
"""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


lowerCamelCase__     =						get_tests_dir("""fixtures/test_sentencepiece.model""")




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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		List[str]		     = tempfile.mkdtemp()

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

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

			__lowerCAmelCase     :		List[str]		     = {
			    '''feature_size''': 24,
			    '''num_mel_bins''': 24,
			    '''padding_value''': 0.0,
			    '''sampling_rate''': 1_60_00,
			    '''return_attention_mask''': False,
			    '''do_normalize''': True,
			}
			save_json(UpperCAmelCase__			,						save_dir / FEATURE_EXTRACTOR_NAME					)

		def 				__lowerCamelCase				( self			,						**_SCREAMING_SNAKE_CASE					):
			return SpeechaTextTokenizer.from_pretrained(self.tmpdirname			,						**UpperCAmelCase__					)

		def 				__lowerCamelCase				( self			,						**_SCREAMING_SNAKE_CASE					):
			return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname			,						**UpperCAmelCase__					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		int		     = self.get_tokenizer()
			__lowerCAmelCase     :		Any		     = self.get_feature_extractor()

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

			processor.save_pretrained(self.tmpdirname					)
			__lowerCAmelCase     :		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					):
			__lowerCAmelCase     :		Dict		     = SpeechaTextProcessor(
			    tokenizer=self.get_tokenizer()			,						feature_extractor=self.get_feature_extractor()					)
			processor.save_pretrained(self.tmpdirname					)

			__lowerCAmelCase     :		Tuple		     = self.get_tokenizer(bos_token='(BOS)'			,						eos_token='(EOS)'					)
			__lowerCAmelCase     :		Union[str, Any]		     = self.get_feature_extractor(do_normalize=UpperCAmelCase__			,						padding_value=1.0					)

			__lowerCAmelCase     :		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					):
			__lowerCAmelCase     :		List[str]		     = self.get_feature_extractor()
			__lowerCAmelCase     :		Dict		     = self.get_tokenizer()

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

			__lowerCAmelCase     :		Dict		     = floats_list((3, 10_00)					)

			__lowerCAmelCase     :		List[Any]		     = feature_extractor(UpperCAmelCase__			,						return_tensors='np'					)
			__lowerCAmelCase     :		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					):
			__lowerCAmelCase     :		List[Any]		     = self.get_feature_extractor()
			__lowerCAmelCase     :		List[str]		     = self.get_tokenizer()

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

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

			__lowerCAmelCase     :		Dict		     = processor(text=UpperCAmelCase__					)

			__lowerCAmelCase     :		List[str]		     = tokenizer(UpperCAmelCase__					)

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		str		     = self.get_feature_extractor()
			__lowerCAmelCase     :		Union[str, Any]		     = self.get_tokenizer()

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

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

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

			self.assertListEqual(UpperCAmelCase__			,						UpperCAmelCase__					)







		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = self.get_feature_extractor()
			__lowerCAmelCase     :		List[str]		     = self.get_tokenizer()

			__lowerCAmelCase     :		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__)
