


"""simple docstring"""






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class     A__     (						unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= "ylacombe/bark-small"
				__lowerCAmelCase   :						Dict				= tempfile.mkdtemp()
				__lowerCAmelCase   :						Optional[int]				= "en_speaker_1"
				__lowerCAmelCase   :						Dict				= "This is a test string"
				__lowerCAmelCase   :						int				= "speaker_embeddings_path.json"
				__lowerCAmelCase   :						List[str]				= "speaker_embeddings"

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    **_SCREAMING_SNAKE_CASE:	List[str])       -> List[str]:

				"""simple docstring"""

				return AutoTokenizer.from_pretrained(self.checkpoint ,    **__A)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Tuple:

				"""simple docstring"""

				shutil.rmtree(self.tmpdirname)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= self.get_tokenizer()

				__lowerCAmelCase   :						Optional[Any]				= BarkProcessor(tokenizer=__A)

				processor.save_pretrained(self.tmpdirname)
				__lowerCAmelCase   :						Optional[Any]				= BarkProcessor.from_pretrained(self.tmpdirname)

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

	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= BarkProcessor.from_pretrained(
				    pretrained_processor_name_or_path=self.checkpoint ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,    )
				processor.save_pretrained(
				    self.tmpdirname ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,    speaker_embeddings_directory=self.speaker_embeddings_directory ,    )

				__lowerCAmelCase   :						Any				= self.get_tokenizer(bos_token="(BOS)" ,    eos_token="(EOS)")

				__lowerCAmelCase   :						Any				= BarkProcessor.from_pretrained(
				    self.tmpdirname ,    self.speaker_embeddings_dict_path ,    bos_token="(BOS)" ,    eos_token="(EOS)" ,    )

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= BarkProcessor.from_pretrained(
				    pretrained_processor_name_or_path=self.checkpoint ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,    )

				__lowerCAmelCase   :						Optional[int]				= 35
				__lowerCAmelCase   :						int				= 2
				__lowerCAmelCase   :						Union[str, Any]				= 8

				__lowerCAmelCase   :						Any				= {
				    "semantic_prompt": np.ones(__A),
				    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
				    "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
				}

				# test providing already loaded voice_preset
				__lowerCAmelCase   :						str				= processor(text=self.input_string ,    voice_preset=__A)

				__lowerCAmelCase   :						Tuple				= inputs["history_prompt"]
				for key in voice_preset:
							self.assertListEqual(voice_preset[key].tolist() ,    processed_voice_preset.get(__A ,    np.array([])).tolist())

				# test loading voice preset from npz file
				__lowerCAmelCase   :						Optional[int]				= os.path.join(self.tmpdirname ,    "file.npz")
				np.savez(__A ,    **__A)
				__lowerCAmelCase   :						List[Any]				= processor(text=self.input_string ,    voice_preset=__A)
				__lowerCAmelCase   :						List[Any]				= inputs["history_prompt"]

				for key in voice_preset:
							self.assertListEqual(voice_preset[key].tolist() ,    processed_voice_preset.get(__A ,    np.array([])).tolist())

				# test loading voice preset from the hub
				__lowerCAmelCase   :						Any				= processor(text=self.input_string ,    voice_preset=self.voice_preset)







	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.get_tokenizer()

				__lowerCAmelCase   :						Optional[Any]				= BarkProcessor(tokenizer=__A)

				__lowerCAmelCase   :						str				= processor(text=self.input_string)

				__lowerCAmelCase   :						int				= tokenizer(
				    self.input_string ,    padding="max_length" ,    max_length=256 ,    add_special_tokens=__A ,    return_attention_mask=__A ,    return_token_type_ids=__A ,    )

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




"""simple docstring"""






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= AlbertForPreTraining(__snake_case			)

			# Load weights from tf checkpoint
			load_tf_weights_in_albert(__snake_case      ,__snake_case      ,__snake_case			)

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


if __name__ == "__main__":
	__snake_case							:     Union[str, Any]   =					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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT 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							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)