





'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


_SCREAMING_SNAKE_CASE    =			get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')






@require_torch
class 							__lowercase				(					unittest.TestCase  ):







	'''simple docstring'''

	def   _UpperCAmelCase   (self       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							tempfile.mkdtemp()

				__lowercase   =							SpeechTaTokenizer(_lowerCamelCase       )
				tokenizer.save_pretrained(self.tmpdirname       )

				__lowercase   =							{
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 16000,
				    '''do_normalize''': False,
				    '''num_mel_bins''': 80,
				    '''hop_length''': 16,
				    '''win_length''': 64,
				    '''win_function''': '''hann_window''',
				    '''fmin''': 80,
				    '''fmax''': 7600,
				    '''mel_floor''': 1E-1_0,
				    '''reduction_factor''': 2,
				    '''return_attention_mask''': True,
				}

				__lowercase   =							os.path.join(self.tmpdirname   ,_lowerCamelCase       )
				with open(self.feature_extraction_file   ,'''w'''   ,encoding='''utf-8'''       ) as fp:
							fp.write(json.dumps(_lowerCamelCase       ) + '''\n'''       )


	def   _UpperCAmelCase   (self   ,**_lowerCamelCase       )							->							Optional[int]:







				'''simple docstring'''





				return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,**_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,**_lowerCamelCase       )							->							Any:







				'''simple docstring'''





				return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,**_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							List[Any]:







				'''simple docstring'''





				shutil.rmtree(self.tmpdirname       )


	def   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				__lowercase   =							self.get_tokenizer()
				__lowercase   =							self.get_feature_extractor()

				__lowercase   =							SpeechTaProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

				processor.save_pretrained(self.tmpdirname       )
				__lowercase   =							SpeechTaProcessor.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   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							SpeechTaProcessor(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=_lowerCamelCase   ,padding_value=1.0       )

				__lowercase   =							SpeechTaProcessor.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   _UpperCAmelCase   (self       )							->							Any:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechTaProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

				__lowercase   =							floats_list((3, 1000)       )

				__lowercase   =							feature_extractor(audio=_lowerCamelCase   ,return_tensors='''np'''       )
				__lowercase   =							processor(audio=_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   _UpperCAmelCase   (self       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechTaProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

				__lowercase   =							floats_list((3, 1000)       )

				__lowercase   =							feature_extractor(audio_target=_lowerCamelCase   ,return_tensors='''np'''       )
				__lowercase   =							processor(audio_target=_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   _UpperCAmelCase   (self       )							->							Dict:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechTaProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

				__lowercase   =							'''This is a test string'''

				__lowercase   =							processor(text=_lowerCamelCase       )
				__lowercase   =							tokenizer(_lowerCamelCase       )

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


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechTaProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

				__lowercase   =							'''This is a test string'''

				__lowercase   =							processor(text_target=_lowerCamelCase       )
				__lowercase   =							tokenizer(_lowerCamelCase       )

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


	def   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechTaProcessor(tokenizer=_lowerCamelCase   ,feature_extractor=_lowerCamelCase       )

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

				__lowercase   =							processor.batch_decode(_lowerCamelCase       )
				__lowercase   =							tokenizer.batch_decode(_lowerCamelCase       )

				self.assertListEqual(_lowerCamelCase   ,_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							Dict:







				'''simple docstring'''





				__lowercase   =							self.get_feature_extractor()
				__lowercase   =							self.get_tokenizer()

				__lowercase   =							SpeechTaProcessor(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'''   ,)












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE    =			{
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE    =			['''DeformableDetrFeatureExtractor''']
	_SCREAMING_SNAKE_CASE    =			['''DeformableDetrImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE    =			[
	    '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DeformableDetrForObjectDetection''',
	    '''DeformableDetrModel''',
	    '''DeformableDetrPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE    =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





