


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


A :	int												=     get_tests_dir('fixtures/test_sentencepiece.model')



@require_torch
@require_torchaudio
@require_sentencepiece
class 							A					( unittest.TestCase							):
					'''simple docstring'''






					def 						lowerCamelCase__  (self					:		Dict	)		->     Dict:




							"""simple docstring"""


							lowercase__      =	tempfile.mkdtemp()

							lowercase__      =	["""<s>""", """<pad>""", """</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est"""]
							lowercase__      =	dict(zip(_UpperCAmelCase   ,      range(len(_UpperCAmelCase	)	)	)	)
							lowercase__      =	Path(self.tmpdirname	)
							save_json(_UpperCAmelCase   ,      save_dir / VOCAB_FILES_NAMES["""vocab_file"""]	)
							if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
									copyfile(_UpperCAmelCase   ,      save_dir / VOCAB_FILES_NAMES["""spm_file"""]	)

							lowercase__      =	SpeechaTextTokenizer.from_pretrained(self.tmpdirname	)
							tokenizer.save_pretrained(self.tmpdirname	)

							lowercase__      =	{
							    """feature_size""": 24,
							    """num_mel_bins""": 24,
							    """padding_value""": 0.0,
							    """sampling_rate""": 1_6000,
							    """return_attention_mask""": False,
							    """do_normalize""": True,
							}
							save_json(_UpperCAmelCase   ,      save_dir / FEATURE_EXTRACTOR_NAME	)

					def 						lowerCamelCase__  (self					:		str   ,      **_UpperCAmelCase					:		Tuple	)		->     Any:




							"""simple docstring"""


							return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,      **_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		Dict   ,      **_UpperCAmelCase					:		Optional[int]	)		->     Tuple:




							"""simple docstring"""


							return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,      **_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		Dict	)		->     List[Any]:




							"""simple docstring"""


							shutil.rmtree(self.tmpdirname	)

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




							"""simple docstring"""


							lowercase__      =	self.get_tokenizer()
							lowercase__      =	self.get_feature_extractor()

							lowercase__      =	SpeechaTextProcessor(tokenizer=_UpperCAmelCase   ,      feature_extractor=_UpperCAmelCase	)

							processor.save_pretrained(self.tmpdirname	)
							lowercase__      =	SpeechaTextProcessor.from_pretrained(self.tmpdirname	)

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

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

					def 						lowerCamelCase__  (self					:		Any	)		->     Dict:




							"""simple docstring"""


							lowercase__      =	SpeechaTextProcessor(
							    tokenizer=self.get_tokenizer()   ,      feature_extractor=self.get_feature_extractor()	)
							processor.save_pretrained(self.tmpdirname	)

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

							lowercase__      =	SpeechaTextProcessor.from_pretrained(
							    self.tmpdirname   ,      bos_token="""(BOS)"""   ,      eos_token="""(EOS)"""   ,      do_normalize=_UpperCAmelCase   ,      padding_value=1.0	)

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

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

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




							"""simple docstring"""


							lowercase__      =	self.get_feature_extractor()
							lowercase__      =	self.get_tokenizer()

							lowercase__      =	SpeechaTextProcessor(tokenizer=_UpperCAmelCase   ,      feature_extractor=_UpperCAmelCase	)

							lowercase__      =	floats_list((3, 1000)	)

							lowercase__      =	feature_extractor(_UpperCAmelCase   ,      return_tensors="""np"""	)
							lowercase__      =	processor(_UpperCAmelCase   ,      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 						lowerCamelCase__  (self					:		str	)		->     str:




							"""simple docstring"""


							lowercase__      =	self.get_feature_extractor()
							lowercase__      =	self.get_tokenizer()

							lowercase__      =	SpeechaTextProcessor(tokenizer=_UpperCAmelCase   ,      feature_extractor=_UpperCAmelCase	)

							lowercase__      =	"""This is a test string"""

							lowercase__      =	processor(text=_UpperCAmelCase	)

							lowercase__      =	tokenizer(_UpperCAmelCase	)

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

					def 						lowerCamelCase__  (self					:		Tuple	)		->     Dict:




							"""simple docstring"""


							lowercase__      =	self.get_feature_extractor()
							lowercase__      =	self.get_tokenizer()

							lowercase__      =	SpeechaTextProcessor(tokenizer=_UpperCAmelCase   ,      feature_extractor=_UpperCAmelCase	)

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

							lowercase__      =	processor.batch_decode(_UpperCAmelCase	)
							lowercase__      =	tokenizer.batch_decode(_UpperCAmelCase	)

							self.assertListEqual(_UpperCAmelCase   ,      _UpperCAmelCase	)


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




							"""simple docstring"""


							lowercase__      =	self.get_feature_extractor()
							lowercase__      =	self.get_tokenizer()

							lowercase__      =	SpeechaTextProcessor(tokenizer=_UpperCAmelCase   ,      feature_extractor=_UpperCAmelCase	)

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A :	int												=     {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	List[Any]												=     [
	    'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RwkvForCausalLM',
	    'RwkvModel',
	    'RwkvPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_rwkv import (
		    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RwkvForCausalLM,
		    RwkvModel,
		    RwkvPreTrainedModel,
		)
else:
	import sys

	A :	Optional[int]												=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

