'''simple docstring'''


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


snake_case__	:     Union[str, Any]      =    get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')






@require_torch
class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = tempfile.mkdtemp()

       UpperCAmelCase_				:  Any  = SpeechTaTokenizer(snake_case_		)
       tokenizer.save_pretrained(self.tmpdirname		)

       UpperCAmelCase_				:  Optional[Any]  = {
           'feature_size': 1,
           'padding_value': 0.0,
           'sampling_rate': 1_6_0_0_0,
           'do_normalize': False,
           'num_mel_bins': 8_0,
           'hop_length': 1_6,
           'win_length': 6_4,
           'win_function': 'hann_window',
           'fmin': 8_0,
           'fmax': 7_6_0_0,
           'mel_floor': 1E-10,
           'reduction_factor': 2,
           'return_attention_mask': True,
       }

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


  def _UpperCamelCase	(					self ,						**snake_case_		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						**snake_case_		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       shutil.rmtree(self.tmpdirname		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.get_tokenizer()
       UpperCAmelCase_				:  str  = self.get_feature_extractor()

       UpperCAmelCase_				:  Optional[Any]  = SpeechTaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       processor.save_pretrained(self.tmpdirname		)
       UpperCAmelCase_				:  Union[str, Any]  = SpeechTaProcessor.from_pretrained(self.tmpdirname		)

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

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = SpeechTaProcessor(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_				:  Any  = self.get_feature_extractor(do_normalize=snake_case_ ,						padding_value=1.0		)

       UpperCAmelCase_				:  int  = SpeechTaProcessor.from_pretrained(
           self.tmpdirname ,						bos_token='(BOS)' ,						eos_token='(EOS)' ,						do_normalize=snake_case_ ,						padding_value=1.0		)

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

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.get_feature_extractor()
       UpperCAmelCase_				:  str  = self.get_tokenizer()

       UpperCAmelCase_				:  List[str]  = SpeechTaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  Dict  = floats_list((3, 1_0_0_0)		)

       UpperCAmelCase_				:  Tuple  = feature_extractor(audio=snake_case_ ,						return_tensors='np'		)
       UpperCAmelCase_				:  Dict  = processor(audio=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'''

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

       UpperCAmelCase_				:  List[str]  = SpeechTaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  Optional[Any]  = floats_list((3, 1_0_0_0)		)

       UpperCAmelCase_				:  List[Any]  = feature_extractor(audio_target=snake_case_ ,						return_tensors='np'		)
       UpperCAmelCase_				:  Optional[int]  = processor(audio_target=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'''

       UpperCAmelCase_				:  List[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Optional[int]  = self.get_tokenizer()

       UpperCAmelCase_				:  Any  = SpeechTaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

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

       UpperCAmelCase_				:  Dict  = processor(text=snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = tokenizer(snake_case_		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Dict  = self.get_tokenizer()

       UpperCAmelCase_				:  Tuple  = SpeechTaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = 'This is a test string'

       UpperCAmelCase_				:  int  = processor(text_target=snake_case_		)
       UpperCAmelCase_				:  Optional[int]  = tokenizer(snake_case_		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = self.get_feature_extractor()
       UpperCAmelCase_				:  Tuple  = self.get_tokenizer()

       UpperCAmelCase_				:  List[str]  = SpeechTaProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  int  = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCAmelCase_				:  List[Any]  = processor.batch_decode(snake_case_		)
       UpperCAmelCase_				:  str  = tokenizer.batch_decode(snake_case_		)

       self.assertListEqual(snake_case_ ,						snake_case_		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Dict  = self.get_tokenizer()

       UpperCAmelCase_				:  Optional[Any]  = SpeechTaProcessor(tokenizer=snake_case_ ,						feature_extractor=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 __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_    :       List[str]       =							TypeVar('''T''')







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     pass







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     pass







class 		__UpperCAmelCase(		Generic[T]			):





     """simple docstring"""



     def __init__(   self			:							str    ,					A			:							int = 10     )      ->      str:






         """simple docstring"""
         a__  			=							[]
         a__  			=							limit

     def __bool__(   self			:							Dict     )      ->      bool:






         """simple docstring"""
         return bool(self.stack     )

     def __str__(   self			:							str     )      ->      str:






         """simple docstring"""
         return str(self.stack     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							T     )      ->      None:






         """simple docstring"""
         if len(self.stack     ) >= self.limit:
             raise StackOverflowError
         self.stack.append(A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      T:






         """simple docstring"""
         if not self.stack:
             raise StackUnderflowError
         return self.stack.pop()

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      T:






         """simple docstring"""
         if not self.stack:
             raise StackUnderflowError
         return self.stack[-1]

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      bool:






         """simple docstring"""
         return not bool(self.stack     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      bool:






         """simple docstring"""
         return self.size() == self.limit

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      int:






         """simple docstring"""
         return len(self.stack     )






     def __contains__(   self			:							int    ,					A			:							T     )      ->      bool:






         """simple docstring"""
         return item in self.stack






def 							__lowerCAmelCase			():
    a__  			=							Stack(10		)
    assert bool(UpperCAmelCase_		) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(UpperCAmelCase_		) == "[]"

    try:
        a__  			=							stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        a__  			=							stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10		):
        assert stack.size() == i
        stack.push(UpperCAmelCase_		)

    assert bool(UpperCAmelCase_		)
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(UpperCAmelCase_		) == str(list(range(10		)		)		)
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100		)
    assert str(UpperCAmelCase_		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

    try:
        stack.push(200		)
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
 test_stack()






