





'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

class      snake_case     (    __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					=CanineTokenizer
 SCREAMING_SNAKE_CASE_	:					Tuple					=False






 def        _lowerCamelCase					(       self       :							Any	):
        super().setUp()
        __UpperCamelCase       =  CanineTokenizer()
        tokenizer.save_pretrained(self.tmpdirname	)






 @cached_property
 def        _lowerCamelCase					(       self       :							Optional[int]	):
        return CanineTokenizer.from_pretrained('google/canine-s'	)






 def        _lowerCamelCase					(       self       :							str							,	**__A       :							List[str]	):
        __UpperCamelCase       =  self.tokenizer_class.from_pretrained(self.tmpdirname							,	**__A	)
        __UpperCamelCase       =  1_0_2_4
        return tokenizer






 @require_torch
 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  self.canine_tokenizer
        __UpperCamelCase       =  ["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
        # fmt: off
        __UpperCamelCase       =  [5_7_3_4_4, 7_6, 1_0_5, 1_0_2, 1_0_1, 3_2, 1_0_5, 1_1_5, 3_2, 1_0_8, 1_0_5, 1_0_7, 1_0_1, 3_2, 9_7, 3_2, 9_8, 1_1_1, 1_2_0, 3_2, 1_1_1, 1_0_2, 3_2, 9_9, 1_0_4, 1_1_1, 9_9, 1_1_1, 1_0_8, 9_7, 1_1_6, 1_0_1, 1_1_5, 4_6, 5_7_3_4_5, 0, 0, 0, 0]
        # fmt: on
        __UpperCamelCase       =  tokenizer(__A							,	padding=__A							,	return_tensors='pt'	)
        self.assertIsInstance(__A							,	__A	)

        __UpperCamelCase       =  list(batch.input_ids.numpy()[0]	)

        self.assertListEqual(__A							,	__A	)

        self.assertEqual((2, 3_9)							,	batch.input_ids.shape	)
        self.assertEqual((2, 3_9)							,	batch.attention_mask.shape	)






 @require_torch
 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  self.canine_tokenizer
        __UpperCamelCase       =  ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
        __UpperCamelCase       =  tokenizer(__A							,	padding=__A							,	return_tensors='pt'	)
        # check if input_ids, attention_mask and token_type_ids are returned
        self.assertIn('input_ids'							,	__A	)
        self.assertIn('attention_mask'							,	__A	)
        self.assertIn('token_type_ids'							,	__A	)






 @require_torch
 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  self.canine_tokenizer
        __UpperCamelCase       =  [
            """What's the weater?""",
            """It's about 25 degrees.""",
        ]
        __UpperCamelCase       =  tokenizer(
            text_target=__A							,	max_length=3_2							,	padding='max_length'							,	truncation=__A							,	return_tensors='pt'	)
        self.assertEqual(3_2							,	targets['input_ids'].shape[1]	)






 def        _lowerCamelCase					(       self       :							int	):
        # safety check on max_len default value so we are sure the test works
        __UpperCamelCase       =  self.get_tokenizers()
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      self.assertNotEqual(tokenizer.model_max_length							,	4_2	)

        # Now let's start the test
        __UpperCamelCase       =  self.get_tokenizers()
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      # Isolate this from the other tests because we save additional tokens/etc
                      __UpperCamelCase       =  tempfile.mkdtemp()

                      __UpperCamelCase       =  """ He is very happy, UNwant\u00E9d,running"""
                      __UpperCamelCase       =  tokenizer.encode(__A							,	add_special_tokens=__A	)
                      tokenizer.save_pretrained(__A	)

                      __UpperCamelCase       =  tokenizer.__class__.from_pretrained(__A	)
                      __UpperCamelCase       =  after_tokenizer.encode(__A							,	add_special_tokens=__A	)
                      self.assertListEqual(__A							,	__A	)

                      shutil.rmtree(__A	)

        __UpperCamelCase       =  self.get_tokenizers(model_max_length=4_2	)
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      # Isolate this from the other tests because we save additional tokens/etc
                      __UpperCamelCase       =  tempfile.mkdtemp()

