


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


__magic_name__       					=      get_tests_dir('''fixtures/test_sentencepiece.model''')





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






						"""simple docstring"""




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							int      =       tempfile.mkdtemp()

							lowerCamelCase_			:							int      =       ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
							lowerCamelCase_			:							Tuple      =       dict(zip(__lowerCamelCase				,							range(len(__lowerCamelCase     )     )     )     )
							lowerCamelCase_			:							int      =       Path(self.tmpdirname     )
							save_json(__lowerCamelCase				,							save_dir / VOCAB_FILES_NAMES["vocab_file"]     )
							if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
								copyfile(__lowerCamelCase				,							save_dir / VOCAB_FILES_NAMES["spm_file"]     )

							lowerCamelCase_			:							str      =       SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
							tokenizer.save_pretrained(self.tmpdirname     )

							lowerCamelCase_			:							Optional[int]      =       {
							    "feature_size": 24,
							    "num_mel_bins": 24,
							    "padding_value": 0.0,
							    "sampling_rate": 1_6000,
							    "return_attention_mask": False,
							    "do_normalize": True,
							}
							save_json(__lowerCamelCase				,							save_dir / FEATURE_EXTRACTOR_NAME     )




						def 					_UpperCamelCase			(   self				,							**a_     ):
							return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,							**__lowerCamelCase     )




						def 					_UpperCamelCase			(   self				,							**a_     ):
							return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,							**__lowerCamelCase     )




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




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							int      =       self.get_tokenizer()
							lowerCamelCase_			:							Union[str, Any]      =       self.get_feature_extractor()

							lowerCamelCase_			:							Union[str, Any]      =       SpeechaTextProcessor(tokenizer=__lowerCamelCase				,							feature_extractor=__lowerCamelCase     )

							processor.save_pretrained(self.tmpdirname     )
							lowerCamelCase_			:							Tuple      =       SpeechaTextProcessor.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     ):
							lowerCamelCase_			:							str      =       SpeechaTextProcessor(
							    tokenizer=self.get_tokenizer()				,							feature_extractor=self.get_feature_extractor()     )
							processor.save_pretrained(self.tmpdirname     )

							lowerCamelCase_			:							List[str]      =       self.get_tokenizer(bos_token="(BOS)"				,							eos_token="(EOS)"     )
							lowerCamelCase_			:							Dict      =       self.get_feature_extractor(do_normalize=__lowerCamelCase				,							padding_value=1.0     )

							lowerCamelCase_			:							List[str]      =       SpeechaTextProcessor.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     ):
							lowerCamelCase_			:							Union[str, Any]      =       self.get_feature_extractor()
							lowerCamelCase_			:							List[Any]      =       self.get_tokenizer()

							lowerCamelCase_			:							List[Any]      =       SpeechaTextProcessor(tokenizer=__lowerCamelCase				,							feature_extractor=__lowerCamelCase     )

							lowerCamelCase_			:							Optional[Any]      =       floats_list((3, 1000)     )

							lowerCamelCase_			:							Tuple      =       feature_extractor(__lowerCamelCase				,							return_tensors="np"     )
							lowerCamelCase_			:							Optional[int]      =       processor(__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     ):
							lowerCamelCase_			:							int      =       self.get_feature_extractor()
							lowerCamelCase_			:							List[Any]      =       self.get_tokenizer()

							lowerCamelCase_			:							Optional[int]      =       SpeechaTextProcessor(tokenizer=__lowerCamelCase				,							feature_extractor=__lowerCamelCase     )

							lowerCamelCase_			:							int      =       "This is a test string"

							lowerCamelCase_			:							Tuple      =       processor(text=__lowerCamelCase     )

							lowerCamelCase_			:							Optional[int]      =       tokenizer(__lowerCamelCase     )

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




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       self.get_feature_extractor()
							lowerCamelCase_			:							int      =       self.get_tokenizer()

							lowerCamelCase_			:							str      =       SpeechaTextProcessor(tokenizer=__lowerCamelCase				,							feature_extractor=__lowerCamelCase     )

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

							lowerCamelCase_			:							str      =       processor.batch_decode(__lowerCamelCase     )
							lowerCamelCase_			:							Optional[int]      =       tokenizer.batch_decode(__lowerCamelCase     )

							self.assertListEqual(__lowerCamelCase				,							__lowerCamelCase     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Any      =       self.get_feature_extractor()
							lowerCamelCase_			:							Optional[Any]      =       self.get_tokenizer()

							lowerCamelCase_			:							List[Any]      =       SpeechaTextProcessor(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"				,							)





from typing import TYPE_CHECKING

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


__magic_name__       					=      {'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''LevitFeatureExtractor''']
							__magic_name__       					=      ['''LevitImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LevitForImageClassification''',
							    '''LevitForImageClassificationWithTeacher''',
							    '''LevitModel''',
							    '''LevitPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_levit import LevitFeatureExtractor
														from .image_processing_levit import LevitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_levit import (
														    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LevitForImageClassification,
														    LevitForImageClassificationWithTeacher,
														    LevitModel,
														    LevitPreTrainedModel,
														)
else:
							import sys

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

