




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


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





@require_torch
@require_torchaudio
@require_sentencepiece
class        lowerCamelCase__(   unittest.TestCase):
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             __lowerCamelCase  =		tempfile.mkdtemp()

             __lowerCamelCase  =		["""<s>""", """<pad>""", """</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est"""]
             __lowerCamelCase  =		dict(zip(UpperCamelCase_       ,			range(len(UpperCamelCase_  )  )  )  )
             __lowerCamelCase  =		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  =		SpeechaTextTokenizer.from_pretrained(self.tmpdirname  )
             tokenizer.save_pretrained(self.tmpdirname  )

             __lowerCamelCase  =		{
                 """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:					Tuple       ,			**UpperCamelCase_:					List[str]  ):
             return SpeechaTextTokenizer.from_pretrained(self.tmpdirname       ,			**UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					int       ,			**UpperCamelCase_:					int  ):
             return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname       ,			**UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					int  ):
             shutil.rmtree(self.tmpdirname  )
      def      lowerCAmelCase__		(							self:					Optional[int]  ):
             __lowerCamelCase  =		self.get_tokenizer()
             __lowerCamelCase  =		self.get_feature_extractor()

             __lowerCamelCase  =		SpeechaTextProcessor(tokenizer=UpperCamelCase_       ,			feature_extractor=UpperCamelCase_  )

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

             __lowerCamelCase  =		self.get_tokenizer(bos_token="""(BOS)"""       ,			eos_token="""(EOS)"""  )
             __lowerCamelCase  =		self.get_feature_extractor(do_normalize=UpperCamelCase_       ,			padding_value=1.0  )

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

             __lowerCamelCase  =		SpeechaTextProcessor(tokenizer=UpperCamelCase_       ,			feature_extractor=UpperCamelCase_  )

             __lowerCamelCase  =		floats_list((3, 10_00)  )

             __lowerCamelCase  =		feature_extractor(UpperCamelCase_       ,			return_tensors="""np"""  )
             __lowerCamelCase  =		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:					Optional[int]  ):
             __lowerCamelCase  =		self.get_feature_extractor()
             __lowerCamelCase  =		self.get_tokenizer()

             __lowerCamelCase  =		SpeechaTextProcessor(tokenizer=UpperCamelCase_       ,			feature_extractor=UpperCamelCase_  )

             __lowerCamelCase  =		"""This is a test string"""

             __lowerCamelCase  =		processor(text=UpperCamelCase_  )

             __lowerCamelCase  =		tokenizer(UpperCamelCase_  )

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]       ,			encoded_processor[key]  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		self.get_feature_extractor()
             __lowerCamelCase  =		self.get_tokenizer()

             __lowerCamelCase  =		SpeechaTextProcessor(tokenizer=UpperCamelCase_       ,			feature_extractor=UpperCamelCase_  )

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

             __lowerCamelCase  =		processor.batch_decode(UpperCamelCase_  )
             __lowerCamelCase  =		tokenizer.batch_decode(UpperCamelCase_  )

             self.assertListEqual(UpperCamelCase_       ,			UpperCamelCase_  )




      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             __lowerCamelCase  =		self.get_feature_extractor()
             __lowerCamelCase  =		self.get_tokenizer()

             __lowerCamelCase  =		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"""       ,			)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_   							=      {'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_   							=      [
           'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'DinatForImageClassification',
           'DinatModel',
           'DinatPreTrainedModel',
           'DinatBackbone',
       ]

if TYPE_CHECKING:
       from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )

else:
       import sys

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

