





import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase       :   Optional[int]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   Optional[int]				       =							{
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """data2vec-audio"""




       def __init__(  self :    Optional[int]							,				A :    List[str]=32							,				A :    Optional[Any]=768							,				A :    Any=12							,				A :    Dict=12							,				A :    List[str]=3_072							,				A :    str="gelu"							,				A :    Dict=0.1							,				A :    Union[str, Any]=0.1							,				A :    Any=0.1							,				A :    Dict=0.0							,				A :    Tuple=0.1							,				A :    Optional[Any]=0.1							,				A :    Tuple=0.02							,				A :    int=1E-5							,				A :    int="gelu"							,				A :    Optional[Any]=(512, 512, 512, 512, 512, 512, 512)							,				A :    List[str]=(5, 2, 2, 2, 2, 2, 2)							,				A :    Tuple=(10, 3, 3, 3, 3, 2, 2)							,				A :    Dict=False							,				A :    List[Any]=16							,				A :    Optional[int]=19							,				A :    Dict=5							,				A :    Any=0.05							,				A :    int=10							,				A :    List[Any]=2							,				A :    List[str]=0.0							,				A :    List[Any]=10							,				A :    Any=0							,				A :    Optional[Any]="sum"							,				A :    Any=False							,				A :    Optional[int]=False							,				A :    Dict=256							,				A :    Optional[Any]=(512, 512, 512, 512, 1_500)							,				A :    Any=(5, 3, 3, 1, 1)							,				A :    Optional[Any]=(1, 2, 3, 1, 1)							,				A :    Optional[int]=512							,				A :    Any=0							,				A :    Tuple=1							,				A :    Optional[int]=2							,				A :    Dict=False							,				A :    Union[str, Any]=3							,				A :    Any=2							,				A :    Dict=3							,				A :    int=None							,				**A :    List[Any]							,				):
         super().__init__(**A							,				pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A		)
         __snake_case:						Optional[int]   =						hidden_size
         __snake_case:						List[Any]   =						feat_extract_activation
         __snake_case:						Optional[int]   =						list(A		)
         __snake_case:						int   =						list(A		)
         __snake_case:						Any   =						list(A		)
         __snake_case:						Union[str, Any]   =						conv_bias
         __snake_case:						List[str]   =						num_conv_pos_embeddings
         __snake_case:						Dict   =						num_conv_pos_embedding_groups
         __snake_case:						int   =						conv_pos_kernel_size
         __snake_case:						Any   =						len(self.conv_dim		)
         __snake_case:						int   =						num_hidden_layers
         __snake_case:						Any   =						intermediate_size
         __snake_case:						Optional[Any]   =						hidden_act
         __snake_case:						List[Any]   =						num_attention_heads
         __snake_case:						Optional[int]   =						hidden_dropout
         __snake_case:						List[str]   =						attention_dropout
         __snake_case:						Any   =						activation_dropout
         __snake_case:						Optional[Any]   =						feat_proj_dropout
         __snake_case:						Optional[Any]   =						final_dropout
         __snake_case:						Dict   =						layerdrop
         __snake_case:						Dict   =						layer_norm_eps
         __snake_case:						Optional[Any]   =						initializer_range
         __snake_case:						List[Any]   =						vocab_size
         __snake_case:						Union[str, Any]   =						use_weighted_layer_sum

         if (
             (len(self.conv_stride		) != self.num_feat_extract_layers)
             or (len(self.conv_kernel		) != self.num_feat_extract_layers)
             or (len(self.conv_dim		) != self.num_feat_extract_layers)
         ):
           raise ValueError(
               """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
               """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
               f''' {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,'''
               f''' `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __snake_case:						Dict   =						mask_time_prob
         __snake_case:						List[str]   =						mask_time_length
         __snake_case:						Any   =						mask_time_min_masks
         __snake_case:						List[str]   =						mask_feature_prob
         __snake_case:						Tuple   =						mask_feature_length
         __snake_case:						int   =						mask_feature_min_masks

         # ctc loss
         __snake_case:						List[Any]   =						ctc_loss_reduction
         __snake_case:						Optional[Any]   =						ctc_zero_infinity

         # adapter
         __snake_case:						int   =						add_adapter
         __snake_case:						Any   =						adapter_kernel_size
         __snake_case:						Union[str, Any]   =						adapter_stride
         __snake_case:						Dict   =						num_adapter_layers
         __snake_case:						Optional[int]   =						output_hidden_size or hidden_size

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         __snake_case:						int   =						classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         __snake_case:						List[Any]   =						list(A		)
         __snake_case:						Union[str, Any]   =						list(A		)
         __snake_case:						List[str]   =						list(A		)
         __snake_case:						Dict   =						xvector_output_dim






       @property
       def 							UpperCAmelCase__ (  self :    Any		):
         return math.prod(self.conv_stride		)













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)