                      __UpperCamelCase       =  """ He is very happy, UNwant\u00E9d,running"""

                      __UpperCamelCase       =  tokenizer.additional_special_tokens

                      # We can add a new special token for Canine as follows:
                      __UpperCamelCase       =  chr(0xe007	)
                      additional_special_tokens.append(__A	)
                      tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}	)
                      __UpperCamelCase       =  tokenizer.encode(__A							,	add_special_tokens=__A	)
                      tokenizer.save_pretrained(__A	)

                      __UpperCamelCase       =  tokenizer.__class__.from_pretrained(__A	)
                      __UpperCamelCase       =  after_tokenizer.encode(__A							,	add_special_tokens=__A	)
                      self.assertListEqual(__A							,	__A	)
                      self.assertIn(__A							,	after_tokenizer.additional_special_tokens	)
                      self.assertEqual(after_tokenizer.model_max_length							,	4_2	)

                      __UpperCamelCase       =  tokenizer.__class__.from_pretrained(__A							,	model_max_length=4_3	)
                      self.assertEqual(tokenizer.model_max_length							,	4_3	)

                      shutil.rmtree(__A	)






 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  self.get_tokenizers(do_lower_case=__A	)
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      __UpperCamelCase       =  self.get_clean_sequence(__A	)

                      # a special token for Canine can be defined as follows:
                      __UpperCamelCase       =  0xe005
                      __UpperCamelCase       =  chr(__A	)

                      tokenizer.add_special_tokens({'cls_token': special_token}	)
                      __UpperCamelCase       =  tokenizer.encode(__A							,	add_special_tokens=__A	)
                      self.assertEqual(len(__A	)							,	1	)

                      __UpperCamelCase       =  tokenizer.decode(ids + encoded_special_token							,	clean_up_tokenization_spaces=__A	)
                      __UpperCamelCase       =  tokenizer.encode(__A							,	add_special_tokens=__A	)

                      __UpperCamelCase       =  tokenizer.encode(__A							,	add_special_tokens=__A	)
                      __UpperCamelCase       =  tokenizer.encode(__A							,	add_special_tokens=__A	)
                      self.assertEqual(__A							,	input_encoded + special_token_id	)

                      __UpperCamelCase       =  tokenizer.decode(__A							,	skip_special_tokens=__A	)
                      self.assertTrue(special_token not in decoded	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  self.get_tokenizers(do_lower_case=__A	)
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      __UpperCamelCase       =  chr(0xe005	)
                      __UpperCamelCase       =  chr(0xe006	)

                      # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                      tokenizer.add_tokens([SPECIAL_TOKEN_1]							,	special_tokens=__A	)
                      # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                      # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                      tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]}	)

                      __UpperCamelCase       =  tokenizer.tokenize(__A	)
                      __UpperCamelCase       =  tokenizer.tokenize(__A	)

                      self.assertEqual(len(__A	)							,	1	)
                      self.assertEqual(len(__A	)							,	1	)
                      self.assertEqual(token_a[0]							,	__A	)
                      self.assertEqual(token_a[0]							,	__A	)






 @require_tokenizers
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.get_tokenizers(do_lower_case=__A	)
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      # a special token for Canine can be defined as follows:
                      __UpperCamelCase       =  0xe006
                      __UpperCamelCase       =  chr(__A	)

                      __UpperCamelCase       =  AddedToken(__A							,	lstrip=__A	)
                      tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}	)

                      with tempfile.TemporaryDirectory() as tmp_dir_name:
                             tokenizer.save_pretrained(__A	)
                             tokenizer.from_pretrained(__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  []
        if self.test_slow_tokenizer:
               tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

        if self.test_rust_tokenizer:
               tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
               with tempfile.TemporaryDirectory() as tmp_dir:
                      tokenizer_utils.save_pretrained(__A	)

                      with open(os.path.join(__A							,	'special_tokens_map.json'	)							,	encoding='utf-8'	) as json_file:
                             __UpperCamelCase       =  json.load(__A	)

                      with open(os.path.join(__A							,	'tokenizer_config.json'	)							,	encoding='utf-8'	) as json_file:
                             __UpperCamelCase       =  json.load(__A	)

                      # a special token for Canine can be defined as follows:
                      __UpperCamelCase       =  0xe006
                      __UpperCamelCase       =  chr(__A	)

