



"""simple docstring"""





import math
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__ = 0			,    lowerCAmelCase__ = 0   ):
      UpperCAmelCase_    				=							end or len(lowerCAmelCase__   )
      for i in range(lowerCAmelCase__			,    lowerCAmelCase__   ):
            UpperCAmelCase_    				=							i
            UpperCAmelCase_    				=							array[i]
            while temp_index != start and temp_index_value < array[temp_index - 1]:
                  UpperCAmelCase_    				=							array[temp_index - 1]
                  temp_index -= 1
            UpperCAmelCase_    				=							temp_index_value
      return array
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):  # Max Heap
      UpperCAmelCase_    				=							index
      UpperCAmelCase_    				=							2 * index + 1  # Left Node
      UpperCAmelCase_    				=							2 * index + 2  # Right Node

      if left_index < heap_size and array[largest] < array[left_index]:
            UpperCAmelCase_    				=							left_index

      if right_index < heap_size and array[largest] < array[right_index]:
            UpperCAmelCase_    				=							right_index

      if largest != index:
            UpperCAmelCase_				,					UpperCAmelCase_    				=							array[largest], array[index]
            heapify(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							len(lowerCAmelCase__   )

      for i in range(n // 2			,    -1			,    -1   ):
            heapify(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )

      for i in range(n - 1			,    0			,    -1   ):
            UpperCAmelCase_				,					UpperCAmelCase_    				=							array[0], array[i]
            heapify(lowerCAmelCase__			,    0			,    lowerCAmelCase__   )

      return array
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      if (array[first_index] > array[middle_index]) != (
          array[first_index] > array[last_index]
      ):
            return array[first_index]
      elif (array[middle_index] > array[first_index]) != (
          array[middle_index] > array[last_index]
      ):
            return array[middle_index]
      else:
            return array[last_index]
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							low
      UpperCAmelCase_    				=							high
      while True:
            while array[i] < pivot:
                  i += 1
            j -= 1
            while pivot < array[j]:
                  j -= 1
            if i >= j:
                  return i
            UpperCAmelCase_				,					UpperCAmelCase_    				=							array[j], array[i]
            i += 1
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) == 0:
            return array
      UpperCAmelCase_    				=							2 * math.ceil(math.loga(len(lowerCAmelCase__   )   )   )
      UpperCAmelCase_    				=							16
      return intro_sort(lowerCAmelCase__			,    0			,    len(lowerCAmelCase__   )			,    lowerCAmelCase__			,    lowerCAmelCase__   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      while end - start > size_threshold:
            if max_depth == 0:
                  return heap_sort(lowerCAmelCase__   )
            max_depth -= 1
            UpperCAmelCase_    				=							median_of_a(lowerCAmelCase__			,    lowerCAmelCase__			,    start + ((end - start) // 2) + 1			,    end - 1   )
            UpperCAmelCase_    				=							partition(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
            intro_sort(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
            UpperCAmelCase_    				=							p
      return insertion_sort(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowerCamelCase      =		input("""Enter numbers separated by a comma : """).strip()
  lowerCamelCase      =		[float(item) for item in user_input.split(""",""")]
  print(sort(unsorted))









"""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)




