'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list

class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      def   _A			( self		:       Optional[int] ):
             _UpperCAmelCase  :						int							   =					"<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" " )
             _UpperCAmelCase  :						str							   =					dict(zip(A		,      range(len(A ) ) ) )

             _UpperCAmelCase  :						List[str]							   =					{
                 "pad_token": "<pad>",
                 "unk_token": "<unk>",
                 "bos_token": "<s>",
                 "eos_token": "</s>",
             }
             _UpperCAmelCase  :						Union[str, Any]							   =					{
                 "feature_size": 1,
                 "padding_value": 0.0,
                 "sampling_rate": 16000,
                 "return_attention_mask": False,
                 "do_normalize": True,
             }

             _UpperCAmelCase  :						List[str]							   =					tempfile.mkdtemp()
             _UpperCAmelCase  :						Tuple							   =					os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"] )
             _UpperCAmelCase  :						Any							   =					os.path.join(self.tmpdirname		,      A )
             with open(self.vocab_file		,      "w"		,      encoding="utf-8" ) as fp:
                    fp.write(json.dumps(A ) + "\n" )

             with open(self.feature_extraction_file		,      "w"		,      encoding="utf-8" ) as fp:
                    fp.write(json.dumps(A ) + "\n" )




      def   _A			( self		:       Any		,      **A		:       Optional[int] ):
             _UpperCAmelCase  :						List[Any]							   =					self.add_kwargs_tokens_map.copy()
             kwargs.update(A )
             return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,      **A )




      def   _A			( self		:       List[str]		,      **A		:       List[Any] ):
             return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,      **A )




      def   _A			( self		:       Dict ):
             shutil.rmtree(self.tmpdirname )




      def   _A			( self		:       Optional[int] ):
             _UpperCAmelCase  :						Optional[int]							   =					self.get_tokenizer()
             _UpperCAmelCase  :						Tuple							   =					self.get_feature_extractor()

             _UpperCAmelCase  :						Optional[int]							   =					WavaVecaProcessor(tokenizer=A		,      feature_extractor=A )

             processor.save_pretrained(self.tmpdirname )
             _UpperCAmelCase  :						List[Any]							   =					WavaVecaProcessor.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   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						List[str]							   =					WavaVecaProcessor(tokenizer=self.get_tokenizer()		,      feature_extractor=self.get_feature_extractor() )
             processor.save_pretrained(self.tmpdirname )

             _UpperCAmelCase  :						List[Any]							   =					self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)" )
             _UpperCAmelCase  :						str							   =					self.get_feature_extractor(do_normalize=A		,      padding_value=1.0 )

             _UpperCAmelCase  :						Union[str, Any]							   =					WavaVecaProcessor.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   _A			( self		:       Any ):
             _UpperCAmelCase  :						Any							   =					self.get_feature_extractor()
             _UpperCAmelCase  :						Optional[Any]							   =					self.get_tokenizer()

             _UpperCAmelCase  :						str							   =					WavaVecaProcessor(tokenizer=A		,      feature_extractor=A )

             _UpperCAmelCase  :						int							   =					floats_list((3, 1000) )

             _UpperCAmelCase  :						Dict							   =					feature_extractor(A		,      return_tensors="np" )
             _UpperCAmelCase  :						str							   =					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   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						Optional[int]							   =					self.get_feature_extractor()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.get_tokenizer()

             _UpperCAmelCase  :						Optional[int]							   =					WavaVecaProcessor(tokenizer=A		,      feature_extractor=A )

             _UpperCAmelCase  :						Optional[Any]							   =					"This is a test string"

             _UpperCAmelCase  :						Tuple							   =					processor(text=A )

             _UpperCAmelCase  :						str							   =					tokenizer(A )

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




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

             _UpperCAmelCase  :						str							   =					WavaVecaProcessor(tokenizer=A		,      feature_extractor=A )

             _UpperCAmelCase  :						Dict							   =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _UpperCAmelCase  :						Union[str, Any]							   =					processor.batch_decode(A )
             _UpperCAmelCase  :						Dict							   =					tokenizer.batch_decode(A )

             self.assertListEqual(A		,      A )





      def   _A			( self		:       Any ):
             _UpperCAmelCase  :						int							   =					self.get_feature_extractor()
             _UpperCAmelCase  :						Any							   =					self.get_tokenizer()

             _UpperCAmelCase  :						int							   =					WavaVecaProcessor(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'''




import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"    ,			[
        (Version("1.0.0"					), True),
        ("1.0.0", True),
        (Version("2.0.0"					), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]    ,			)
def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   List[Any]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Union[str, Any]							   =					Version("1.0.0"					)
       assert (version == other) is expected_equality
       assert (version != other) is not expected_equality
       assert (hash(_UpperCAmelCase					) == hash(_UpperCAmelCase					)) is expected_equality

