





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


snake_case_           =     {
    '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',
}

snake_case_           =     AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')






def lowerCamelCase__       (				snake_case_      :    Optional[int]			, snake_case_      :    Optional[int]=False			) ->							Tuple:
   __snake_case			,      __snake_case							=  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 lowerCamelCase__       (				snake_case_      :    List[Any]			) ->							Any:
   __snake_case							=  {}

   __snake_case							=  R'''.*sequential.(\d+).*'''
   __snake_case							=  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							=  key.replace(snake_case_			, snake_case_			)

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

         __snake_case							=  key.replace(f"""sequential.{sequential_layer}."""			, f"""layers.{int(snake_case_			)//3}.linear."""			)
      elif re.match(snake_case_			, snake_case_			):
         __snake_case							=  int(re.match(snake_case_			, snake_case_			).group(1			)			)

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

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

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

         __snake_case							=  query_layer
         __snake_case							=  key_layer
         __snake_case							=  value_layer
      else:
         __snake_case							=  value

   return model_state_dict






def lowerCamelCase__       (				snake_case_      :    Optional[int]			, snake_case_      :    Dict			, snake_case_      :    List[str]			, snake_case_      :    Dict=False			) ->							Any:
   __snake_case			,      __snake_case							=  init_clap(snake_case_			, enable_fusion=snake_case_			)

   clap_model.eval()
   __snake_case							=  clap_model.state_dict()
   __snake_case							=  rename_state_dict(snake_case_			)

   __snake_case							=  ClapConfig()
   __snake_case							=  enable_fusion
   __snake_case							=  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__":
    snake_case_           =     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')
    snake_case_           =     parser.parse_args()

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








'''simple docstring'''

from __future__ import annotations

import queue
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			data
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None







def    a__							(							)						->						TreeNode:





     """simple docstring"""

     print('''\n********Press N to stop entering at any point of time********\n''' )
     _UpperCamelCase							       =			input('''Enter the value of the root node: ''' ).strip().lower()
     _UpperCamelCase							       =			queue.Queue()
     _UpperCamelCase							       =			TreeNode(int(lowercase ) )
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          _UpperCamelCase							       =			F"""Enter the left node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			left_node
          q.put(lowercase )
          _UpperCamelCase							       =			F"""Enter the right node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			right_node
          q.put(lowercase )
     raise







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     print(node.data,   end=''',''' )
     pre_order(node.left )
     pre_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     in_order(node.left )
     print(node.data,   end=''',''' )
     in_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     post_order(node.left )
     post_order(node.right )
     print(node.data,   end=''',''' )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          print(node_dequeued.data,   end=''',''' )
          if node_dequeued.left:
               q.put(node_dequeued.left )
          if node_dequeued.right:
               q.put(node_dequeued.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			[]
          while not q.empty():
               _UpperCamelCase							       =			q.get()
               print(node_dequeued.data,   end=''',''' )
               if node_dequeued.left:
                    list_.append(node_dequeued.left )
               if node_dequeued.right:
                    list_.append(node_dequeued.right )
          print()
          for node in list_:
               q.put(lowercase )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data,   end=''',''' )
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          # end of while means current node doesn't have left child
          _UpperCamelCase							       =			stack.pop()
          # start to traverse its right child
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          _UpperCamelCase							       =			stack.pop()
          print(n.data,   end=''',''' )
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase       ,							_UpperCamelCase							       =			[], []
     _UpperCamelCase							       =			node
     stacka.append(lowercase )
     while stacka:  # to find the reversed order of post order, store it in stack2
          _UpperCamelCase							       =			stacka.pop()
          if n.left:
               stacka.append(n.left )
          if n.right:
               stacka.append(n.right )
          stacka.append(lowercase )
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data,   end=''',''' )







def    a__							(							lowercase   :	str = "",   lowercase   :	List[str]=50,   lowercase   :	List[str]="*" )						->						str:





     """simple docstring"""

     if not s:
          return "\n" + width * char
     _UpperCamelCase       ,							_UpperCamelCase							       =			divmod(width - len(lowercase ) - 2,   2 )
     return F"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(prompt('Binary Tree Traversals'))

   lowercase__		:     TreeNode			=							build_tree()
   print(prompt('Pre Order Traversal'))
   pre_order(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal'))
   in_order(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal'))
   post_order(node)
   print(prompt() + '\n')

   print(prompt('Level Order Traversal'))
   level_order(node)
   print(prompt() + '\n')

   print(prompt('Actual Level Order Traversal'))
   level_order_actual(node)
   print('*' * 50 + '\n')

   print(prompt('Pre Order Traversal - Iteration Version'))
   pre_order_iter(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal - Iteration Version'))
   in_order_iter(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal - Iteration Version'))
   post_order_iter(node)
   print(prompt())



