
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 							A__	(			unittest.TestCase):
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Dict           =   """<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(""" """   )
       lowerCamelCase	:			Optional[int]           =   dict(zip(__magic_name__					,						range(len(__magic_name__   )   )   )   )

       lowerCamelCase	:			int           =   {
           """pad_token""": """<pad>""",
           """unk_token""": """<unk>""",
           """bos_token""": """<s>""",
           """eos_token""": """</s>""",
       }
       lowerCamelCase	:			Optional[Any]           =   {
           """feature_size""": 1,
           """padding_value""": 0.0,
           """sampling_rate""": 1_6_0_0_0,
           """return_attention_mask""": False,
           """do_normalize""": True,
       }

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

       with open(self.feature_extraction_file					,						"""w"""					,						encoding="""utf-8"""   ) as fp:
        fp.write(json.dumps(__magic_name__   ) + """\n"""   )
      def 		UpperCamelCase__							(   self					,						**__magic_name__   ):
       lowerCamelCase	:			Union[str, Any]           =   self.add_kwargs_tokens_map.copy()
       kwargs.update(__magic_name__   )
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,						**__magic_name__   )
      def 		UpperCamelCase__							(   self					,						**__magic_name__   ):
       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,						**__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       shutil.rmtree(self.tmpdirname   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			int           =   self.get_tokenizer()
       lowerCamelCase	:			Tuple           =   self.get_feature_extractor()

       lowerCamelCase	:			Tuple           =   WavaVecaProcessor(tokenizer=__magic_name__					,						feature_extractor=__magic_name__   )

       processor.save_pretrained(self.tmpdirname   )
       lowerCamelCase	:			Optional[Any]           =   WavaVecaProcessor.from_pretrained(self.tmpdirname   )

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

       self.assertEqual(processor.feature_extractor.to_json_string()					,						feature_extractor.to_json_string()   )
       self.assertIsInstance(processor.feature_extractor					,						__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Tuple           =   WavaVecaProcessor(tokenizer=self.get_tokenizer()					,						feature_extractor=self.get_feature_extractor()   )
       processor.save_pretrained(self.tmpdirname   )

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

       lowerCamelCase	:			Union[str, Any]           =   WavaVecaProcessor.from_pretrained(
           self.tmpdirname					,						bos_token="""(BOS)"""					,						eos_token="""(EOS)"""					,						do_normalize=__magic_name__					,						padding_value=1.0   )

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

       self.assertEqual(processor.feature_extractor.to_json_string()					,						feature_extractor_add_kwargs.to_json_string()   )
       self.assertIsInstance(processor.feature_extractor					,						__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			str           =   self.get_feature_extractor()
       lowerCamelCase	:			Any           =   self.get_tokenizer()

       lowerCamelCase	:			Any           =   WavaVecaProcessor(tokenizer=__magic_name__					,						feature_extractor=__magic_name__   )

       lowerCamelCase	:			List[str]           =   floats_list((3, 1_0_0_0)   )

       lowerCamelCase	:			Union[str, Any]           =   feature_extractor(__magic_name__					,						return_tensors="""np"""   )
       lowerCamelCase	:			Tuple           =   processor(__magic_name__					,						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	:			List[Any]           =   self.get_feature_extractor()
       lowerCamelCase	:			Dict           =   self.get_tokenizer()

       lowerCamelCase	:			Any           =   WavaVecaProcessor(tokenizer=__magic_name__					,						feature_extractor=__magic_name__   )

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

       lowerCamelCase	:			Optional[Any]           =   processor(text=__magic_name__   )

       lowerCamelCase	:			Optional[Any]           =   tokenizer(__magic_name__   )

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]					,						encoded_processor[key]   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[str]           =   self.get_feature_extractor()
       lowerCamelCase	:			Dict           =   self.get_tokenizer()

       lowerCamelCase	:			Tuple           =   WavaVecaProcessor(tokenizer=__magic_name__					,						feature_extractor=__magic_name__   )

       lowerCamelCase	:			List[Any]           =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowerCamelCase	:			Union[str, Any]           =   processor.batch_decode(__magic_name__   )
       lowerCamelCase	:			int           =   tokenizer.batch_decode(__magic_name__   )

       self.assertListEqual(__magic_name__					,						__magic_name__   )






      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[str]           =   self.get_feature_extractor()
       lowerCamelCase	:			List[str]           =   self.get_tokenizer()

       lowerCamelCase	:			List[str]           =   WavaVecaProcessor(tokenizer=__magic_name__					,						feature_extractor=__magic_name__   )

       self.assertListEqual(
           processor.model_input_names					,						feature_extractor.model_input_names					,						msg="""`processor` and `feature_extractor` model input names do not match"""					,						)






def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Optional[Any]           =   float()
 try:
  lowerCamelCase	:			Tuple           =   mass * acceleration
 except Exception:
  return -0.0
 return force


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # demo
      _lowerCamelCase           =12.5
      _lowerCamelCase           =1_0
      _lowerCamelCase           =newtons_second_law_of_motion(mass, acceleration)
      print("""The force is """, force, """N""")




