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


UpperCAmelCase      		=					get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')




@require_torch
class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Tuple	)			->				Any:
        _UpperCamelCase  :							List[Any]	    =	tempfile.mkdtemp()

        _UpperCamelCase  :							str	    =	SpeechTaTokenizer(lowerCamelCase	)
        tokenizer.save_pretrained(self.tmpdirname	)

        _UpperCamelCase  :							Union[str, 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[str]	    =	os.path.join(self.tmpdirname							,      lowerCamelCase	)
        with open(self.feature_extraction_file							,      'w'							,      encoding='utf-8'	) as fp:
               fp.write(json.dumps(lowerCamelCase	) + '\n'	)

 def        __snake_case       (      self							:		Union[str, Any]							,      **lowerCamelCase							:		int	)			->				Tuple:
        return SpeechTaTokenizer.from_pretrained(self.tmpdirname							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]							,      **lowerCamelCase							:		Optional[int]	)			->				Optional[Any]:
        return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				str:
        shutil.rmtree(self.tmpdirname	)

 def        __snake_case       (      self							:		Dict	)			->				Tuple:
        _UpperCamelCase  :							int	    =	self.get_tokenizer()
        _UpperCamelCase  :							Dict	    =	self.get_feature_extractor()

        _UpperCamelCase  :							Optional[Any]	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        processor.save_pretrained(self.tmpdirname	)
        _UpperCamelCase  :							Tuple	    =	SpeechTaProcessor.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							:		str	)			->				int:
        _UpperCamelCase  :							Union[str, Any]	    =	SpeechTaProcessor(tokenizer=self.get_tokenizer()							,      feature_extractor=self.get_feature_extractor()	)
        processor.save_pretrained(self.tmpdirname	)

        _UpperCamelCase  :							Any	    =	self.get_tokenizer(bos_token='(BOS)'							,      eos_token='(EOS)'	)
        _UpperCamelCase  :							Any	    =	self.get_feature_extractor(do_normalize=lowerCamelCase							,      padding_value=1.0	)

        _UpperCamelCase  :							int	    =	SpeechTaProcessor.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							:		Union[str, Any]	)			->				Tuple:
        _UpperCamelCase  :							List[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							Dict	    =	self.get_tokenizer()

        _UpperCamelCase  :							Dict	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Any	    =	floats_list((3, 1_0_0_0)	)

        _UpperCamelCase  :							Union[str, Any]	    =	feature_extractor(audio=lowerCamelCase							,      return_tensors='np'	)
        _UpperCamelCase  :							Optional[int]	    =	processor(audio=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							:		List[str]	)			->				int:
        _UpperCamelCase  :							List[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							int	    =	self.get_tokenizer()

        _UpperCamelCase  :							Any	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Optional[int]	    =	floats_list((3, 1_0_0_0)	)

        _UpperCamelCase  :							Any	    =	feature_extractor(audio_target=lowerCamelCase							,      return_tensors='np'	)
        _UpperCamelCase  :							List[Any]	    =	processor(audio_target=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							:		Tuple	)			->				Any:
        _UpperCamelCase  :							Dict	    =	self.get_feature_extractor()
        _UpperCamelCase  :							List[Any]	    =	self.get_tokenizer()

        _UpperCamelCase  :							List[str]	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Any	    =	'This is a test string'

        _UpperCamelCase  :							Dict	    =	processor(text=lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	tokenizer(lowerCamelCase	)

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

 def        __snake_case       (      self							:		List[Any]	)			->				int:
        _UpperCamelCase  :							List[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							List[Any]	    =	self.get_tokenizer()

        _UpperCamelCase  :							Tuple	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

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

        _UpperCamelCase  :							Any	    =	processor(text_target=lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	tokenizer(lowerCamelCase	)

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

 def        __snake_case       (      self							:		List[str]	)			->				Union[str, Any]:
        _UpperCamelCase  :							Optional[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							str	    =	self.get_tokenizer()

        _UpperCamelCase  :							Tuple	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

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

        _UpperCamelCase  :							str	    =	processor.batch_decode(lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	tokenizer.batch_decode(lowerCamelCase	)

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		str	)			->				Any:
        _UpperCamelCase  :							int	    =	self.get_feature_extractor()
        _UpperCamelCase  :							List[Any]	    =	self.get_tokenizer()

        _UpperCamelCase  :							Union[str, Any]	    =	SpeechTaProcessor(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 collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase			     =					TypeVar('''T''')
UpperCamelCase			     =					TypeVar('''U''')






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  def __init__( self				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							T | None					,  SCREAMING_SNAKE_CASE_				:							U | None	)			->		Any:







       '''simple docstring'''



       A:		Union[str, Any]          =		key
       A:		Union[str, Any]          =		val
       A:		int          =		0
       A:		DoubleLinkedListNode[T, U] | None          =		None
       A:		DoubleLinkedListNode[T, U] | None          =		None




  def __repr__( self				:							Any	)			->		str:







       '''simple docstring'''



       return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
           self.key					,  self.val					,  self.freq					,  self.next is not None					,  self.prev is not None	)






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  def __init__( self				:							int	)			->		None:







