
'''simple docstring'''







import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list







@require_torchaudio
@require_sentencepiece
class UpperCAmelCase__      (					unittest.TestCase      ):





  """simple docstring"""

  def     __lowercase						(						self :	List[Any]			):


     '''simple docstring'''






     _a							:      List[Any]     =  'laion/clap-htsat-unfused'
     _a							:      Dict     =  tempfile.mkdtemp()


  def     __lowercase						(						self :	Dict				,**_a :	int			):


     '''simple docstring'''






     return RobertaTokenizer.from_pretrained(self.checkpoint				,**__a			)


  def     __lowercase						(						self :	Tuple				,**_a :	Optional[Any]			):


     '''simple docstring'''






     return ClapFeatureExtractor.from_pretrained(self.checkpoint				,**__a			)


  def     __lowercase						(						self :	List[str]			):


     '''simple docstring'''






     shutil.rmtree(self.tmpdirname			)


  def     __lowercase						(						self :	Any			):


     '''simple docstring'''






     _a							:      Any     =  self.get_tokenizer()
     _a							:      Optional[int]     =  self.get_feature_extractor()

     _a							:      int     =  ClapProcessor(tokenizer=__a				,feature_extractor=__a			)

     processor.save_pretrained(self.tmpdirname			)
     _a							:      Tuple     =  ClapProcessor.from_pretrained(self.tmpdirname			)

     self.assertEqual(processor.tokenizer.get_vocab()				,tokenizer.get_vocab()			)
     self.assertIsInstance(processor.tokenizer				,__a			)

     self.assertEqual(processor.feature_extractor.to_json_string()				,feature_extractor.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor				,__a			)


  def     __lowercase						(						self :	int			):


     '''simple docstring'''






     _a							:      Union[str, Any]     =  ClapProcessor(tokenizer=self.get_tokenizer()				,feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

     _a							:      Tuple     =  self.get_tokenizer(bos_token='(BOS)'				,eos_token='(EOS)'			)
     _a							:      List[str]     =  self.get_feature_extractor(do_normalize=__a				,padding_value=1.0			)

     _a							:      Union[str, Any]     =  ClapProcessor.from_pretrained(
         self.tmpdirname				,bos_token='(BOS)'				,eos_token='(EOS)'				,do_normalize=__a				,padding_value=1.0			)

     self.assertEqual(processor.tokenizer.get_vocab()				,tokenizer_add_kwargs.get_vocab()			)
     self.assertIsInstance(processor.tokenizer				,__a			)

     self.assertEqual(processor.feature_extractor.to_json_string()				,feature_extractor_add_kwargs.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor				,__a			)


  def     __lowercase						(						self :	List[str]			):


     '''simple docstring'''






     _a							:      Optional[Any]     =  self.get_feature_extractor()
     _a							:      int     =  self.get_tokenizer()

     _a							:      Optional[int]     =  ClapProcessor(tokenizer=__a				,feature_extractor=__a			)

     _a							:      List[Any]     =  floats_list((3, 1000)			)

     _a							:      str     =  feature_extractor(__a				,return_tensors='np'			)
     _a							:      Optional[Any]     =  processor(audios=__a				,return_tensors='np'			)

     for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,input_processor[key].sum()				,delta=1E-2			)


  def     __lowercase						(						self :	str			):


     '''simple docstring'''






     _a							:      List[Any]     =  self.get_feature_extractor()
     _a							:      List[str]     =  self.get_tokenizer()

     _a							:      Optional[int]     =  ClapProcessor(tokenizer=__a				,feature_extractor=__a			)

     _a							:      List[Any]     =  'This is a test string'

     _a							:      Optional[int]     =  processor(text=__a			)

     _a							:      List[Any]     =  tokenizer(__a			)

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]				,encoded_processor[key]			)


  def     __lowercase						(						self :	Dict			):


     '''simple docstring'''






     _a							:      Dict     =  self.get_feature_extractor()
     _a							:      Dict     =  self.get_tokenizer()

     _a							:      Any     =  ClapProcessor(tokenizer=__a				,feature_extractor=__a			)

     _a							:      Optional[int]     =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     _a							:      Any     =  processor.batch_decode(__a			)
     _a							:      Union[str, Any]     =  tokenizer.batch_decode(__a			)

     self.assertListEqual(__a				,__a			)


  def     __lowercase						(						self :	str			):


     '''simple docstring'''






     _a							:      Tuple     =  self.get_feature_extractor()
     _a							:      Optional[int]     =  self.get_tokenizer()

     _a							:      Optional[Any]     =  ClapProcessor(tokenizer=__a				,feature_extractor=__a			)

     self.assertListEqual(
         processor.model_input_names[2:]				,feature_extractor.model_input_names				,msg='`processor` and `feature_extractor` model input names do not match'				,)




'''simple docstring'''


import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    lowerCAmelCase       (		UpperCamelCase__					: Optional[int]     ,       UpperCamelCase__					: List[str]     ,       UpperCamelCase__					: Optional[Any]							):


	"""simple docstring"""
	# Initialise PyTorch model
	__UpperCAmelCase      					=      BertConfig.from_json_file(UpperCamelCase__							)
	print(f"""Building PyTorch model from configuration: {config}"""							)
	__UpperCAmelCase      					=      BertForPreTraining(UpperCamelCase__							)

	# Load weights from tf checkpoint
	load_tf_weights_in_bert(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""							)
	torch.save(model.state_dict()     ,       UpperCamelCase__							)


if __name__ == "__main__":
					__lowerCAmelCase						:							Tuple    							=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
					)
					parser.add_argument(
					    "--bert_config_file",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        "The config json file corresponding to the pre-trained BERT model. \n"
					        "This specifies the model architecture."
					    ),
					)
					parser.add_argument(
					    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					__lowerCAmelCase						:							Optional[Any]    							=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
