





'''simple docstring'''






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


__UpperCamelCase      :   str								=     get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_torch
@require_torchaudio
@require_sentencepiece
class  lowerCamelCase__				(						unittest.TestCase						):


	"""simple docstring"""





	def 				_lowerCamelCase				(					self			)       ->				Optional[Any]:
					_A :				List[str]				=  tempfile.mkdtemp()

					_A :				Tuple				=  ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
					_A :				Any				=  dict(zip(UpperCAmelCase__   ,     range(len(UpperCAmelCase__			)			)			)			)
					_A :				List[str]				=  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''']			)

					_A :				Optional[int]				=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
					tokenizer.save_pretrained(self.tmpdirname			)

					_A :				List[str]				=  {
					    '''feature_size''': 2_4,
					    '''num_mel_bins''': 2_4,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_6_0_0_0,
					    '''return_attention_mask''': False,
					    '''do_normalize''': True,
					}
					save_json(UpperCAmelCase__   ,     save_dir / FEATURE_EXTRACTOR_NAME			)




	def 				_lowerCamelCase				(					self   ,     **UpperCAmelCase__			)       ->				Union[str, Any]:
					return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,     **UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self   ,     **UpperCAmelCase__			)       ->				Any:
					return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,     **UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self			)       ->				Optional[int]:
					shutil.rmtree(self.tmpdirname			)




	def 				_lowerCamelCase				(					self			)       ->				Tuple:
					_A :				int				=  self.get_tokenizer()
					_A :				Any				=  self.get_feature_extractor()

					_A :				int				=  SpeechaTextProcessor(tokenizer=UpperCAmelCase__   ,     feature_extractor=UpperCAmelCase__			)

					processor.save_pretrained(self.tmpdirname			)
					_A :				str				=  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:
					_A :				Dict				=  SpeechaTextProcessor(
					    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 :				Union[str, Any]				=  self.get_feature_extractor(do_normalize=UpperCAmelCase__   ,     padding_value=1.0			)

					_A :				Optional[Any]				=  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]:
					_A :				List[str]				=  self.get_feature_extractor()
					_A :				Dict				=  self.get_tokenizer()

					_A :				Union[str, Any]				=  SpeechaTextProcessor(tokenizer=UpperCAmelCase__   ,     feature_extractor=UpperCAmelCase__			)

					_A :				Dict				=  floats_list((3, 1_0_0_0)			)

					_A :				List[Any]				=  feature_extractor(UpperCAmelCase__   ,     return_tensors='''np'''			)
					_A :				List[Any]				=  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			)       ->				Tuple:
					_A :				List[Any]				=  self.get_feature_extractor()
					_A :				List[str]				=  self.get_tokenizer()

					_A :				List[Any]				=  SpeechaTextProcessor(tokenizer=UpperCAmelCase__   ,     feature_extractor=UpperCAmelCase__			)

					_A :				List[str]				=  '''This is a test string'''

					_A :				Dict				=  processor(text=UpperCAmelCase__			)

					_A :				List[str]				=  tokenizer(UpperCAmelCase__			)

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




	def 				_lowerCamelCase				(					self			)       ->				Optional[int]:
					_A :				str				=  self.get_feature_extractor()
					_A :				Union[str, Any]				=  self.get_tokenizer()

					_A :				Tuple				=  SpeechaTextProcessor(tokenizer=UpperCAmelCase__   ,     feature_extractor=UpperCAmelCase__			)

					_A :				Union[str, Any]				=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					_A :				str				=  processor.batch_decode(UpperCAmelCase__			)
					_A :				int				=  tokenizer.batch_decode(UpperCAmelCase__			)

					self.assertListEqual(UpperCAmelCase__   ,     UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self			)       ->				Optional[Any]:
					_A :				Any				=  self.get_feature_extractor()
					_A :				List[str]				=  self.get_tokenizer()

					_A :				Optional[Any]				=  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'''   ,     )







'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   List[Any]								=     [
	    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTJForCausalLM''',
	    '''GPTJForQuestionAnswering''',
	    '''GPTJForSequenceClassification''',
	    '''GPTJModel''',
	    '''GPTJPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Optional[int]								=     [
	    '''TFGPTJForCausalLM''',
	    '''TFGPTJForQuestionAnswering''',
	    '''TFGPTJForSequenceClassification''',
	    '''TFGPTJModel''',
	    '''TFGPTJPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''FlaxGPTJForCausalLM''',
	    '''FlaxGPTJModel''',
	    '''FlaxGPTJPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
