



import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


A__       =			get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")





@require_torch
class    __lowerCAmelCase					(	unittest.TestCase			):




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       tempfile.mkdtemp()

									_lowerCAmelCase         =       SpeechTaTokenizer(_snake_case						)
									tokenizer.save_pretrained(self.tmpdirname						)

									_lowerCAmelCase         =       {
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16000,
									    """do_normalize""": False,
									    """num_mel_bins""": 80,
									    """hop_length""": 16,
									    """win_length""": 64,
									    """win_function""": """hann_window""",
									    """fmin""": 80,
									    """fmax""": 7600,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

									_lowerCAmelCase         =       os.path.join(self.tmpdirname		,							_snake_case						)
									with open(self.feature_extraction_file		,							"""w"""		,							encoding="""utf-8"""						) as fp:
														fp.write(json.dumps(_snake_case						) + """\n"""						)




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return SpeechTaTokenizer.from_pretrained(self.tmpdirname		,							**_snake_case						)




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname		,							**_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									shutil.rmtree(self.tmpdirname						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_tokenizer()
									_lowerCAmelCase         =       self.get_feature_extractor()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									processor.save_pretrained(self.tmpdirname						)
									_lowerCAmelCase         =       SpeechTaProcessor.from_pretrained(self.tmpdirname						)

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

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=self.get_tokenizer()		,							feature_extractor=self.get_feature_extractor()						)
									processor.save_pretrained(self.tmpdirname						)

									_lowerCAmelCase         =       self.get_tokenizer(bos_token="""(BOS)"""		,							eos_token="""(EOS)"""						)
									_lowerCAmelCase         =       self.get_feature_extractor(do_normalize=_snake_case		,							padding_value=1.0						)

									_lowerCAmelCase         =       SpeechTaProcessor.from_pretrained(
									    self.tmpdirname		,							bos_token="""(BOS)"""		,							eos_token="""(EOS)"""		,							do_normalize=_snake_case		,							padding_value=1.0						)

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

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       floats_list((3, 1000)						)

									_lowerCAmelCase         =       feature_extractor(audio=_snake_case		,							return_tensors="""np"""						)
									_lowerCAmelCase         =       processor(audio=_snake_case		,							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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       floats_list((3, 1000)						)

									_lowerCAmelCase         =       feature_extractor(audio_target=_snake_case		,							return_tensors="""np"""						)
									_lowerCAmelCase         =       processor(audio_target=_snake_case		,							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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       """This is a test string"""

									_lowerCAmelCase         =       processor(text=_snake_case						)
									_lowerCAmelCase         =       tokenizer(_snake_case						)

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       """This is a test string"""

									_lowerCAmelCase         =       processor(text_target=_snake_case						)
									_lowerCAmelCase         =       tokenizer(_snake_case						)

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									_lowerCAmelCase         =       processor.batch_decode(_snake_case						)
									_lowerCAmelCase         =       tokenizer.batch_decode(_snake_case						)

									self.assertListEqual(_snake_case		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									self.assertListEqual(
									    processor.model_input_names		,							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 RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


if __name__ == "__main__":
   __snake_case												=							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.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)