                      __UpperCamelCase       =  [new_token_a]
                      __UpperCamelCase       =  [new_token_a]

                      with open(os.path.join(__A							,	'special_tokens_map.json'	)							,	'w'							,	encoding='utf-8'	) as outfile:
                             json.dump(__A							,	__A	)
                      with open(os.path.join(__A							,	'tokenizer_config.json'	)							,	'w'							,	encoding='utf-8'	) as outfile:
                             json.dump(__A							,	__A	)

                      # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                      # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                      # "special_tokens_map.json" files
                      __UpperCamelCase       =  tokenizer_class.from_pretrained(__A							,	extra_ids=0	)
                      self.assertIn(__A							,	tokenizer_without_change_in_init.additional_special_tokens	)
                      # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                      self.assertEqual(
                          [new_token_a]							,	tokenizer_without_change_in_init.convert_ids_to_tokens(
                              tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]	)	)							,	)

                      __UpperCamelCase       =  0xe007
                      __UpperCamelCase       =  chr(__A	)
                      # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                      __UpperCamelCase       =  [AddedToken(__A							,	lstrip=__A	)]
                      __UpperCamelCase       =  tokenizer_class.from_pretrained(
                          __A							,	additional_special_tokens=__A							,	extra_ids=0	)

                      self.assertIn(__A							,	tokenizer.additional_special_tokens	)
                      # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                      self.assertEqual(
                          [new_token_a]							,	tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]	)	)	)






 @require_tokenizers
 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  self.get_tokenizers(do_lower_case=__A	)
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      __UpperCamelCase       =  """hello world"""
                      if self.space_between_special_tokens:
                             __UpperCamelCase       =  """[CLS] hello world [SEP]"""
                      else:
                             __UpperCamelCase       =  input
                      __UpperCamelCase       =  tokenizer.encode(__A							,	add_special_tokens=__A	)
                      __UpperCamelCase       =  tokenizer.decode(__A							,	spaces_between_special_tokens=self.space_between_special_tokens	)
                      self.assertIn(__A							,	[output, output.lower()]	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.get_tokenizers()
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      __UpperCamelCase       =  [
                          """bos_token""",
                          """eos_token""",
                          """unk_token""",
                          """sep_token""",
                          """pad_token""",
                          """cls_token""",
                          """mask_token""",
                      ]

        __UpperCamelCase       =  """a"""
        __UpperCamelCase       =  ord(__A	)

        for attr in attributes_list:
               setattr(__A							,	attr + '_id'							,	__A	)
               self.assertEqual(getattr(__A							,	__A	)							,	__A	)
               self.assertEqual(getattr(__A							,	attr + '_id'	)							,	__A	)

               setattr(__A							,	attr + '_id'							,	__A	)
               self.assertEqual(getattr(__A							,	__A	)							,	__A	)
               self.assertEqual(getattr(__A							,	attr + '_id'	)							,	__A	)

        setattr(__A							,	'additional_special_tokens_ids'							,	[]	)
        self.assertListEqual(getattr(__A							,	'additional_special_tokens'	)							,	[]	)
        self.assertListEqual(getattr(__A							,	'additional_special_tokens_ids'	)							,	[]	)

        __UpperCamelCase       =  0xe006
        __UpperCamelCase       =  chr(__A	)
        setattr(__A							,	'additional_special_tokens_ids'							,	[additional_special_token_id]	)
        self.assertListEqual(getattr(__A							,	'additional_special_tokens'	)							,	[additional_special_token]	)
        self.assertListEqual(getattr(__A							,	'additional_special_tokens_ids'	)							,	[additional_special_token_id]	)






 def        _lowerCamelCase					(       self       :							int	):
        pass






 def        _lowerCamelCase					(       self       :							Dict	):
        pass






 def        _lowerCamelCase					(       self       :							Tuple	):
        pass






 def        _lowerCamelCase					(       self       :							Tuple	):
        pass






 def        _lowerCamelCase					(       self       :							Tuple	):
        pass






 def        _lowerCamelCase					(       self       :							int	):
        pass






 def        _lowerCamelCase					(       self       :							Any	):
        pass






 def        _lowerCamelCase					(       self       :							Any	):
        pass







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)






