




"""simple docstring"""







import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  lowerCamelCase__ (	_SCREAMING_SNAKE_CASE   ,					unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      LEDTokenizer
    SCREAMING_SNAKE_CASE		=      LEDTokenizerFast
    SCREAMING_SNAKE_CASE		=      True






    def     _UpperCamelCase							(			self ):
          super().setUp()
          UpperCAmelCase          =	[
              """l""",
              """o""",
              """w""",
              """e""",
              """r""",
              """s""",
              """t""",
              """i""",
              """d""",
              """n""",
              """\u0120""",
              """\u0120l""",
              """\u0120n""",
              """\u0120lo""",
              """\u0120low""",
              """er""",
              """\u0120lowest""",
              """\u0120newer""",
              """\u0120wider""",
              """<unk>""",
          ]
          UpperCAmelCase          =	dict(zip(A     ,range(len(A ) ) ) )
          UpperCAmelCase          =	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
          UpperCAmelCase          =	{"""unk_token""": """<unk>"""}

          UpperCAmelCase          =	os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["""vocab_file"""] )
          UpperCAmelCase          =	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(A ) + """\n""" )
          with open(self.merges_file     ,"""w"""     ,encoding="""utf-8""" ) as fp:
                fp.write("""\n""".join(A ) )






    def     _UpperCamelCase							(			self     ,**A ):
          kwargs.update(self.special_tokens_map )
          return self.tokenizer_class.from_pretrained(self.tmpdirname     ,**A )






    def     _UpperCamelCase							(			self     ,**A ):
          kwargs.update(self.special_tokens_map )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,**A )






    def     _UpperCamelCase							(			self     ,A ):
          return "lower newer", "lower newer"






    @cached_property
    def     _UpperCamelCase							(			self ):
          return LEDTokenizer.from_pretrained("""allenai/led-base-16384""" )






    @cached_property
    def     _UpperCamelCase							(			self ):
          return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""" )






    @require_torch
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          UpperCAmelCase          =	[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase          =	tokenizer(A     ,max_length=len(A )     ,padding=A     ,return_tensors="""pt""" )
                self.assertIsInstance(A     ,A )

                self.assertEqual((2, 9)     ,batch.input_ids.shape )
                self.assertEqual((2, 9)     ,batch.attention_mask.shape )
                UpperCAmelCase          =	batch.input_ids.tolist()[0]
                self.assertListEqual(A     ,A )






    @require_torch
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase          =	tokenizer(A     ,padding=A     ,return_tensors="""pt""" )
                self.assertIn("""input_ids"""     ,A )
                self.assertIn("""attention_mask"""     ,A )
                self.assertNotIn("""labels"""     ,A )
                self.assertNotIn("""decoder_attention_mask"""     ,A )






    @require_torch
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	[
              """Summary of the text.""",
              """Another summary.""",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase          =	tokenizer(text_target=A     ,max_length=32     ,padding="""max_length"""     ,return_tensors="""pt""" )
                self.assertEqual(32     ,targets["""input_ids"""].shape[1] )






    @require_torch
    def     _UpperCamelCase							(			self ):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase          =	tokenizer(
                    ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,padding=A     ,truncation=A     ,return_tensors="""pt""" )
                self.assertIsInstance(A     ,A )
                self.assertEqual(batch.input_ids.shape     ,(2, 5_122) )






    @require_torch
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	["""A long paragraph for summarization."""]
          UpperCAmelCase          =	[
              """Summary of the text.""",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase          =	tokenizer(A     ,return_tensors="""pt""" )
                UpperCAmelCase          =	tokenizer(text_target=A     ,return_tensors="""pt""" )
                UpperCAmelCase          =	inputs["""input_ids"""]
                UpperCAmelCase          =	targets["""input_ids"""]
                self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
                self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
                self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
                self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )






    @require_torch
    def     _UpperCamelCase							(			self ):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                UpperCAmelCase          =	["""Summary of the text.""", """Another summary."""]
                UpperCAmelCase          =	[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                UpperCAmelCase          =	tokenizer(A     ,padding=A )
                UpperCAmelCase          =	[[0] * len(A ) for x in encoded_output["""input_ids"""]]
                UpperCAmelCase          =	tokenizer.pad(A )
                self.assertSequenceEqual(outputs["""global_attention_mask"""]     ,A )






    def     _UpperCamelCase							(			self ):
          pass






    def     _UpperCamelCase							(			self ):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
                      UpperCAmelCase          =	self.rust_tokenizer_class.from_pretrained(A     ,**A )
                      UpperCAmelCase          =	self.tokenizer_class.from_pretrained(A     ,**A )
                      UpperCAmelCase          =	"""A, <mask> AllenNLP sentence."""
                      UpperCAmelCase          =	tokenizer_r.encode_plus(A     ,add_special_tokens=A     ,return_token_type_ids=A )
                      UpperCAmelCase          =	tokenizer_p.encode_plus(A     ,add_special_tokens=A     ,return_token_type_ids=A )
                      self.assertEqual(sum(tokens_r["""token_type_ids"""] )     ,sum(tokens_p["""token_type_ids"""] ) )
                      self.assertEqual(
                          sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] )     ,sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] )     ,)

                      UpperCAmelCase          =	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
                      UpperCAmelCase          =	tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
                      self.assertSequenceEqual(tokens_p["""input_ids"""]     ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
                      self.assertSequenceEqual(tokens_r["""input_ids"""]     ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

                      self.assertSequenceEqual(
                          A     ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
                      self.assertSequenceEqual(
                          A     ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )











"""simple docstring"""







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCamelCase					      =  {
    """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					      =  AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")

def _a			(		_snake_case				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	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 _a			(		_snake_case							):







      """simple docstring"""






      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(_snake_case				,	_snake_case							)

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

                  UpperCAmelCase          =	key.replace(F'''sequential.{sequential_layer}.'''				,	F'''layers.{int(_snake_case							)//3}.linear.'''							)
            elif re.match(_snake_case				,	_snake_case							):
                  UpperCAmelCase          =	int(re.match(_snake_case				,	_snake_case							).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			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	init_clap(_snake_case				,	enable_fusion=_snake_case							)

      clap_model.eval()
      UpperCAmelCase          =	clap_model.state_dict()
      UpperCAmelCase          =	rename_state_dict(_snake_case							)

      UpperCAmelCase          =	ClapConfig()
      UpperCAmelCase          =	enable_fusion
      UpperCAmelCase          =	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__":
  _UpperCamelCase					      =  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					      =  parser.parse_args()

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



