

import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
    from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

    from .test_feature_extraction_speechta import floats_list


lowerCAmelCase       :				Union[str, Any]         =					get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_torch
class 	_A						(					unittest.TestCase):

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tempfile.mkdtemp()

        SCREAMING_SNAKE_CASE_			:					int          =       SpeechTaTokenizer(_SCREAMING_SNAKE_CASE   )
        tokenizer.save_pretrained(self.tmpdirname   )

        SCREAMING_SNAKE_CASE_			:					int          =       {
            'feature_size': 1,
            'padding_value': 0.0,
            'sampling_rate': 1_6000,
            'do_normalize': False,
            'num_mel_bins': 80,
            'hop_length': 16,
            'win_length': 64,
            'win_function': 'hann_window',
            'fmin': 80,
            'fmax': 7600,
            'mel_floor': 1e-10,
            'reduction_factor': 2,
            'return_attention_mask': True,
        }

        SCREAMING_SNAKE_CASE_			:					List[Any]          =       os.path.join(self.tmpdirname	,   _SCREAMING_SNAKE_CASE   )
        with open(self.feature_extraction_file	,   'w'	,   encoding='utf-8'   ) as fp:
              fp.write(json.dumps(_SCREAMING_SNAKE_CASE   ) + '\n'   )

  def 	UpperCAmelCase      (			self	,   **_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        return SpeechTaTokenizer.from_pretrained(self.tmpdirname	,   **_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self	,   **_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname	,   **_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        shutil.rmtree(self.tmpdirname   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       self.get_tokenizer()
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       self.get_feature_extractor()

        SCREAMING_SNAKE_CASE_			:					Tuple          =       SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,   feature_extractor=_SCREAMING_SNAKE_CASE   )

        processor.save_pretrained(self.tmpdirname   )
        SCREAMING_SNAKE_CASE_			:					List[str]          =       SpeechTaProcessor.from_pretrained(self.tmpdirname   )

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

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

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Dict          =       SpeechTaProcessor(tokenizer=self.get_tokenizer()	,   feature_extractor=self.get_feature_extractor()   )
        processor.save_pretrained(self.tmpdirname   )

        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       self.get_tokenizer(bos_token='(BOS)'	,   eos_token='(EOS)'   )
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       self.get_feature_extractor(do_normalize=_SCREAMING_SNAKE_CASE	,   padding_value=1.0   )

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       SpeechTaProcessor.from_pretrained(
            self.tmpdirname	,   bos_token='(BOS)'	,   eos_token='(EOS)'	,   do_normalize=_SCREAMING_SNAKE_CASE	,   padding_value=1.0   )

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

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

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.get_feature_extractor()
        SCREAMING_SNAKE_CASE_			:					Dict          =       self.get_tokenizer()

        SCREAMING_SNAKE_CASE_			:					List[Any]          =       SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,   feature_extractor=_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					List[str]          =       floats_list((3, 1000)   )

        SCREAMING_SNAKE_CASE_			:					str          =       feature_extractor(audio=_SCREAMING_SNAKE_CASE	,   return_tensors='np'   )
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       processor(audio=_SCREAMING_SNAKE_CASE	,   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   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       self.get_feature_extractor()
        SCREAMING_SNAKE_CASE_			:					Any          =       self.get_tokenizer()

        SCREAMING_SNAKE_CASE_			:					Tuple          =       SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,   feature_extractor=_SCREAMING_SNAKE_CASE   )

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

        SCREAMING_SNAKE_CASE_			:					List[str]          =       feature_extractor(audio_target=_SCREAMING_SNAKE_CASE	,   return_tensors='np'   )
        SCREAMING_SNAKE_CASE_			:					Any          =       processor(audio_target=_SCREAMING_SNAKE_CASE	,   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   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       self.get_feature_extractor()
        SCREAMING_SNAKE_CASE_			:					str          =       self.get_tokenizer()

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,   feature_extractor=_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       'This is a test string'

        SCREAMING_SNAKE_CASE_			:					int          =       processor(text=_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Tuple          =       tokenizer(_SCREAMING_SNAKE_CASE   )

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

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,   feature_extractor=_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					List[Any]          =       'This is a test string'

        SCREAMING_SNAKE_CASE_			:					int          =       processor(text_target=_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tokenizer(_SCREAMING_SNAKE_CASE   )

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

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       self.get_feature_extractor()
        SCREAMING_SNAKE_CASE_			:					str          =       self.get_tokenizer()

        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,   feature_extractor=_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					List[str]          =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       processor.batch_decode(_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   )

        self.assertListEqual(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       self.get_feature_extractor()
        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.get_tokenizer()

        SCREAMING_SNAKE_CASE_			:					str          =       SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,   feature_extractor=_SCREAMING_SNAKE_CASE   )

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









from collections import defaultdict

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					defaultdict[str, int]          =       defaultdict(a					)
      # Creating a dictionary containing count of each word
      for word in sentence.split():
            occurrence[word] += 1
      return occurrence


if __name__ == "__main__":
    for word, count in word_occurrence('INPUT STRING').items():
        print(F'{word}: {count}')






