



"""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


__SCREAMING_SNAKE_CASE     :  Any										=					get_tests_dir('''fixtures/test_sentencepiece.model''')


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





					'''simple docstring'''



					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = tempfile.mkdtemp()

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

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

									_lowerCamelCase					     = {
									    '''feature_size''': 2_4,
									    '''num_mel_bins''': 2_4,
									    '''padding_value''': 0.0,
									    '''sampling_rate''': 1_6_0_0_0,
									    '''return_attention_mask''': False,
									    '''do_normalize''': True,
									}
									save_json(lowerCamelCase__ ,				save_dir / FEATURE_EXTRACTOR_NAME      )

					def 	snake_case__					(			self ,				**lowerCamelCase__      ):
									return SpeechaTextTokenizer.from_pretrained(self.tmpdirname ,				**lowerCamelCase__      )

					def 	snake_case__					(			self ,				**lowerCamelCase__      ):
									return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname ,				**lowerCamelCase__      )

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

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.get_tokenizer()
									_lowerCamelCase					     = self.get_feature_extractor()

									_lowerCamelCase					     = SpeechaTextProcessor(tokenizer=lowerCamelCase__ ,				feature_extractor=lowerCamelCase__      )

									processor.save_pretrained(self.tmpdirname      )
									_lowerCamelCase					     = SpeechaTextProcessor.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 	snake_case__					(			self      ):
									_lowerCamelCase					     = SpeechaTextProcessor(
									    tokenizer=self.get_tokenizer() ,				feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

									_lowerCamelCase					     = self.get_tokenizer(bos_token='''(BOS)''' ,				eos_token='''(EOS)'''      )
									_lowerCamelCase					     = self.get_feature_extractor(do_normalize=lowerCamelCase__ ,				padding_value=1.0      )

									_lowerCamelCase					     = SpeechaTextProcessor.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 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.get_feature_extractor()
									_lowerCamelCase					     = self.get_tokenizer()

									_lowerCamelCase					     = SpeechaTextProcessor(tokenizer=lowerCamelCase__ ,				feature_extractor=lowerCamelCase__      )

									_lowerCamelCase					     = floats_list((3, 1_0_0_0)      )

									_lowerCamelCase					     = feature_extractor(lowerCamelCase__ ,				return_tensors='''np'''      )
									_lowerCamelCase					     = 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 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.get_feature_extractor()
									_lowerCamelCase					     = self.get_tokenizer()

									_lowerCamelCase					     = SpeechaTextProcessor(tokenizer=lowerCamelCase__ ,				feature_extractor=lowerCamelCase__      )

									_lowerCamelCase					     = '''This is a test string'''

									_lowerCamelCase					     = processor(text=lowerCamelCase__      )

									_lowerCamelCase					     = tokenizer(lowerCamelCase__      )

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

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.get_feature_extractor()
									_lowerCamelCase					     = self.get_tokenizer()

									_lowerCamelCase					     = SpeechaTextProcessor(tokenizer=lowerCamelCase__ ,				feature_extractor=lowerCamelCase__      )

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

									_lowerCamelCase					     = processor.batch_decode(lowerCamelCase__      )
									_lowerCamelCase					     = tokenizer.batch_decode(lowerCamelCase__      )

									self.assertListEqual(lowerCamelCase__ ,				lowerCamelCase__      )





					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.get_feature_extractor()
									_lowerCamelCase					     = self.get_tokenizer()

									_lowerCamelCase					     = SpeechaTextProcessor(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''' ,				)








"""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())



