




"""simple docstring"""




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a_            =   {
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_            =   [
			    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ConvNextV2ForImageClassification',
			    'ConvNextV2Model',
			    'ConvNextV2PreTrainedModel',
			    'ConvNextV2Backbone',
			]


if TYPE_CHECKING:
			from .configuration_convnextva import (
			    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ConvNextVaConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnextva import (
						    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextVaBackbone,
						    ConvNextVaForImageClassification,
						    ConvNextVaModel,
						    ConvNextVaPreTrainedModel,
						)

else:
			import sys

			a_            =   _LazyModule(__name__, globals()['__file__'], _import_structure)









"""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


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

@require_torch
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   Dict:
					__lowercase	:       Union[str, Any]				=   tempfile.mkdtemp()

					__lowercase	:       List[str]				=   SpeechTaTokenizer(UpperCamelCase_   )
					tokenizer.save_pretrained(self.tmpdirname   )

					__lowercase	:       Dict				=   {
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1E-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					__lowercase	:       List[Any]				=   os.path.join(self.tmpdirname							,		UpperCamelCase_   )
					with open(self.feature_extraction_file							,		'''w'''							,		encoding='''utf-8'''   ) as fp:
								fp.write(json.dumps(UpperCamelCase_   ) + '''\n'''   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Union[str, Any]:
					return SpeechTaTokenizer.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Optional[Any]:
					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					shutil.rmtree(self.tmpdirname   )
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					__lowercase	:       List[str]				=   self.get_tokenizer()
					__lowercase	:       int				=   self.get_feature_extractor()

					__lowercase	:       Any				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					processor.save_pretrained(self.tmpdirname   )
					__lowercase	:       List[Any]				=   SpeechTaProcessor.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   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   SpeechTaProcessor(tokenizer=self.get_tokenizer()							,		feature_extractor=self.get_feature_extractor()   )
					processor.save_pretrained(self.tmpdirname   )

					__lowercase	:       str				=   self.get_tokenizer(bos_token='''(BOS)'''							,		eos_token='''(EOS)'''   )
					__lowercase	:       Dict				=   self.get_feature_extractor(do_normalize=UpperCamelCase_							,		padding_value=1.0   )

					__lowercase	:       Tuple				=   SpeechTaProcessor.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   )		->   List[str]:
					__lowercase	:       Optional[Any]				=   self.get_feature_extractor()
					__lowercase	:       Optional[Any]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       int				=   floats_list((3, 10_00)   )

					__lowercase	:       str				=   feature_extractor(audio=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       Dict				=   processor(audio=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   )		->   List[Any]:
					__lowercase	:       str				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Optional[int]				=   floats_list((3, 10_00)   )

					__lowercase	:       List[str]				=   feature_extractor(audio_target=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       List[str]				=   processor(audio_target=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   )		->   Any:
					__lowercase	:       List[Any]				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

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

					__lowercase	:       Any				=   processor(text=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	:       int				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       List[Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   '''This is a test string'''

					__lowercase	:       Tuple				=   processor(text_target=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       int				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

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

					__lowercase	:       str				=   processor.batch_decode(UpperCamelCase_   )
					__lowercase	:       int				=   tokenizer.batch_decode(UpperCamelCase_   )

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

					__lowercase	:       Optional[int]				=   SpeechTaProcessor(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'''							,		)



