



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a__   	=			{
    "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",
}

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

def   _lowerCAmelCase   (						A__						,  A__=False	):
      lowercase__,	lowercase__     =  create_model(
          'HTSAT-tiny'						,  'roberta'						,  A__						,  precision='fp32'						,  device='cuda:0' if torch.cuda.is_available() else 'cpu'						,  enable_fusion=A__						,  fusion_type='aff_2d' if enable_fusion else None						,  )
      return model, model_cfg

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  {}

      lowercase__     =  r'.*sequential.(\d+).*'
      lowercase__     =  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:
                        lowercase__     =  key.replace(A__						,  A__	)

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

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

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

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

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

                  lowercase__     =  query_layer
                  lowercase__     =  key_layer
                  lowercase__     =  value_layer
            else:
                  lowercase__     =  value

      return model_state_dict

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__=False	):
      lowercase__,	lowercase__     =  init_clap(A__						,  enable_fusion=A__	)

      clap_model.eval()
      lowercase__     =  clap_model.state_dict()
      lowercase__     =  rename_state_dict(A__	)

      lowercase__     =  ClapConfig()
      lowercase__     =  enable_fusion
      lowercase__     =  ClapModel(A__	)

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

      model.save_pretrained(A__	)
      transformers_config.save_pretrained(A__	)


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

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





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


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

a__      :				Optional[Any]   	=			{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    "umt5"
   A    :  List[str]       				=    ["past_key_values"]


   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			Optional[int]=25_01_12			,				lowerCAmelCase  :			str=5_12			,				lowerCAmelCase  :			List[Any]=64			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Union[str, Any]=8			,				lowerCAmelCase  :			Tuple=None			,				lowerCAmelCase  :			Optional[Any]=6			,				lowerCAmelCase  :			int=32			,				lowerCAmelCase  :			int=1_28			,				lowerCAmelCase  :			List[str]=0.1			,				lowerCAmelCase  :			List[str]=1E-6			,				lowerCAmelCase  :			Optional[int]=1.0			,				lowerCAmelCase  :			Optional[Any]="gated-gelu"			,				lowerCAmelCase  :			List[Any]=True			,				lowerCAmelCase  :			List[str]=True			,				lowerCAmelCase  :			List[Any]="T5Tokenizer"			,				lowerCAmelCase  :			str=True			,				lowerCAmelCase  :			Optional[int]=0			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			Any=0			,				**lowerCAmelCase  :			int			,				)	->				str:




         """simple docstring"""

         super().__init__(
             is_encoder_decoder=lowerCAmelCase			,				tokenizer_class=lowerCAmelCase			,				tie_word_embeddings=lowerCAmelCase			,				pad_token_id=lowerCAmelCase			,				eos_token_id=lowerCAmelCase			,				decoder_start_token_id=lowerCAmelCase			,				**lowerCAmelCase			,				)
         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  d_kv
         lowercase__     =  d_ff
         lowercase__     =  num_layers
         lowercase__     =  (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowercase__     =  num_heads
         lowercase__     =  relative_attention_num_buckets
         lowercase__     =  relative_attention_max_distance
         lowercase__     =  dropout_rate
         lowercase__     =  layer_norm_epsilon
         lowercase__     =  initializer_factor
         lowercase__     =  feed_forward_proj
         lowercase__     =  use_cache

         lowercase__     =  self.feed_forward_proj.split('-')
         lowercase__     =  act_info[-1]
         lowercase__     =  act_info[0] == 'gated'

         if len(lowerCAmelCase) > 1 and act_info[0] != "gated" or len(lowerCAmelCase) > 2:
               raise ValueError(
                   f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                   'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                   '\'gated-gelu\' or \'relu\'')

         if feed_forward_proj == "gated-gelu":
               lowercase__     =  'gelu_new'


   @property
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         return self.d_model


   @property
   def 			UpperCAmelCase		(						self  :			List[str])	->				Union[str, Any]:




         """simple docstring"""

         return self.num_heads



   @property
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Optional[Any]:




         """simple docstring"""

         return self.num_layers



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         lowercase__     =  {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
               lowercase__     =  'past_encoder_sequence + sequence'
               lowercase__     =  {0: 'batch'}
               lowercase__     =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
               lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}
               lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
               self.fill_with_past_key_values_(lowerCAmelCase			,				direction='inputs')

         return common_inputs


   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 			UpperCAmelCase		(						self  :			int)	->				int:




         """simple docstring"""

         return 13



   @property
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				float:




         """simple docstring"""

         return 5E-4
