




'''simple docstring'''







import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
						pass

if is_speech_available():
						from transformers import EncodecFeatureExtractor, MusicgenProcessor


_lowerCamelCase				:		Optional[Any]				      =    random.Random()






def 						_lowercase						(      lowercase_		,				lowercase_=1.0		,				lowercase_=None		,				lowercase_=None    ):



		'''simple docstring'''







		if rng is None:
				_snake_case      			=			global_rng

		_snake_case      			=			[]
		for batch_idx in range(shape[0]    ):
				values.append([]    )
				for _ in range(shape[1]    ):
						values[-1].append(rng.random() * scale    )

		return values





@require_torch
@require_sentencepiece
class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			"facebook/musicgen-small"
							_snake_case      			=			tempfile.mkdtemp()






					def 							__SCREAMING_SNAKE_CASE      (       self,    **__UpperCamelCase					):
							return TaTokenizer.from_pretrained(self.checkpoint,    **_lowerCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    **__UpperCamelCase					):
							return EncodecFeatureExtractor.from_pretrained(self.checkpoint,    **_lowerCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							shutil.rmtree(self.tmpdirname					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.get_tokenizer()
							_snake_case      			=			self.get_feature_extractor()

							_snake_case      			=			MusicgenProcessor(tokenizer=_lowerCamelCase,    feature_extractor=_lowerCamelCase					)

							processor.save_pretrained(self.tmpdirname					)
							_snake_case      			=			MusicgenProcessor.from_pretrained(self.tmpdirname					)

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

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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			MusicgenProcessor(tokenizer=self.get_tokenizer(),    feature_extractor=self.get_feature_extractor()					)
							processor.save_pretrained(self.tmpdirname					)

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

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

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

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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.get_feature_extractor()
							_snake_case      			=			self.get_tokenizer()

							_snake_case      			=			MusicgenProcessor(tokenizer=_lowerCamelCase,    feature_extractor=_lowerCamelCase					)

							_snake_case      			=			floats_list((3, 1_000)					)

							_snake_case      			=			feature_extractor(_lowerCamelCase,    return_tensors="np"					)
							_snake_case      			=			processor(_lowerCamelCase,    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 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.get_feature_extractor()
							_snake_case      			=			self.get_tokenizer()

							_snake_case      			=			MusicgenProcessor(tokenizer=_lowerCamelCase,    feature_extractor=_lowerCamelCase					)

							_snake_case      			=			"This is a test string"

							_snake_case      			=			processor(text=_lowerCamelCase					)

							_snake_case      			=			tokenizer(_lowerCamelCase					)

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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.get_feature_extractor()
							_snake_case      			=			self.get_tokenizer()

							_snake_case      			=			MusicgenProcessor(tokenizer=_lowerCamelCase,    feature_extractor=_lowerCamelCase					)

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

							_snake_case      			=			processor.batch_decode(sequences=_lowerCamelCase					)
							_snake_case      			=			tokenizer.batch_decode(_lowerCamelCase					)

							self.assertListEqual(_lowerCamelCase,    _lowerCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.get_feature_extractor()
							_snake_case      			=			self.get_tokenizer()

							_snake_case      			=			MusicgenProcessor(tokenizer=_lowerCamelCase,    feature_extractor=_lowerCamelCase					)

							self.assertListEqual(
							    processor.model_input_names,    feature_extractor.model_input_names,    msg="`processor` and `feature_extractor` model input names do not match",    )






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.get_feature_extractor(padding_side="left"					)
							_snake_case      			=			self.get_tokenizer()

							_snake_case      			=			MusicgenProcessor(tokenizer=_lowerCamelCase,    feature_extractor=_lowerCamelCase					)

							_snake_case      			=			[floats_list((1, x)					)[0] for x in range(5,    20,    5					)]
							_snake_case      			=			processor(_lowerCamelCase					).padding_mask

							_snake_case      			=			np.asarray(floats_list((3, 20)					)					)[:, None, :]
							_snake_case      			=			processor.batch_decode(_lowerCamelCase,    padding_mask=_lowerCamelCase					)

							self.assertIsInstance(_lowerCamelCase,    _lowerCamelCase					)

							for audio in decoded_audios:
									self.assertIsInstance(_lowerCamelCase,    np.ndarray					)

							self.assertTrue(decoded_audios[0].shape == (1, 10)					)
							self.assertTrue(decoded_audios[1].shape == (1, 15)					)
							self.assertTrue(decoded_audios[2].shape == (1, 20)					)













"""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    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







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

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

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


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




