

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      ):







      def       lowerCAmelCase_       (	self  )		->			Dict:
        snake_case_					= """<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(""" """  )
        snake_case_					= dict(zip(lowerCamelCase				,    range(len(lowerCamelCase  )  )  )  )

        snake_case_					= {
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        snake_case_					= {
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

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

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







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			Optional[int]:
        snake_case_					= self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCamelCase  )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			Union[str, Any]:
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Dict:
        shutil.rmtree(self.tmpdirname  )







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        snake_case_					= self.get_tokenizer()
        snake_case_					= self.get_feature_extractor()

        snake_case_					= WavaVecaProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        processor.save_pretrained(self.tmpdirname  )
        snake_case_					= WavaVecaProcessor.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       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= WavaVecaProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()  )
        processor.save_pretrained(self.tmpdirname  )

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

        snake_case_					= WavaVecaProcessor.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       lowerCAmelCase_       (	self  )		->			str:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= WavaVecaProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= floats_list((3, 1000)  )

        snake_case_					= feature_extractor(lowerCamelCase				,    return_tensors="""np"""  )
        snake_case_					= 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       lowerCAmelCase_       (	self  )		->			str:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= WavaVecaProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= """This is a test string"""

        snake_case_					= processor(text=lowerCamelCase  )

        snake_case_					= tokenizer(lowerCamelCase  )

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







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= WavaVecaProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

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

        snake_case_					= processor.batch_decode(lowerCamelCase  )
        snake_case_					= tokenizer.batch_decode(lowerCamelCase  )

        self.assertListEqual(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			str:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= WavaVecaProcessor(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"""				,    )


def   UpperCamelCase(   lowercase_				)      ->							float:





  '''simple docstring'''





  return -num if num < 0 else num



def   UpperCamelCase(   lowercase_				)      ->							int:





  '''simple docstring'''





  if len(lowercase_				) == 0:
    raise ValueError("""abs_min() arg is an empty sequence"""				)
  snake_case_					= x[0]
  for i in x:
    if abs_val(lowercase_				) < abs_val(lowercase_				):
      snake_case_					= i
  return j



def   UpperCamelCase(   lowercase_				)      ->							int:





  '''simple docstring'''





  if len(lowercase_				) == 0:
    raise ValueError("""abs_max() arg is an empty sequence"""				)
  snake_case_					= x[0]
  for i in x:
    if abs(lowercase_				) > abs(lowercase_				):
      snake_case_					= i
  return j



def   UpperCamelCase(   lowercase_				)      ->							int:





  '''simple docstring'''





  if len(lowercase_				) == 0:
    raise ValueError("""abs_max_sort() arg is an empty sequence"""				)
  return sorted(lowercase_     ,   key=lowercase_				)[-1]



def   UpperCamelCase(   )      ->							List[Any]:





  '''simple docstring'''





  assert abs_val(0				) == 0
  assert abs_val(34				) == 34
  assert abs_val(-100000000000				) == 100000000000

  snake_case_					= [-3, -1, 2, -11]
  assert abs_max(lowercase_				) == -11
  assert abs_max_sort(lowercase_				) == -11
  assert abs_min(lowercase_				) == -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    test_abs_val()
    print(abs_val(-34))  # --> 34