



'''simple docstring'''






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


A_				:					Optional[int]							=      get_tests_dir("""fixtures/test_sentencepiece.model""")



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


 """simple docstring"""


 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		str     	=					tempfile.mkdtemp()

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

        _UpperCAmelCase    :		Dict     	=					SpeechaTextTokenizer.from_pretrained(self.tmpdirname				)
        tokenizer.save_pretrained(self.tmpdirname				)

        _UpperCAmelCase    :		List[Any]     	=					{
            """feature_size""": 24,
            """num_mel_bins""": 24,
            """padding_value""": 0.0,
            """sampling_rate""": 16_000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }
        save_json(a_    ,save_dir / FEATURE_EXTRACTOR_NAME				)







 def _snake_case					(				self    ,**a_				)						->  str:
        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,**a_				)







 def _snake_case					(				self    ,**a_				)						->  Union[str, Any]:
        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,**a_				)







 def _snake_case					(				self				)						->  Union[str, Any]:
        shutil.rmtree(self.tmpdirname				)







 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		Optional[int]     	=					self.get_tokenizer()
        _UpperCAmelCase    :		Tuple     	=					self.get_feature_extractor()

        _UpperCAmelCase    :		Dict     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

        processor.save_pretrained(self.tmpdirname				)
        _UpperCAmelCase    :		Tuple     	=					SpeechaTextProcessor.from_pretrained(self.tmpdirname				)

        self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer.get_vocab()				)
        self.assertIsInstance(processor.tokenizer    ,a_				)

        self.assertEqual(processor.feature_extractor.to_json_string()    ,feature_extractor.to_json_string()				)
        self.assertIsInstance(processor.feature_extractor    ,a_				)







 def _snake_case					(				self				)						->  Union[str, Any]:
        _UpperCAmelCase    :		List[str]     	=					SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()    ,feature_extractor=self.get_feature_extractor()				)
        processor.save_pretrained(self.tmpdirname				)

        _UpperCAmelCase    :		Optional[int]     	=					self.get_tokenizer(bos_token="""(BOS)"""    ,eos_token="""(EOS)"""				)
        _UpperCAmelCase    :		Tuple     	=					self.get_feature_extractor(do_normalize=a_    ,padding_value=1.0				)

        _UpperCAmelCase    :		List[str]     	=					SpeechaTextProcessor.from_pretrained(
            self.tmpdirname    ,bos_token="""(BOS)"""    ,eos_token="""(EOS)"""    ,do_normalize=a_    ,padding_value=1.0				)

        self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer_add_kwargs.get_vocab()				)
        self.assertIsInstance(processor.tokenizer    ,a_				)

        self.assertEqual(processor.feature_extractor.to_json_string()    ,feature_extractor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.feature_extractor    ,a_				)







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Union[str, Any]     	=					self.get_feature_extractor()
        _UpperCAmelCase    :		Optional[Any]     	=					self.get_tokenizer()

        _UpperCAmelCase    :		Optional[int]     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

        _UpperCAmelCase    :		Optional[int]     	=					floats_list((3, 1_000)				)

        _UpperCAmelCase    :		Any     	=					feature_extractor(a_    ,return_tensors="""np"""				)
        _UpperCAmelCase    :		Dict     	=					processor(a_    ,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 _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		Dict     	=					self.get_feature_extractor()
        _UpperCAmelCase    :		str     	=					self.get_tokenizer()

        _UpperCAmelCase    :		int     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

        _UpperCAmelCase    :		Dict     	=					"""This is a test string"""

        _UpperCAmelCase    :		Tuple     	=					processor(text=a_				)

        _UpperCAmelCase    :		List[Any]     	=					tokenizer(a_				)

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







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Tuple     	=					self.get_feature_extractor()
        _UpperCAmelCase    :		Dict     	=					self.get_tokenizer()

        _UpperCAmelCase    :		int     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

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

        _UpperCAmelCase    :		str     	=					processor.batch_decode(a_				)
        _UpperCAmelCase    :		str     	=					tokenizer.batch_decode(a_				)

        self.assertListEqual(a_    ,a_				)







 def _snake_case					(				self				)						->  List[Any]:
        _UpperCAmelCase    :		List[Any]     	=					self.get_feature_extractor()
        _UpperCAmelCase    :		Any     	=					self.get_tokenizer()

        _UpperCAmelCase    :		int     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

        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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_	:		Union[str, Any]         =   {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Any         =   ['CamembertTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   ['CamembertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Optional[Any]         =   [
       'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'CamembertForCausalLM',
       'CamembertForMaskedLM',
       'CamembertForMultipleChoice',
       'CamembertForQuestionAnswering',
       'CamembertForSequenceClassification',
       'CamembertForTokenClassification',
       'CamembertModel',
       'CamembertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   [
       'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFCamembertForCausalLM',
       'TFCamembertForMaskedLM',
       'TFCamembertForMultipleChoice',
       'TFCamembertForQuestionAnswering',
       'TFCamembertForSequenceClassification',
       'TFCamembertForTokenClassification',
       'TFCamembertModel',
       'TFCamembertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert import CamembertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert_fast import CamembertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_camembert import (
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CamembertForCausalLM,
          CamembertForMaskedLM,
          CamembertForMultipleChoice,
          CamembertForQuestionAnswering,
          CamembertForSequenceClassification,
          CamembertForTokenClassification,
          CamembertModel,
          CamembertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_camembert import (
          TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCamembertForCausalLM,
          TFCamembertForMaskedLM,
          TFCamembertForMultipleChoice,
          TFCamembertForQuestionAnswering,
          TFCamembertForSequenceClassification,
          TFCamembertForTokenClassification,
          TFCamembertModel,
          TFCamembertPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_	:		int         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)