






'''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


__lowerCAmelCase							=get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")


@require_torch
class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   tempfile.mkdtemp()

										a_									=   SpeechTaTokenizer(UpperCAmelCase__     )
										tokenizer.save_pretrained(self.tmpdirname     )

										a_									=   {
										    'feature_size': 1,
										    'padding_value': 0.0,
										    'sampling_rate': 1_6000,
										    'do_normalize': False,
										    'num_mel_bins': 80,
										    'hop_length': 16,
										    'win_length': 64,
										    'win_function': 'hann_window',
										    'fmin': 80,
										    'fmax': 7600,
										    'mel_floor': 1e-10,
										    'reduction_factor': 2,
										    'return_attention_mask': True,
										}

										a_									=   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  __SCREAMING_SNAKE_CASE (     self   , **UpperCAmelCase__     )      ->	Union[str, Any]:
										return SpeechTaTokenizer.from_pretrained(self.tmpdirname   , **UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , **UpperCAmelCase__     )      ->	Optional[int]:
										return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   , **UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:
										shutil.rmtree(self.tmpdirname     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:
										a_									=   self.get_tokenizer()
										a_									=   self.get_feature_extractor()

										a_									=   SpeechTaProcessor(tokenizer=UpperCAmelCase__   , feature_extractor=UpperCAmelCase__     )

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

										a_									=   self.get_tokenizer(bos_token='(BOS)'   , eos_token='(EOS)'     )
										a_									=   self.get_feature_extractor(do_normalize=UpperCAmelCase__   , padding_value=1.0     )

										a_									=   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  __SCREAMING_SNAKE_CASE (     self     )      ->	str:
										a_									=   self.get_feature_extractor()
										a_									=   self.get_tokenizer()

										a_									=   SpeechTaProcessor(tokenizer=UpperCAmelCase__   , feature_extractor=UpperCAmelCase__     )

										a_									=   floats_list((3, 1000)     )

										a_									=   feature_extractor(audio=UpperCAmelCase__   , return_tensors='np'     )
										a_									=   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  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[int]:
										a_									=   self.get_feature_extractor()
										a_									=   self.get_tokenizer()

										a_									=   SpeechTaProcessor(tokenizer=UpperCAmelCase__   , feature_extractor=UpperCAmelCase__     )

										a_									=   floats_list((3, 1000)     )

										a_									=   feature_extractor(audio_target=UpperCAmelCase__   , return_tensors='np'     )
										a_									=   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  __SCREAMING_SNAKE_CASE (     self     )      ->	str:
										a_									=   self.get_feature_extractor()
										a_									=   self.get_tokenizer()

										a_									=   SpeechTaProcessor(tokenizer=UpperCAmelCase__   , feature_extractor=UpperCAmelCase__     )

										a_									=   'This is a test string'

										a_									=   processor(text=UpperCAmelCase__     )
										a_									=   tokenizer(UpperCAmelCase__     )

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




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										a_									=   self.get_feature_extractor()
										a_									=   self.get_tokenizer()

										a_									=   SpeechTaProcessor(tokenizer=UpperCAmelCase__   , feature_extractor=UpperCAmelCase__     )

										a_									=   'This is a test string'

										a_									=   processor(text_target=UpperCAmelCase__     )
										a_									=   tokenizer(UpperCAmelCase__     )

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




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										a_									=   self.get_feature_extractor()
										a_									=   self.get_tokenizer()

										a_									=   SpeechTaProcessor(tokenizer=UpperCAmelCase__   , feature_extractor=UpperCAmelCase__     )

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

										a_									=   processor.batch_decode(UpperCAmelCase__     )
										a_									=   tokenizer.batch_decode(UpperCAmelCase__     )

										self.assertListEqual(UpperCAmelCase__   , UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:
										a_									=   self.get_feature_extractor()
										a_									=   self.get_tokenizer()

										a_									=   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'   , )









'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase							={"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RegNetForImageClassification",
						    "RegNetModel",
						    "RegNetPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRegNetForImageClassification",
						    "TFRegNetModel",
						    "TFRegNetPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "FlaxRegNetForImageClassification",
						    "FlaxRegNetModel",
						    "FlaxRegNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_regnet import (
												    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RegNetForImageClassification,
												    RegNetModel,
												    RegNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_regnet import (
												    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRegNetForImageClassification,
												    TFRegNetModel,
												    TFRegNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_regnet import (
												    FlaxRegNetForImageClassification,
												    FlaxRegNetModel,
												    FlaxRegNetPreTrainedModel,
												)


else:
						import sys

						__lowerCAmelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure)

