



"""simple docstring"""





import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


lowerCamelCase      =		False
try:
  lowerCamelCase      =		_is_package_available("""google.colab""")
except ModuleNotFoundError:
  pass
@input.register
class    lowercase__     :







       '''simple docstring'''


       def __init__(							self    :						Dict	,       _UpperCAmelCase    :						str = None	,       _UpperCAmelCase    :						list = []					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							0
             UpperCAmelCase_    				=							choices
             UpperCAmelCase_    				=							prompt
             if sys.platform == "win32":
                   UpperCAmelCase_    				=							"""*"""
             else:
                   UpperCAmelCase_    				=							"""➔ """



       def   lowercase__       (							self    :						List[str]	,       _UpperCAmelCase    :						Optional[int]	,       _UpperCAmelCase    :						str = ""					)				->		List[str]:


             '''simple docstring'''




             if sys.platform != "win32":
                   writeColor(self.choices[index]	,       32	,       a_					)
             else:
                   forceWrite(self.choices[index]	,       a_					)



       def   lowercase__       (							self    :						List[str]	,       _UpperCAmelCase    :						int					)				->		Optional[Any]:


             '''simple docstring'''




             if index == self.position:
                   forceWrite(F""" {self.arrow_char} """					)
                   self.write_choice(a_					)
             else:
                   forceWrite(F"""    {self.choices[index]}"""					)
             reset_cursor()



       def   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						Direction	,       _UpperCAmelCase    :						int = 1					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.position
             if direction == Direction.DOWN:
                   if self.position + 1 >= len(self.choices					):
                         return
                   self.position += num_spaces
             else:
                   if self.position - 1 < 0:
                         return
                   self.position -= num_spaces
             clear_line()
             self.print_choice(a_					)
             move_cursor(a_	,       direction.name					)
             self.print_choice(self.position					)



       @input.mark(KEYMAP["up"]					)
       def   lowercase__       (							self    :						Dict					)				->		List[str]:


             '''simple docstring'''




             self.move_direction(Direction.UP					)



       @input.mark(KEYMAP["down"]					)
       def   lowercase__       (							self    :						Optional[int]					)				->		Dict:


             '''simple docstring'''




             self.move_direction(Direction.DOWN					)



       @input.mark(KEYMAP["newline"]					)
       def   lowercase__       (							self    :						Tuple					)				->		Tuple:


             '''simple docstring'''




             move_cursor(len(self.choices					) - self.position	,       "DOWN"					)
             return self.position



       @input.mark(KEYMAP["interrupt"]					)
       def   lowercase__       (							self    :						Optional[Any]					)				->		Tuple:


             '''simple docstring'''




             move_cursor(len(self.choices					) - self.position	,       "DOWN"					)
             raise KeyboardInterrupt



       @input.mark_multiple(*[KEYMAP[str(a_					)] for number in range(10					)]					)
       def   lowercase__       (							self    :						Any					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							int(chr(self.current_selection					)					)
             UpperCAmelCase_    				=							index - self.position
             if index == self.position:
                   return
             if index < len(self.choices					):
                   if self.position > index:
                         self.move_direction(Direction.UP	,       -movement					)
                   elif self.position < index:
                         self.move_direction(Direction.DOWN	,       a_					)
                   else:
                         return
             else:
                   return







       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						int = 0					)				->		List[Any]:


             '''simple docstring'''




             if self.prompt:
                   linebreak()
                   forceWrite(self.prompt	,       "\n"					)
                   if in_colab:
                         forceWrite("Please input a choice index (starting from 0), and press enter"	,       "\n"					)
                   else:
                         forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"	,       "\n"					)
             UpperCAmelCase_    				=							default_choice
             for i in range(len(self.choices					)					):
                   self.print_choice(a_					)
                   forceWrite("\n"					)
             move_cursor(len(self.choices					) - self.position	,       "UP"					)
             with cursor.hide():
                   while True:
                         if in_colab:
                               try:
                                     UpperCAmelCase_    				=							int(builtins.input()					)
                               except ValueError:
                                     UpperCAmelCase_    				=							default_choice
                         else:
                               UpperCAmelCase_    				=							self.handle_input()
                         if choice is not None:
                               reset_cursor()
                               for _ in range(len(self.choices					) + 1					):
                                     move_cursor(1	,       "UP"					)
                                     clear_line()
                               self.write_choice(a_	,       "\n"					)
                               return choice









"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase      =		{
    """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      =		AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							create_model(
          "HTSAT-tiny"			,    "roberta"			,    lowerCAmelCase__			,    precision="fp32"			,    device="cuda:0" if torch.cuda.is_available() else "cpu"			,    enable_fusion=lowerCAmelCase__			,    fusion_type="aff_2d" if enable_fusion else None			,    )
      return model, model_cfg
def      a__			(   lowerCAmelCase__   ):
      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(lowerCAmelCase__			,    lowerCAmelCase__   )

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

                  UpperCAmelCase_    				=							key.replace(f"""sequential.{sequential_layer}."""			,    f"""layers.{int(lowerCAmelCase__   )//3}.linear."""   )
            elif re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							int(re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).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__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							init_clap(lowerCAmelCase__			,    enable_fusion=lowerCAmelCase__   )

      clap_model.eval()
      UpperCAmelCase_    				=							clap_model.state_dict()
      UpperCAmelCase_    				=							rename_state_dict(lowerCAmelCase__   )

      UpperCAmelCase_    				=							ClapConfig()
      UpperCAmelCase_    				=							enable_fusion
      UpperCAmelCase_    				=							ClapModel(lowerCAmelCase__   )

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

      model.save_pretrained(lowerCAmelCase__   )
      transformers_config.save_pretrained(lowerCAmelCase__   )


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

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




