






"""simple docstring"""







import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
  from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase     			=  get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_torch
@require_torchaudio
@require_sentencepiece
class 					lowerCamelCase__					(       unittest.TestCase ):






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       tempfile.mkdtemp()

          UpperCAmelCase							=       ["""<s>""", """<pad>""", """</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est"""]
          UpperCAmelCase							=       dict(zip(A	,range(len(A					)					)					)					)
          UpperCAmelCase							=       Path(self.tmpdirname					)
          save_json(A	,save_dir / VOCAB_FILES_NAMES["""vocab_file"""]					)
          if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                copyfile(A	,save_dir / VOCAB_FILES_NAMES["""spm_file"""]					)

          UpperCAmelCase							=       SpeechaTextTokenizer.from_pretrained(self.tmpdirname					)
          tokenizer.save_pretrained(self.tmpdirname					)

          UpperCAmelCase							=       {
              """feature_size""": 24,
              """num_mel_bins""": 24,
              """padding_value""": 0.0,
              """sampling_rate""": 16_000,
              """return_attention_mask""": False,
              """do_normalize""": True,
          }
          save_json(A	,save_dir / FEATURE_EXTRACTOR_NAME					)






    def 				_UpperCamelCase							( self	,**A					):
          return SpeechaTextTokenizer.from_pretrained(self.tmpdirname	,**A					)






    def 				_UpperCamelCase							( self	,**A					):
          return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname	,**A					)






    def 				_UpperCamelCase							( self					):
          shutil.rmtree(self.tmpdirname					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_tokenizer()
          UpperCAmelCase							=       self.get_feature_extractor()

          UpperCAmelCase							=       SpeechaTextProcessor(tokenizer=A	,feature_extractor=A					)

          processor.save_pretrained(self.tmpdirname					)
          UpperCAmelCase							=       SpeechaTextProcessor.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 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       SpeechaTextProcessor(
              tokenizer=self.get_tokenizer()	,feature_extractor=self.get_feature_extractor()					)
          processor.save_pretrained(self.tmpdirname					)

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

          UpperCAmelCase							=       SpeechaTextProcessor.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 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_feature_extractor()
          UpperCAmelCase							=       self.get_tokenizer()

          UpperCAmelCase							=       SpeechaTextProcessor(tokenizer=A	,feature_extractor=A					)

          UpperCAmelCase							=       floats_list((3, 1_000)					)

          UpperCAmelCase							=       feature_extractor(A	,return_tensors="""np"""					)
          UpperCAmelCase							=       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 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_feature_extractor()
          UpperCAmelCase							=       self.get_tokenizer()

          UpperCAmelCase							=       SpeechaTextProcessor(tokenizer=A	,feature_extractor=A					)

          UpperCAmelCase							=       """This is a test string"""

          UpperCAmelCase							=       processor(text=A					)

          UpperCAmelCase							=       tokenizer(A					)

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






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_feature_extractor()
          UpperCAmelCase							=       self.get_tokenizer()

          UpperCAmelCase							=       SpeechaTextProcessor(tokenizer=A	,feature_extractor=A					)

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

          UpperCAmelCase							=       processor.batch_decode(A					)
          UpperCAmelCase							=       tokenizer.batch_decode(A					)

          self.assertListEqual(A	,A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_feature_extractor()
          UpperCAmelCase							=       self.get_tokenizer()

          UpperCAmelCase							=       SpeechaTextProcessor(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"""







from __future__ import annotations

from typing import Generic, TypeVar

_UpperCamelCase     			=  TypeVar("""T""")







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self	,A					):
          UpperCAmelCase							=       data  # Assign data
          UpperCAmelCase							=       None  # Initialize next as null
          UpperCAmelCase							=       None  # Initialize prev as null







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self					):
          UpperCAmelCase							=       None






    def 				_UpperCamelCase							( self	,A					):
          if self.head is None:
                UpperCAmelCase							=       Node(A					)
          else:
                UpperCAmelCase							=       Node(A					)
                UpperCAmelCase							=       new_node
                UpperCAmelCase							=       self.head
                UpperCAmelCase							=       None
                UpperCAmelCase							=       new_node






    def 				_UpperCamelCase							( self					):
          if self.head is None:
                return None
          else:
                assert self.head is not None
                UpperCAmelCase							=       self.head.data
                UpperCAmelCase							=       self.head.next
                if self.head is not None:
                      UpperCAmelCase							=       None
                return temp






    def 				_UpperCamelCase							( self					):
          return self.head.data if self.head is not None else None






    def __len__( self					):
          UpperCAmelCase							=       self.head
          UpperCAmelCase							=       0
          while temp is not None:
                count += 1
                UpperCAmelCase							=       temp.next
          return count






    def 				_UpperCamelCase							( self					):
          return self.head is None






    def 				_UpperCamelCase							( self					):
          print("""stack elements are:"""					)
          UpperCAmelCase							=       self.head
          while temp is not None:
                print(temp.data	,end="""->"""					)
                UpperCAmelCase							=       temp.next


# Code execution starts here
if __name__ == "__main__":
  # Start with the empty stack
  _UpperCamelCase     			=  Stack()

  # Insert 4 at the beginning. So stack becomes 4->None
  print("""Stack operations using Doubly LinkedList""")
  stack.push(4)

  # Insert 5 at the beginning. So stack becomes 4->5->None
  stack.push(5)

  # Insert 6 at the beginning. So stack becomes 4->5->6->None
  stack.push(6)

  # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
  stack.push(7)

  # Print the stack
  stack.print_stack()

  # Print the top element
  print("""\nTop element is """, stack.top())

  # Print the stack size
  print("""Size of the stack is """, len(stack))

  # pop the top element
  stack.pop()

  # pop the top element
  stack.pop()

  # two elements have now been popped off
  stack.print_stack()

  # Print True if the stack is empty else False
  print("""\nstack is empty:""", stack.is_empty())