       '''simple docstring'''



       A:		DoubleLinkedListNode[T, U]          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		DoubleLinkedListNode[T, U]          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A     ,    A:		Any          =		self.rear, self.head



  def __repr__( self				:							Optional[int]	)			->		str:







       '''simple docstring'''



       A:		int          =		['''DoubleLinkedList''']
       A:		Dict          =		self.head
       while node.next is not None:
            rep.append(str(SCREAMING_SNAKE_CASE_	)	)
            A:		int          =		node.next
       rep.append(str(self.rear	)	)
       return ",\n    ".join(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Any					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		None:







       '''simple docstring'''



       A:		Union[str, Any]          =		self.rear.prev

       # All nodes other than self.head are guaranteed to have non-None previous
       assert previous is not None

       A:		Optional[int]          =		node
       A:		int          =		previous
       A:		Dict          =		node
       A:		int          =		self.rear
       node.freq += 1
       self._position_node(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							int					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		None:







       '''simple docstring'''



       while node.prev is not None and node.prev.freq > node.freq:
            # swap node with previous node
            A:		Any          =		node.prev

            A:		Optional[Any]          =		previous_node.prev
            A:		int          =		node.prev
            A:		int          =		previous_node
            A:		Optional[int]          =		node




  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		DoubleLinkedListNode[T, U] | None:







       '''simple docstring'''



       if node.prev is None or node.next is None:
            return None

       A:		List[Any]          =		node.next
       A:		Dict          =		node.prev
       A:		str          =		None
       A:		Union[str, Any]          =		None
       return node






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  UpperCamelCase_			:      dict[Callable[[T], U], LFUCache[T, U]]										=				{}



  def __init__( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							int	)			->		int:







       '''simple docstring'''



       A:		DoubleLinkedList[T, U]          =		DoubleLinkedList()
       A:		Any          =		capacity
       A:		List[Any]          =		0
       A:		Dict          =		0
       A:		Dict          =		0
       A:		dict[T, DoubleLinkedListNode[T, U]]          =		{}



  def __repr__( self				:							Optional[int]	)			->		str:







       '''simple docstring'''



       return (
           f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
           f"""capacity={self.capacity}, current_size={self.num_keys})"""
       )



  def __contains__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							T	)			->		bool:







       '''simple docstring'''



       return key in self.cache



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							T	)			->		U | None:







       '''simple docstring'''



       if key in self.cache:
            self.hits += 1
            A:		DoubleLinkedListNode[T, U]          =		self.cache[key]
            A:		List[str]          =		self.list.remove(self.cache[key]	)
            assert node == value_node

            # node is guaranteed not None because it is in self.cache
            assert node is not None
            self.list.add(SCREAMING_SNAKE_CASE_	)
            return node.val
       self.miss += 1
       return None



  def 					_snake_case     ( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							T					,  SCREAMING_SNAKE_CASE_				:							U	)			->		None:







       '''simple docstring'''



       if key not in self.cache:
            if self.num_keys >= self.capacity:
                 # delete first node when over capacity
                 A:		List[Any]          =		self.list.head.next

                 # guaranteed to have a non-None first node when num_keys > 0
                 # explain to type checker via assertions
                 assert first_node is not None
                 assert first_node.key is not None
                 assert self.list.remove(SCREAMING_SNAKE_CASE_	) is not None
                 # first_node guaranteed to be in list

                 del self.cache[first_node.key]
                 self.num_keys -= 1
            A:		Tuple          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
            self.list.add(self.cache[key]	)
            self.num_keys += 1

       else:
            A:		Optional[Any]          =		self.list.remove(self.cache[key]	)
            assert node is not None  # node guaranteed to be in list
            A:		List[str]          =		value
            self.list.add(SCREAMING_SNAKE_CASE_	)




  @classmethod
  def 					_snake_case     ( cls				:							type[LFUCache[T, U]]					,  SCREAMING_SNAKE_CASE_				:							int = 1_28	)			->		Callable[[Callable[[T], U]], Callable[..., U]]:







       '''simple docstring'''



       def cache_decorator_inner(SCREAMING_SNAKE_CASE_				:							Callable[[T], U]	) -> Callable[..., U]:
            def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE_				:							T	) -> U:
                 if func not in cls.decorator_function_to_instance_map:
                      A:		Tuple          =		LFUCache(SCREAMING_SNAKE_CASE_	)

                 A:		List[Any]          =		cls.decorator_function_to_instance_map[func].get(args[0]	)
                 if result is None:
                      A:		List[str]          =		func(*SCREAMING_SNAKE_CASE_	)
                      cls.decorator_function_to_instance_map[func].put(args[0]					,  SCREAMING_SNAKE_CASE_	)
                 return result

            def cache_info() -> LFUCache[T, U]:
                 return cls.decorator_function_to_instance_map[func]

            setattr(SCREAMING_SNAKE_CASE_					,  '''cache_info'''					,  SCREAMING_SNAKE_CASE_	)  # noqa: B010

            return cache_decorator_wrapper

       return cache_decorator_inner


if __name__ == "__main__":
     import doctest

     doctest.testmod()






