

'''simple docstring'''






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Any							=			DebertaTokenizer
  SCREAMING_SNAKE_CASE    :  str							=			True
  SCREAMING_SNAKE_CASE    :  List[Any]							=			DebertaTokenizerFast






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   __lowercase							  = [
       '''l''',
       '''o''',
       '''w''',
       '''e''',
       '''r''',
       '''s''',
       '''t''',
       '''i''',
       '''d''',
       '''n''',
       '''\u0120''',
       '''\u0120l''',
       '''\u0120n''',
       '''\u0120lo''',
       '''\u0120low''',
       '''er''',
       '''\u0120lowest''',
       '''\u0120newer''',
       '''\u0120wider''',
       '''[UNK]''',
   ]
   __lowercase							  = dict(zip(lowercase__					,range(len(lowercase__			)			)			)			)
   __lowercase							  = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
   __lowercase							  = {'''unk_token''': '''[UNK]'''}

   __lowercase							  = os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''vocab_file''']			)
   __lowercase							  = os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''merges_file''']			)
   with open(self.vocab_file					,'''w'''					,encoding='''utf-8'''			) as fp:
    fp.write(json.dumps(lowercase__			) + '''\n'''			)
   with open(self.merges_file					,'''w'''					,encoding='''utf-8'''			) as fp:
    fp.write('''\n'''.join(lowercase__			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,**lowercase__   :     Any			):
   kwargs.update(self.special_tokens_map			)
   return self.tokenizer_class.from_pretrained(self.tmpdirname					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     int			):
   __lowercase							  = '''lower newer'''
   __lowercase							  = '''lower newer'''
   return input_text, output_text






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_tokenizer()
   __lowercase							  = '''lower newer'''
   __lowercase							  = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
   __lowercase							  = tokenizer.tokenize(lowercase__			)
   self.assertListEqual(lowercase__					,lowercase__			)

   __lowercase							  = tokens + [tokenizer.unk_token]
   __lowercase							  = [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__			)					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = self.get_tokenizer()
   __lowercase							  = tokenizer('''Hello'''					,'''World'''			)
   __lowercase							  = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
   self.assertListEqual(tokd['''token_type_ids''']					,lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''			)

   __lowercase							  = tokenizer.encode('''sequence builders'''					,add_special_tokens=lowercase__			)
   __lowercase							  = tokenizer.encode('''multi-sequence build'''					,add_special_tokens=lowercase__			)

   __lowercase							  = tokenizer.encode(
       '''sequence builders'''					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)
   __lowercase							  = tokenizer.encode(
       '''sequence builders'''					,'''multi-sequence build'''					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)

   __lowercase							  = tokenizer.build_inputs_with_special_tokens(lowercase__			)
   __lowercase							  = tokenizer.build_inputs_with_special_tokens(lowercase__					,lowercase__			)

   assert encoded_sentence == encoded_text_from_decode
   assert encoded_pair == encoded_pair_from_decode






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = [self.tokenizer_class]
   if self.test_rust_tokenizer:
    tokenizer_classes.append(self.rust_tokenizer_class			)

   for tokenizer_class in tokenizer_classes:
    __lowercase							  = tokenizer_class.from_pretrained('''microsoft/deberta-base'''			)

    __lowercase							  = [
        '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
        '''ALBERT incorporates two parameter reduction techniques''',
        '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
        ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
        ''' vocabulary embedding.''',
    ]

    __lowercase							  = tokenizer(lowercase__					,padding=lowercase__			)
    __lowercase							  = [tokenizer.decode(lowercase__					,skip_special_tokens=lowercase__			) for seq in encoding['''input_ids''']]

    # fmt: off
    __lowercase							  = {
        '''input_ids''': [
            [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
        ],
        '''token_type_ids''': [
            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        ],
        '''attention_mask''': [
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        ]
    }
    # fmt: on

    __lowercase							  = [
        '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
        '''ALBERT incorporates two parameter reduction techniques''',
        '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
        ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
        ''' vocabulary embedding.''',
    ]

    self.assertDictEqual(encoding.data					,lowercase__			)

    for expected, decoded in zip(lowercase__					,lowercase__			):
     self.assertEqual(lowercase__					,lowercase__			)








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)






