__main__":
  _UpperCamelCase					      =  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					      =  parser.parse_args()

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



